CROSSH - China Resilience of Schools to Seismic Hazard: a Case Study in Beichuan Qiang

Home , 2008 Sichuan earthquake

LONDON’S GLOBAL UNIVERSITY I

Abstract

Department of Civil, Environmental & Geomatic Engineering Earthquake and People Interaction Centre (EPICentre)

International Center for Collaborative Research on Disaster Risk Reduction (ICCR-DRR)

FINAL REPORT CROSSH - China Resilience of Schools to Seismic Hazard: a case study in Beichuan Qiang

Autonomous County, Sichuan

by Zhou, Linghui; Yan, Shuang; Xue, Zeyue; Wang, Yanru;

Galasso, Carmine; D'Ayala, Dina

Feburary 2018

III Abstract

Executive Summary

The Comprehensive School Safety Framework (2017) is funded on three pillars: ‘Safe Learning Facilities’, ‘School Disaster Management’, and ‘Risk Reduction and Resilience Education’. CROSSH, funded by the International Center for Collaborative Research on Disaster Risk Reduction (ICCR-DRR), at Beijing Normal University, China, aimed to investigate how the three pillars underpinned the process of school infrastructure recovery post-2008 Wenchuan earthquake in China, which caused more than 5,000 school children deaths. The focus here is on Beichuan Qiang Autonomous County, Sichuan. When the M7.9 Wenchuan earthquake struck Sichuan on May 12, 2008, it caused the total or partial collapse of several schools: 1,200 of 2,600 students in the Beichuan Middle School died or were missing due to the building collapse; 500 students in the Beichuan Maoba Middle School died due to the landslides triggered by the earthquake.

This report presents the main findings and outcomes of the CROSSH project, including three parts 1) a methodology for physical and social vulnerability assessment and its application to school buildings in the County, through new analytical and empirical models validated against observations from recent Chinese earthquake; 2) an investigation on the enhancement of seismic resilience of schools through retrofitting of school buildings; 3) role of schools to develop a more resilient community through investigating perceptions, perspectives and hazard adjustments of students regarding earthquakes. Different types of seismic strengthening techniques for selected schools in Beichuan are compared; preliminary recommendations are given aiming to a prioritization scheme for strengthening intervention in school buildings.

In particular, the proposed physical vulnerability assessment methodology consists of a) systematic collection of data on school infrastructure location and characteristics across the County; b) development of a comprehensive database of typical and systematically defined structural typologies, including main structural and non- structural characteristics (age of construction, number of story, lateral load resisting system and materials, number of occupants, etc.); c) development of a library of fragility functions, derived within the project, for each identified structural typology; d) improve the understanding of seismic risk, and overcome the barriers to increase resilience of schools and communities to earthquakes. These tasks have been carried through a desk study combined with targeted field surveys in Beichuan.

The field surveys included both old (pre-Wenchuan) and new (post-Wenchuan) school sites, for a total of 102 school buildings visited during the mission. The project team used a pre-designed data collection form and a mobile app developed by the authors

within a previous project. Questionnaires to local practitioners on structural engineering aspects as well as to both middle school and university students were also carried out. The latter aimed to investigate behavioural responses and evaluate the awareness and trust of school users, some of whom experienced recent seismic events, about earthquake risk and disaster risk reduction.

The overall project framework and results from the study can be effectively used by Civil Protection and School Authorities in Sichuan to inform their preparedness actions planning and implementation.

V Acknowledgements

Acknowledgements

Firstly, we would like to express our sincere gratitude to the International Center for Collaborative Research on Disaster Risk Reduction (ICCR-DRR) and Community- Based Disaster Management in Asia Programme (CBDM Asia) for funding this project. We really appreciate having had this valuable and unique opportunity.

A significant part of this project could not have been achieved without the support of Prof. Saini Yang and staff members Miss Yaqiao Wu and Miss Yanjing Qin in ICCR- DRR. They always responded to our request promptly and provided us with any support we needed.

This project has also been made possible thanks to Prof. Jingsong Lei (Southwest University of Science and Technology), Mr Jianguo Tong (Sichuan Jianguo Technology Development Co., Ltd), Mrs Quan and Miss Rujia Deng (Mianyang Science and Technology City), Mr Hao Bing (Sichuan Univerisity), Mr Tao Li (Ji Zhun Fang Zhong Architectural Design Co. Ltd) and Mr Xinxin Li (China Southwest Architectural Design and Research Institute Corp., Ltd), all of whom helped us making the data collection in Sichuan possible. We are very grateful for their great help and very warm hospitality during the field trip.

Table of Content

Executive Summary ...... III

Acknowledgements ...... V

Table of Content ...... VI

List of Figures ...... XI

List of Tables ...... XV

Section I Backgound Informatio and Literature Review ...... 18

Introduction ...... 19

1.1 Research Background ...... 19

1.2 Aims and Objectives ...... 21

1.3 Report Outline ...... 22

Literature Review ...... 6

2.1 Seismic Fragility Assessment of Schools ...... 6

2.1.1 Philippines...... 6

2.1.2 Italy ...... 7

2.1.3 Switzerland ...... 7

2.1.4 Iran ...... 8

2.1.5 China ...... 8

2.2 Framework for Analytical Fragility Assessment ...... 9

2.2.1 Uncertainty in Deriving Analytical Fragility Functions ...... 11

2.3 Non-linear Dynamic Analysis ...... 12

2.3.1 Cloud Analysis ...... 13

2.3.2 Incremental Dynamic Analysis ...... 13 VI Table of Content

2.4 Seismic Evaluation of Existing Buildings ...... 15

2.4.1 Introduction of Seismic Evaluation Process ...... 15

2.4.2 Push-over Analysis...... 18

2.5 Seismic Rehabilitation and Retrofitting for RC Frames ...... 19

2.5.1 Seismic Deficiencies and Affecting Factors ...... 19

2.5.2 Strategies and Measures of Rehabilitation and Retrofitting ...... 20

2.6 FRP for Retrofitting ...... 25

2.6.1 FRP Bonding System and De-bonding Failure ...... 25

2.6.2 Beam Strengthening ...... 29

2.6.3 Column Strengthening ...... 36

2.6.4 Joint Strengthening ...... 39

2.7 Summary and Conclusions ...... 40

Section II Seismic Exposure and Vulnerability Modelling for Seismic Hazard ...... 41

Methodology for Section II ...... 42

Seismic Exposure of School Buildings ...... 44

4.1 Field Investigation ...... 44

4.2 Design of the Index Building ...... 49

4.2.1 General structural information ...... 50

4.2.2 Earthquake load calculation and seismic check ...... 50

4.2.3 Detailed cross-section design of columns ...... 52

4.2.4 Detailed cross-section design of beams ...... 53

Seismic Vulnerability Assessment...... 59

5.1 Numerical modelling of the Index Building ...... 59

5.1.1 Simplifications and limitations ...... 60

VII

Table of Content

5.1.2 Hysteretic behaviour of material ...... 60

5.2 Non-linear Dynamic Analysis ...... 61

5.2.1 Ground Motion Selection ...... 61

5.2.2 Ground motion scaling ...... 63

5.2.3 Non-linear Dynamic Analysis of the Index Building ...... 65

5.3 Fragility Assessment ...... 66

5.3.1 Defining Damage-State Thresholds ...... 66

5.3.2 Derivation of Fragility Curves ...... 67

5.3.3 Seismic Resistance Evaluation of the Index Building ...... 69

Conclusions for Section II ...... 71

Section III Resilience Improving Solutions ...... 73

Methodology for Section III ...... 74

Seismic Retrofitting of Index Building ...... 76

8.1 Structural Analysis in SeimsmoStruct ...... 76

8.1.1 Non-linear Static Push-over analysis ...... 76

8.1.2 Damage Criteria ...... 77

8.1.3 Structural Performance of the Index Building ...... 80

8.2 Designing of Retrofitting Strategies ...... 82

8.2.1 Selection of Retrofitting methods ...... 82

8.2.2 Retrofitting of Index Building in x-direction ...... 84

8.2.3 Retrofitting of Index building in y-direction ...... 86

8.2.4 Combination of Two Directions ...... 91

Fragility Assessment without and with Seismic Retrofitting ...... 93

9.1 Fragility Curves ...... 93

9.2 Effectiveness of Applied Retrofitting Methods ...... 96 IX Table of Content

Conclusions for Section III ...... 99

Section IV Social Science Analysis ...... 100

Social Vulnerability Assessment ...... 101

11.1 Impact of the 2008 and 2013 earthquakes on students ...... 101

11.2 Risk perception, hazard adjustments and disaster resilience ...... 101

11.3 Risk perceptions and demographic variables ...... 102

Methodology for Section IV ...... 104

12.1 Online questionnaire ...... 104

12.2 In-depth interviews ...... 105

Results ...... 106

13.1 General result of the online questionnaires ...... 106

13.1.1 Results in the context of different genders ...... 106

13.1.2 Results in the context of different groups of students with different disaster experience ...... 109

13.2 General results of the in-depth interviews ...... 112

Discussion on Perceptions, Perspectives and Hazard Adjustment ...... 113

14.1 In the context of different genders ...... 113

14.1.1 Student perceptions ...... 113

14.1.2 Student perspectives ...... 114

14.1.3 Student hazard adjustments ...... 114

14.2 In the context of people with and without earthquake experience ...... 115

14.2.1 Student perceptions ...... 115

14.2.2 Student perspectives ...... 117

14.2.3 Student hazard adjustments ...... 118

Table of Content

14.3 Strengths and weakness ...... 118

14.4 Suggestions for improvement ...... 119

Conclusions for Section IV ...... 120

Summary and Impact ...... 121

16.1 Summary and Conclusions ...... 121

16.2 Impact ...... 122

16.3 Recommendation for future work ...... 123

References ...... 124

Appendix A – Visual Survey form for the field investigation ...... 135

Appendix B – Matlab script for Cloud Analysis ...... 136

Appendix C – Matlab script for Incremental Dynamic Analysis ...... 138

Appendix D – Matlab script for generating spectral acceleration ...... 140

Appendix E – OpenSees script for 3D modelling of the index building ...... 142

Appendix F –Damgae States in SeismoStruct ...... 148

Appendix G –Online Questionnaire ...... 156

Appendix H – In-depth interview questions(IQs) ...... 162

XI Table of Content

List of Figures

Fig. 2-1 Mixing and matching for numerical modelling/analysis type (D’Ayala et al, 2015) ...... 11

Fig. 2-2 Main phases of analytical fragility assessment and their associated uncertainties (Maio and Tsionis, 2015) ...... 12

Fig. 2-3 Incremental dynamic analysis using increasingly scaled ground motion records ...... 14

Fig. 2-4 Steps to plot structure response subjected to scaled ground motion records ...... 14

Fig. 2-5 Generating IDA curve using cubic spline interpolation ...... 15

Fig. 2-6 interpretation of structure response according to IDA curve ...... 15

Fig. 2-7 Response spectrum from GB 50011-2010 ...... 17

Fig. 2-8 Soft-storey mechanisms in old Beichuan town ...... 20

Fig. 2-9 Steel jacketing and FRP wrapping ...... 23

Fig. 2-10 Bracing arrangements (FEMA, 2006) ...... 24

Fig. 2-11 Bracing connections (Maheri & Hadjipour, 2003) ...... 24

Fig. 2-12 Rubber isolation bearing and sliding isolation bearing (Liu, et al., 2012) ...... 25

Fig. 2-13 FRP bonding system (ACI Committee 440, 2008) ...... 26

Fig. 2-14 Layers of FRP systems (CNR-DT 200 R1/2013, 2013) ...... 26

Fig. 2-15 Debonding failure mode (Aram, et al., 2008) ...... 27

Fig. 2-16 Flexural strengthening of beams ...... 30

Fig. 2-17 Strains in the FRP-concrete system from GB 50608-2010 ...... 32

Fig. 2-18 Shear strengthening schemes for RC beams by FRP (Teng & Chen, 2007) ...... 33

Table of Content

Fig. 2-19 Lateral view of FRP shear strengthening system ...... 33

Fig. 2-20 Examples of FRP wrapping in beam-column joints ...... 39

Fig. 2-21 FRP wrapping for beam-column joints in 3D view (ReLUIS, 2009) .. 40

Fig. 3-1 Flowchart for the derivation of fragility functions with analytical method ...... 43

Fig. 4-1 Location of surveyed schools in Beichuan County and Mianyang City 45

Fig. 4-2 Structural condition for surveyed school buildings ...... 47

Fig. 4-3 Number of storey for surveyed school buildings ...... 47

Fig. 4-4 Vulnerability factors (a) built on slope (b) pounding (c) opening irregular (d) opening corridor for surveyed school buildings ...... 47

Fig. 4-5 Surveyed school buildings in Beichuan County ...... 49

Fig. 4-6 Perspective view of index building ...... 49

Fig. 4-7 Design response spectrum for index building ...... 51

Fig. 4-8 design of columns at ground floor ...... 55

Fig. 4-9 Detailed design of columns at first and second floor ...... 56

Fig. 4-10 Detailed design of beams at first and second floor ...... 57

Fig. 4-11 Detailed design of beams at roof level ...... 58

Fig. 5-1 Stress-strain relationships for (a) concrete and (b) steel material models

adopted from OpenSees (2000) ...... 61

Fig. 5-2 Response spectrum of employed ground motion records from FEMA p695...... 63

Fig. 5-3 MIDR and roof drift ratio of the index building from cloud analysis .... 65

Fig. 5-4 Comparison between cloud analysis and IDA results ...... 65

Fig. 5-5 Cloud analysis results for the index building ...... 66

Fig. 5-6 Incremental dynamic analysis results for the index building ...... 66

Fig. 5-7 Fragility function for the index building derived from cloud analysis .. 68 XI Table of Content

Fig. 5-8 Fragility function for the index building derived from IDA ...... 68

Fig. 7-1 Flow chart of Seismic retrofitting ...... 75

Fig. 8-1 Pushover analysis results for the school building model in x- and y- directions, using triangular and uniform lateral loading patterns ...... 76

Fig. 8-2 Damage views of building (D’Ayala, et al., 2014) ...... 78

Fig. 8-3 Capacity curve of index building in x-direction ...... 81

Fig. 8-4 Capacity curve of index building in y-direction ...... 82

Fig. 8-5 Failure mode of index building in x-direction ...... 84

Fig. 8-6 View of applied FRP system in x-direction ...... 84

Fig. 8-7 Comparison of pushover curves between original building and FRP retrofitted building in x-direction ...... 86

Fig. 8-8 Failure view of index building in y-direction ...... 87

Fig. 8-9 FRP retrofitted columns in y-direction analysis ...... 87

Fig. 8-10 Comparison of pushover curves between original building and FRP retrofitted building in y-direction ...... 89

Fig. 8-11 Comparison of pushover curves between original building and concrete jacketing retrofitted building in y-direction ...... 91

Fig. 8-12 Comparison of pushover curves between original building and combined FRP retrofitted building in the x-direction analysis ...... 92

Fig. 8-13 Comparison of pushover curves between original building and combined FRP and concrete retrofitted building in the x-direction analysis ...... 92

Fig. 9-1 Backbone curve for hysteretic models (Ibarra, Medina and Krawinkler, 2005)...... 93

Fig. 9-2 Fragility curve of FRP retrofitted and original building in x-direction .. 94

Fig. 9-3 Fragility curve of FRP, concrete jacketing retrofitted and original building in y-direction ...... 94

Fig. 9-4 Fragility curve of combined FRP and concrete jacketing retrofitted and original building in x-direction ...... 95

XIII

Table of Content

Fig. 9-5 Fragility curve of combined FRP retrofitting and original building in x- direction ...... 95

Fig. 9-6 Response spectrum for 2%–3% exceedance in 50 years at intensity 8 .. 96

Fig. 9-7 The spectral acceleration over various fault distances, together with the attenuation models for period of 0.2s (Wen, et al., 2010) ...... 97

X Table of Content

List of Tables

Table 2-1 Performance levels ...... 16

Table 2-2: Rehabilitation objective ...... 16

Table 2-3: Seismic intensities in GB50011-2010 ...... 17

Table 2-4: Seismic rehabilitation measures for RC frames (FEMA, 2006) ...... 21

Table 2-5: Debonding modes and calculation in CNR-DT 200 R1/2013 ...... 29

Table 2-6: Calculation of flexural strengthening for beams in CNR-DT 200 R1/2013 ...... 31

Table 2-7: Calculation of shear strengthening for beams in CNR-DT 200 R1/2013 ...... 34

Table 2-8: Calculation of shear strengthening for beams in GB 50608-2010 ..... 35

Table 2-9: Calculation of confinement for columns in CNR-DT 200 R1/2013 ... 36

Table 2-10: Calculation of flexural strengthening for columns in CNR-DT 200 R1/2013 ...... 37

Table 4-1 Rapid visual survey criteria for seismic hazard assessment ...... 46

Table 4-2 General information of the index building ...... 50

Table 4-3 EQ load distribution and seismic shear force check ...... 51

Table 4-4 Shear capacity of index building ...... 52

Table 4-5 Moment capacity of index building ...... 52

Table 4-6 Detailed cross section design of columns at ground floor ...... 52

Table 4-7 Detailed cross section design of columns at first and second floor ..... 53

Table of Content

Table 4-8 Detailed cross section design of beams at first and second floor ...... 53

Table 4-9 Detailed cross section design of beams at roof level ...... 54

Table 5-1 Uniform load distribution in Chinese design code ...... 60

Table 5-2 Parameters for uniaxial material model of concrete and steel ...... 61

Table 5-3 Source of GM records in the SIMBAD database (Smerzini et al., 2014) ...... 62

Table 5-4 Detailed information and characteristics of employed earthquake records ...... 64

Table 5-5 Inter-storey drift for different performance level for rare earthquake events ...... 67

Table 5-6 Damage states thresholds for general RC frame structures ...... 67

Table 5-7 Median and dispersion of damage state curves derived from cloud analysis and IDA ...... 69

Table 5-8 The probability of exceeding each damage states according to the fragility functions derived from Cloud Analysis ...... 69

Table 5-9 The probability of exceeding each damage states according to the fragility functions derived from IDA ...... 69

Table 8-1: Material performance criteria ...... 78

Table 8-2: Damage states from GB 50011-2010...... 79

Table 8-3: Existing ISD related to damage states for RC buildings (D’Ayala, et al., 2014; Rossetto & Elnashai, 2003) ...... 79

Table 8-4: Definition of damage thresholds of x-direction of index building ..... 80

Table 8-5: Definition of damage thresholds of y-direction of index building ..... 81

Table 8-6: Properties of applied CFRP ...... 83

Table 8-7: Reactions of beams at failure ...... 85

Table 8-8: Flexural capacity of beams before and after retrofitting ...... 85

Table 8-9: Damage thresholds for FRP retrofitted frame in x-direction ...... 85

Table 8-10: Updated material performance criteria ...... 88 X Table of Content

Table 8-11: Damage thresholds for FRP retrofitted frame in y-direction ...... 88

Table 8-12: Cross-section of retrofitted columns ...... 89

Table 8-13: Damage thresholds for concrete jacketing retrofitted frame in y- direction ...... 90

Table 9-1 Maximum spectral acceleration of response spectra from GB 50011- 2010...... 96

Table 9-2 Fundamental periods of index building and retrofitted structures ...... 97

Table 9-3: Summary of probability of all damage states in spectral acceleration 1.2g...... 98

Table 13-1 Internal reliability analysis of the online questionnaire ...... 106

Table 13-2 Genders and perceptions ...... 107

Table 13-3 Results of Mann-Whitney test on student perceptions between 2008 and 2013 ...... 107

Table 13-4 Genders and perspectives ...... 108

Table 13-5 Genders and hazard adjustments ...... 108

Table 13-6 Results of Mann-Whitney test on student hazard adjustments between 2008 and 2013 ...... 109

Table 13-7 Earthquake experience and perceptions ...... 109

Table 13-8 Earthquake experience and perspectives ...... 110

Table 13-9 Earthquake experience and hazard adjustments ...... 110

Table 13-10 Results of the online questionnaire from Q19 to Q30 ...... 111

Table 0-1 ...... 148

Table 0-2: Damage views of index building in y-direction ...... 149

Table 0-3: Damage views of FRP retrofitted frame in x-direction ...... 151

Table 0-4: Damage views of FRP retrofitted frame in y-direction ...... 152

Table 0-5: Damage view of concrete jacketing retrofitted frame in y-direction 154

XVII

Chapter 1 Introduction

Section I Background Informatio and Literature Review Section I

Background Information and Literature Review

By Yan Shuang, Zeyue Xue, Linghui Zhou, and Carmine Galasso

19 Chapter 1 Introduction

Introduction

1.1 Research Background

The World Disasters Report 2015 shows a drastic increase in the number of people killed and affected by natural disasters in several developing countries worldwide, particularly in Asia. This worrying trend results from growing urbanization and rapid increase of poorly built housing and infrastructure (including educational and healthcare facilities), uncontrolled land development and overstretched services, which increase both the exposure and vulnerability of populations to natural hazards. Moreover, the World Bank estimates that for some developing countries the cost of recovery from hazardous events each year is greater than their GDP – years of development investment and progress can be wiped out: Disasters can hit developing countries with an economic force that can roll back development gains and exacerbate inequality.

Like other infrastructure, public school buildings constructed prior to adequate building codes, share structural deficiencies common to other buildings of the same structural types in the same setting, but several considerations set school buildings apart from their peers in terms of priority for assessment and retrofit. An unsafe school in a hazard-prone region can incur the loss of the lives of hundreds of school children (a vulnerable population due to their age and their developmental stage) in addition to the potential damage to the property. On the other hand, a safer school can save valuable lives of children, provide a safe haven for the local community, serve as a temporary shelter and help to bring normalcy back to society in times of disaster. The collapse of a school building is particularly devastating to communities, as schools can hold an entire generation (i.e., all children of a certain age range in the society), a community’s future. Risk management of schools in earthquake prone areas has gained increasing attention worldwide. Governments, private sectors, as well as academic institutions have collaborated to address the issue of school safety and resilience (GFDRR, 2016).

When the M 7.9 earthquake struck Sichuan on May 12, 2008, it caused 87,149 dead and 374,643 injured, with numerous buildings destroyed, especially schools were severely damaged during this exceptionally devastating earthquake that is rare in human history (Wang, 2008). The disastrous performance of school buildings during the earthquake caused the deaths of 5,335 pupils (Associated Press, 2009). Among these, 1,200 of 2,600 students in the Beichuan Middle School died or were missing due to the building collapse; 500 students in the Beichuan Maoba Middle School died due to the landslides triggered by the earthquake. Field investigation carried by the joint mission of EEFIT and ELSA -JRC concluded that the main

Chapter 1 Introduction

reason for the poor performance of school buildings was that the previous version of Chinses seismic design code (GB, 2001) was not adequate to resist the demands imposed by Wenchuan earthquake. The large stock of school buildings with irregular topologies, poor construction quality in terms of building materials, and inadequate detailing not conforming to the Chinese seismic design code, offered margins of safety with possible brittle failure (Zhao, Taucer & Rossetto, 2009).

Such catastrophe drew attention to the seismic vulnerability of school buildings.in China and other countries where school were considered especially vulnerable to earthquake disaster. For instance, the collapse of a primary school in San Giuliano, Italy, in the 2002 Molise earthquake killed 27 students, which was prior to the implementation of new seismic zonation and new seismic codes in 2003. Other than inadequate seismic zonation and seismic code at that time, reasons were given in terms of irregular layout, low standard in construction practices, low inspection on construction maintenance and risky structural changes implemented during the operation of the buildings (Dolce, 2004).

As a particularly vulnerable sector of society, the safety and protection of school children should always be emphasised by organisations and governments. A Comprehensive School Safety framework in alignment with Sustainable Development Goals 2015-2030 and Sendai Framework for Disaster Risk Reduction was recently proposed by GADRRRES and shared a vision to reduce all hazard risks to public and private schools. The framework can be decomposed into three pillars with regard to facilities, management and education. The Safe Learning Facilities section pointed out the key responsibilities of education and planning authorities are to implement disaster-resilient design and constructions and to perform assessment and prioritisation plans for retrofitting or replacing unsafe schools (UNISDR, 2015; GADRRRES, 2017).

After the Wenchuan earthquake, the China Development Research Foundation organised an International Training Programme of the Post-Earthquake Reconstruction of Public Facilities in December 2008. The programme drew lessons from OECD, Italy and Turkey in seismic safety and recommended the formulation of a comprehensive scheme for the reconstruction and retrofitting of infrastructures such as schools (Exchange, 2009).

On the basis of the findings from the post-earthquake investigation, the Chinese government issued the updated version of the Standard for Classification of Seismic Protection of Building Constructions (GB, 2008) and the Code for Seismic Design of Buildings (GB, 2010). The post- disaster reconstruction of critical infrastructures was prioritised, and a total number of 8,283 schools were reconstructed or retrofitted (Wenchuan earthquake memorial museum, 2017). The main modifications in the code consist of the updates in seismic protection categories of buildings, seismic zoning map, structural regularity, robustness and multi-protection line of structures (Zhao, Taucher & Lu, 2010). School buildings protection category was improved from SP to EP, which means the school buildings will be designed to one degree higher than the intensity of that seismic zone. The seismic zoning map was modified for 70 earthquake damaged areas in Sichuan, Gansu and Shanxi Province in terms of seismic fortification intensity and the design PGA. Structural regularity and the Strong-column Weak-beam in framed structures was particularly emphasised. The concept of multi-protection line of structures was also modified so that the use of single-span frames was limited.

Although lessons were learnt from past earthquakes with more attention given in modifying structure seismic design, robust framework for assessing and increasing seismic resilience of schools in China is lacking; no formalized guidance exists on improving structural capacity 21 Chapter 1 Introduction and increasing the resilience of schools, except a few examples of retrofit projects. On the other hand, a substantial body of work exists on vulnerability assessment of structures (and its reduction), particularly in developed countries. These resources form the reference framework for assessing the structural vulnerability of schools in CROSSH. However, the project aims to progress beyond the state-of-the-art by using actual (local) data, high fidelity building models for nonlinear dynamic analysis, Bayesian networks to consistently assess the probability of occurrence of different failure modes, multiple-criteria decision-making for resilience improving solutions in the specific regional context. In particular, Beichuan Qiang Autonomous County, Sichuan, China is selected as a case-study location in CROSSH.

Apart from the assessment of structural vulnerability, social science analysis in terms of finding out perceptions, perspectives and hazard adjustments of students regarding earthquakes is assessed as the main method to understand seismic risk, and overcome the barriers to increase resilience of schools and communities to earthquakes, This is due to public risk perceptions towards specific hazards are of great importance in the process of policy making (Frewer, 2004). The agreement of public perceptions concerning hazard adjustments attributes is the key in risk communication decision-making (Lindell et al. 2009), and facilitating effective risk management, thus establishing public confidence to address risks (Vincent and Covello, 2008). Risk perceptions and people’s attitude towards hazard adjustments are tools to test community’s resilience regarding hazards. 444 residents in Dhaka City finished a questionnaire regarding their seismic risk perceptions, and the results show that the majority of participants were ill-prepared for a major earthquake. Hence earthquake awareness and preparedness became a priority (Paul and Bhuiyan, 2010). Similarly, a survey conducted in two districts in Istanbul indicates inadequate safe planning for residences, however, with the help of data, public awareness and seismic risk preparations may improve (Eraybar et al., 2009). 1.2 Aims and Objectives

The main aim of CROSSH is to develop an innovative, advanced, seismic risk and resilience assessment framework for school infrastructure in China. In order to achieve this, the following measurable objectives are proposed: 1) Assess the impact of earthquakes on school buildings through new-analytical vulnerability models; 2) Investigate the enhancement of seismic resilience of schools through retrofitting of school buildings and disseminating a culture of safe schools and safe communities; 3) Overcome the barriers to increase resilience of schools and communities to earthquakes through investigating perspectives and hazard adjustments of students regarding earthquakes; This resilience assessment protocol will be used by Civil Protection and School Authorities to inform their preparedness actions planning and implementation. The project investigates the effectiveness of buildings retrofit measures, early warning provisions and social preparedness measures as means of preventing casualties, reducing economic losses and maintaining functionality of the school infrastructure and its role within the community in the event of natural disasters.

Chapter 1 Introduction

1.3 Report Outline

This report consists four sections with 15 chapters in total, with the first section setting out the framework for the following three sections, which are corresponding to the aforementioned three main research objectives.

Section I is consisted of two chapters. 0 states the background, research motivation, aims & objectives and the outline of this report. Chapter 2 shows a complete literature review of fragility assessment of schools in different counties, the step-to-step guideline of analytical fragility assessment framework, the nonlinear dynamic analysis required for fragility derivation, seismic retrofitting evaluation process of existing buildings, different retrofitting methods and past studies on school retrofitting. It provides readers with a preliminary overview for relevant information on the upcoming survey and analysis.

Section II, which starts from Chapter 3 and ends at Chapter 6, presents seismic exposure and vulnerability modelling for school buildings in Beichuan. Chapter 3 briefly describes the methodology employed in this part. Detailed design of the index building per current Chinese seismic code is also presented. Chapter 4 focuses on developing a school building exposure model through field investigation and defining the index buildings representative for school buildings in Beichuan County. Chapter 5 presents the process of vulnerability assessment in this project through illustrating the numerical modelling of the index building and introducing simplifications made in order to reduce computational efforts. And also depicting the results of cloud analysis and incremental dynamic analysis. Fragility curves are then derived and compared. Finally, this part is concluded in Chapter 6 regarding the fragility assessment of the school buildings in Beichuan County and the improvements of the overall framework.

Section III includes Chapter 7 to Chapter 10, with the aim of investigating the enhancement of seismic resilience of schools through retrofitting of school buildings. Chapter 7 introduces the methodology of doing seismic retrofitting in this project. Chapter 8 presents the interview results for seismic strengthening during the filed investigation and retrofitting strategies applied to the model. Chapter 9 demonstrates the fragility analysis for the index building and retrofitted buildings, followed by a discussion involving structural deficiencies, and the overall effectiveness of applied retrofitting methods and retrofitting strategies. Conclusions for this part are presented in Chapter 10.

With the aim of understanding seismic risk perception, and overcoming the barriers to increase the resilience of schools and communities to earthquakes, perspectives and hazard adjustments of students regarding earthquakes are carried out in Section IV which starts from Chapter 11 and ends at Chapter 15. Chapter 11 introduces the background of doing this analysis and present the importance of understanding risk perception, hazard adjustments and disaster resilience. Chapter 12 presents the online questionnaire and in-depth interviews. Results and discussions are given in Chapter 13 and Chapter 14, separately. Finally, conclusions for this part are described in Chapter 15.

Chapter 2 Literature Review

Literature Review

2.1 Seismic Fragility Assessment of Schools

The poor performance of school buildings in the past earthquakes events has become an ongoing problem in many countries exposed to the high seismic hazard. The proportion of the number of schools with severe damage or even collapse was much higher compared with the general building stock. Many studies have demonstrated that school buildings in the presence of irregular architectural layout are unfavourable for seismic resilience as the damage concentrating in specific parts of a building could lead to global failure (Rodgers, 2012). The presence of long span classrooms and large openings in the infill walls created local weaknesses and excessive ductility demand in reinforced concrete buildings. Furthermore, the corruption of inspection mechanisms in China has to be highlighted, which directly led to the poor construction quality of school buildings. Moreover, deferred maintenance and ineffective retrofits also contributed to the vulnerability of structures to earthquake hazard. In addition, from the perspective of social function, schools act as a key role in post-earthquake rehabilitation by providing emergency shelters after the disaster. It is therefore crucial to propose a comprehensive framework to systematically evaluate the seismic vulnerability of school buildings in regionals exposed to the high seismic hazard. Researches and frameworks on seismic vulnerability assessment of school buildings proposed and used in other countries can be adopted as a reference to develop a framework which can be applied in China.

2.1.1 Philippines

The Philippines has a long history of earthquake hazards with six earthquake events with magnitude higher than seven since 1955. The disastrous M7.2 Bohol earthquake in 2013 caused the collapse of a total 2,300 schools in the Philippines. In the context of high seismic vulnerability of school buildings nationwide, a Safer Communities through Safer Schools (SCOSSO) project was proposed by Galasso et al. (2017) to develop an advanced multi-hazard risk assessment framework for school infrastructures in the Philippines. This project included the assessment of a variety of natural hazards including earthquakes through new analytical & empirical vulnerability models. Initially, a rapid visual survey was performed, and a total number of 115 individual school buildings were surveyed and documented. Building information including the characteristics of the structural system and possible vulnerability factors were recorded along with confidence rating. For the ease of data collection, a rapid visual survey form was drafted, and an Android application was developed to allow instant vulnerability estimation and data extraction.

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The findings of field investigation showed that 71% of school buildings were RC frame structures and the majority (94%) of buildings only consists of single or double storeys. Based on the data collected, a vulnerability index was produced to indicate the proportion of school buildings with various vulnerability level to multi-hazard. For the seismic analysis, based on the collected building information, an index building was designed to represent the general school building stock in the Philippines. A 2-storey RC frame structures were built in SeismoStruct, and a nonlinear dynamic analysis was performed to simulate its performance under 2013 Bohol earthquake records. To finalise the fragility assessment of the index building, the analytical fragility curves with three damage states were constructed using FRACAS (Rossetto et al., 2016).

2.1.2 Italy

The collapse of a primary school in San Giuliano during the Molise earthquake in 2002 alerted the nation to the vulnerability of school buildings in Italy. The disaster was due to inadequate seismic zonation of Italian territory since the area affected by 2002 Molise earthquake was not classified as seismic zones prior to 2003. Thus buildings were not designed to seismic provisions. It was also due to inappropriate extension and restructuring of the original building. Lessons learnt from the tragedy and a prioritisation scheme for seismic intervention in school buildings was developed at national level as a result of this event (Damian et al., 2007). Although the risk-management framework was initially applied to Italian school buildings, it was flexible enough to be adapted to another type of buildings in the future work.

A multi-level risk assessment procedure was identified within the framework to allow the reduction in the size of school building inventory. The school building stock consists of around 60,000 schools that would be difficult to manage and assess. Therefore, high-risk buildings were filtered to reduce the building inventory to a manageable size. The first step of filtering was based on the seismic resistance ability in terms of PGA. The difference between design requirement at the given site and the seismic resistance of the building was computed. However, in this step, assumptions were made in terms of uniform code compliance and constant site amplification factors. Features for individual buildings were also not characterised in detail. The second filter was based on the GNDT vulnerability index, which has been widely used in Italy. The index accounts for the configuration of the structural system and the quality of the construction. Finally, the simplified mechanics-based structural assessment was implemented, specifically, the DBELA methods (Crowley et al., 2004) for RC frame structures and MeBaSe method for masonry structures (Restrepo-Vélez and Magenes, 2004). For both methods, seismic demand was obtained from the Italian seismic code spectrum and the capacity of the buildings was based on the configuration and material properties. The overall framework allowed the prioritisation for seismic intervention and timescales for detailed assessment, which led to the decision making of retrofitting or demolition.

2.1.3 Switzerland

A coherent framework for simulating probabilistic mechanics-based loss scenarios for mid-size building stock was developed by Michel, Hannewald, Lestuzzi, Fäh and Husen (2017). The general framework used OpenQuake engine as the state-of-art tool for seismic risk computation (Michel et al., 2017). Within this framework, a case study was carried out to assess the loss of

Chapter 2 Literature Review school buildings in the city of Basel in Switzerland to different earthquake scenarios. The ground motions were selected from historical events and disaggregation of the 2015 Swiss Probabilistic Seismic Hazard Model. The model includes all the available seismicity information and appropriate ground motion predication equations to estimate the occurrence relationship at the location of interests. Site amplification of ground motion was also considered for the scenario computation due to specific local soil conditions.

Having established the earthquake scenario, the next step in the framework falls into the vulnerability assessment of school buildings in Basel. Around 60 schools were surveyed and 121 school buildings with classrooms were selected for this study. The taxonomy and distribution of school buildings with or without retrofitting were identified. Mainly three types of structures were observed; namely masonry structures, RC bare frame structures and masonry infilled RC structures. The majority of surveyed buildings were built before the introduction of the first seismic code in Switzerland in 1989. In order to derive analytical fragility curves for each type of buildings, a non-linear static analysis method of Lin and Miranda (2008) was selected in which the fundamental period elongate and the damping of the structure increases with increasing deformation. The probability of exceedance in terms of pseudo-spectral acceleration was plotted for each type of buildings, which was then compared with empirical fragility curves.

2.1.4 Iran

More than 90% of Iranian territory is located on the Alpine-Himalayan earthquake belt, which has a long history of seismic activity. In the last century, 14 earthquakes of Mw>7.0 and 51 earthquakes of Mw 6.0~6.9 hit Iran and caused almost 126,000 fatalities. Evidence shows that masonry school buildings in Iran exhibits poor seismic performance during past earthquakes, and was one of the most vulnerable critical infrastructures to seismic hazards. According to the survey organised by the State Organization of Schools Renovation, Development and Mobilization of Iran (SOSRI) on more than 380,000 classrooms in 100,000 school buildings, roughly two-third of the surveyed classrooms have to be demolished or retrofitted (Mahdizadeh,2011). Therefore, for the purpose of improving the seismic performance of school buildings, an index-based loss assessment framework for brick masonry school buildings was proposed by Azizi-Bondarabadi, Mendes, Lourenço and Sadeghi (2016). The school building database in Yazd province in central Iran was provided by SOSRI, including the geometrical features, building materials, type of structural systems, type of seismic resistance systems and state of damage to the structural components. In order to derive the fragility and vulnerability curves, the study proposed a new method that correlated Iranian method (SOSRI, 2015) and the GNDT II level method for 75 masonry schools with different typologies, and then combined the GNDT II level method and Macroseismic method (Giovinazzi, 2005) through a correlation between the PGA and macro-seismic intensity. Empirical fragility curves and the loss estimation for three school typologies were then generated using the proposed method.

2.1.5 China

The Great Wenchuan Earthquake has become the most destructive earthquake event since the 1976 Tangshan earthquake. The maximum induced PGA according to the Wolong observation station was 0.98g in E-W direction with a direct distance of 19 kilometres to the epicentre (Li

9 Chapter 2 Literature Review et al., 2008). However, the affected area according to the Chinese seismic design code at that time (GB, 2001) was classified as intensity 7 with a PGA of MCE =220 cm/s2 (equivalent to Sa,max=0.5g), which was significantly underestimated compared with the actual records. According to the field investigation conducted by Zhao, Taucer & Rossetto (2009), numerous school buildings experienced extensive damage or collapse, typical examples such as the Xuankou Middle School in the Yinxiu Town and Beichuan Middle School in the Beichuan Town, both of which were located close to the rupture surface of the fault.

Although, there is no systematic framework for seismic fragility assessment of school buildings, a thorough post-earthquake study on the seismic damage assessment of RC frame structures in Xuankou Middle School was carried out by Lu et al. (2011). Seismic damage was observed in classroom buildings than in office buildings even though they were designed to the same seismic code and the construction site and year were exactly identical. To investigate the reasons behind it, a collapsed classroom building and a standalone office building were selected and their seismic performance was simulated using nonlinear finite element analysis. Following the nonlinear finite element analysis, the collapse fragility curves of both buildings were compared using incremental dynamic analysis, the results of which indicated the seismic resilience of the classroom building was much lower than that of the office building due to its limited lateral deformation capacity with longer span and higher axial load ratio. The study also highlighted that the implementation of accurate computational model is crucial. The strengthening effect of slabs on beams may result in strong beam weak column failure mode and the inconsideration of footing rotation could lead to column failure. 2.2 Framework for Analytical Fragility Assessment

For the purpose of assessing the seismic performance of buildings, analytical fragility function is derived to quantitatively express the likelihood of incurring different damage states of the assessed building given a certain range of ground motion intensity. Fragility assessment plays an important role in buildings that were not designed to seismic provisions while subjected to seismic hazards. It also forms part of performance-based design process as documented in FEMA P-58 Volume 1 which provides a thorough seismic performance assessment framework of buildings (FEMA, 2012). As described in FEMA P-58, the performance assessment initialised with assembling building performance models and defining earthquake hazards. The results of building response analysis lead to the development of collapse fragility functions. Three types of performance assessment were identified, namely intensity-based, scenario- based and time-based assessments. These methods evaluate the probable performance of a building based on different levels of uncertainty in terms of the magnitude, location and occurrence relationship of future earthquakes.

GEM (Global Earthquake Model) guideline also delivers a comprehensive framework for analytical vulnerability assessment of low/mid-rise buildings (D’Ayala et al., 2015). A step- by-step approach was detailed in the GEM guideline which includes six stages. At the first step, an appropriate sampling technique has to be selected and index buildings have to be identified to represent a building class. Generally, three index buildings are recommended to represent different seismic performance level within the same nominal GEM Taxonomy building typology. However, due to time constrains, one index building would also be sufficient for a small building database. The second step is to define components for response analysis and loss estimation. Both structural and non-structural components need to be included in seismic

Chapter 2 Literature Review vulnerability assessment. With regard to the non-structural components, masonry infill walls for RC buildings are recommended to be modelled and analysed as in most cases they could affect the seismic response of a structure and cause losses in an earthquake event.

Having identified the components monitoring the structural response of the selected index building, the level of modelling complexity need to be decided in consideration of time available, computational efforts and the acceptable epistemic uncertainty. Although a three- dimensional MDoF model is able to capture the details in the structural components as part of lateral load-resisting system, a simplified 2D model can also be implemented as long as it serves the purpose of a study.

In the next step, damage sates at the element and global level have to be identified. In most cases, five damage states are suggested, namely, no damage, slight, moderate, near collapse and collapse. Although many guidelines provided a comprehensive definition of defining damage states for different type of structure systems (FEMA, 2000; FEMA, 2012; ASCE, 2007), a customised definition for each damage states is recommended. Since the nonlinear model of index building inherently incorporates the capacity of each structural element, it makes sense to tailor the damage state thresholds to each index building. In addition, the demand-capacity correlation is introduced that may have a significant impact on the fragility assessment results.

With the identification of the mathematical model and engineering demand parameter for the index building, the following step is to choose analysis type to evaluate the median and dispersion of its structural response. Three commonly used analysis options are available in descending order of complexity; nonlinear dynamic analysis (NLD), nonlinear static analysis (NLS) and nonlinear static analysis based on Simplified Mechanical Models (SMM-NLS). The relatively more accurate method to assess the structural response to ground motion is NLD method which requires time-history analysis under a set of ground motion records. While the NLS analysis is based on the pushover analysis of the structure model and the SMM-NLS analysis is based on smoothed design response spectrum. Finally, building-based fragility curves can be derived in the expression of probability of a damaged state, ds, sustained by the assessed building being reached a given intensity level, IM. The fragility curve is commonly fitted in the form of lognormal cumulative distribution function with a median value and logarithmic standard deviation. The mathematical expression is shown below.

푙푛(퐼푀) − 훼DS│IM 푃(퐷푆 ≥ 푑푠 |퐼푀) = Φ ( ) 푖 훽 where,

Φ is the standard normal cumulative distribution function

훼DS│IM is the lognormal mean of generic structural response conditioned on the ground motion intensity, IM

훽 is the lognormal standard deviation of DS│IM

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2.2.1Uncertainty in Deriving Analytical Fragility Functions

Despite the rapid evolution of computational simulation of structures, analytical curves require a certain level of simplification and assumptions on the numerical model to reduce the computational cost. Such idealised model inevitable introduces epistemic uncertainties in the construction of fragility functions. Some widely implemented simplifications or assumptions such as neglecting the presence of masonry infill walls, may decrease the reliability and accuracy of the analysis results.

The relationship between the level of complicity in analysis/modelling and the associated uncertainty is demonstrated in Fig. 2-1. One should be noticed that reducing or better quantifying uncertainty associated with one component of the procedure does not necessarily means improving overall reliability and robustness of the final results (D’Ayala et al., 2015).

Fig. 2-1 Mixing and matching for numerical modelling/analysis type (D’Ayala et al, 2015)

According to D’Ayala and Meslem (2013), the process of analytical fragility assessment can be divided into three stages, namely structural analysis, damage analysis and fragility analysis. With regard to the structural analysis, the choice of material mechanical properties, geometric configuration and structural detailing has a direct impact on the analysis results. Uncertainty can also be introduced by the selection of ground motion records, since they captured the variation in the seismic source mechanism, attenuation of wave propagation and the site effects on the seismic event (FEMA, 2012). The choice of intensity measure needs to represent the seismological properties of ground motion to allow a robust analysis. For the fragility assessment to be efficient, the structural response to any given level of IM should exhibits a reduced record-to-record variability. In addition, a sufficient IM should remove any bias in seismological parameters such as the magnitude, distance-to-fault, fault rupture mechanism of ground motions (Luco and Cornell, 2007).

With regard to the damage analysis, the consistency between the chosen damage model and the type of analysis, and the representativeness of damage indicator to a structure’s damage states, have a substantial influence on deriving fragility functions (NIBE-FEMA 2013). Apart from the uncertainties identified from structural and damage analysis, the choice of fitting techniques and the sampling methods has a dominate impact on fragility analysis (Wen et al. 2004; Pagnini et al. 2011; FEMA2012). Fig. 2-2 summarises the main stages of analytical fragility assessment and their associated uncertainties.

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Referring to Fig. 2-2, the variation in structural characteristics-related parameters and in mathematical modelling were two main sources of uncertainties in capacity modelling. For instance, in terms of non-ductile reinforced concrete buildings designed to earlier seismic code, study shows that the variation of strength in concrete and masonry has a significant impact on building capacity by increasing the progress of damage conditions for the concrete. Furthermore, modelling RC buildings without infill walls underestimated the risk of damage in predicting the seismic performance of the building (D’Ayala and Meslem, 2013).

Fig. 2-2 Main phases of analytical fragility assessment and their associated uncertainties (Maio and Tsionis, 2015) 2.3 Non-linear Dynamic Analysis

With the rapid development of computational capacity, seismic analysis methods expand from static to dynamic and from linear to nonlinear. Common types of nonlinear dynamics analysis are incremental dynamic analysis (Vamvatsikos and Cornell, 2002), cloud and stripe analysis (Jalayer and Cornell, 2009). Nonlinear dynamics analysis is able to simulate the step-by-step behaviour of a structural model to seismic input in the time domain, i.e. ground motion records. It often requires a detailed hysteretic model of material to describe its behaviour

However, the computed response of the structure can be very sensitive to individual ground motion records due to their unique characteristics. Thus, a suite of far-field ground motion records is often required as per FEMA P-695 (2009) and FEMA P-58 (2012) to reduce record- to-record variation. Ideally, for NLD analysis, one set of 22 ground motion record pairs with a fault-to-site distance larger than 10km is required as the “Far-Field” record set. While both far- field and near-field ground motion records are available, only far-field records are required for collapse fragility assessment. In terms of assessing collapse capacity, the computation of median collapse capacity of a structure is required as per FEMA P-58 (2012).

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2.3.1 Cloud Analysis

Cloud analysis was first proposed by Jalayer (2003) to perform a seismic assessment of a 3- storey reinforced concrete structure located in Los Angeles. However, at this stage, a numerical model was built in 2D thus cannot capture the spatial response of the structure under biaxial seismic loading. Fragility functions were also not derived after the analysis. The complete procedure of Bayesian Cloud Analysis was later on demonstrated by Jalayer et al. (2015) through a case study of performing cloud analysis and fragility assessment of a school structure located in Avellino, Italy. In this study, uncertainties associated with structural modelling were sufficiently addressed by applying different realisations of structural models generated through a standard Monte Carlo simulation. A robust fragility curve was estimated as the average of the generated fragility curves.

Cloud analysis describes a procedure of evaluating structural response subjected to a set of unscaled ground motion records. It requires a large ground motion database thus is computationally demanding. To achieve a complete probabilistic-based seismic fragility assessment, the selected ground motion records are often required to cover a wide range of magnitude. The “cloud” response of the structure subjected to the set of ground motion records are then plotted as EDP against IM. Due to the fact that less ground motion records with higher magnitude are available, the researcher may encounter problems that for well-designed ductile structure, limited data can be obtained for the structure to reach complete collapse. Thus, the derivation of fragility function for complete collapse damage state may be questionable.

2.3.2 Incremental Dynamic Analysis

Incremental Dynamic Analysis (IDA) was first proposed by (Vamvatsikos and Cornell, 2002) in assessing steel moment-resisting frames and now formed part of the performance-based seismic design. A comprehensive procedure of collapse fragility development using incremental dynamic analysis is described in FEMA P-58 (2012).

IDA requires heavy computational efforts to run a large number of nonlinear time history analyses given a suite of ground motions that are systematically scaled to increasing levels of intensity. IDA yields a relationship between IM and EDP, which demonstrates the seismic behaviour of structure for the whole range from elasticity to complete collapse. In the preparation of IDA, seismic inputs are selected to a user-defined spectrum and scaled by an incremental scaling factor, as shown in Fig. 2-3. As illustrated in Fig. 2-4, for the derivation of an IDA curve, nonlinear- time history analysis of the structure is performed for the selected ground motion multiplied by pre-defined incremental scaling factor. As the amplitude of the ground motion incremented, a non-converging numerical error occurs indicating that structure has collapsed.

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Fig. 2-3 Incremental dynamic analysis using increasingly scaled ground motion records

Fig. 2-4 Steps to plot structure response subjected to scaled ground motion records (D’Ayala et al., 2015)

Cubic spline interpolation technique is used to obtain a continuous IDA curve, shown in Fig. 2-5. Ideally, the IDA curve can be interpreted into three stages, as depicted in Fig. 2-6. The first stage consists of a straight line meaning a linear relationship between EDP and IM. Yielding occurs at the end of the elastic region, and beyond which, a series of steps are observed from the plot. It may be explained as the variation in response to small changes in the ground motion intensity. As IM increases, a new damage threshold is reached and at the end, numerical instability occurs where the IDA curve becomes flat. Such process is repeated for all the selected ground motion records and a median IDA curve can then be determined as 50% of all the maximum response recorded at each level of IM. The record-to-record dispersion can be estimated directly in the expression of lognormal standard deviation (Wen et al., 2004).

β = √ln⁡(1 + 퐶표푉2) where, CoV=s.d./mean

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Fig. 2-5 Generating IDA curve using cubic spline interpolation

(a) yielding (b) softening (c) instability

Fig. 2-6 interpretation of structure response according to IDA curve (D’Ayala et al., 2015)

2.4 Seismic Evaluation of Existing Buildings

2.4.1 Introduction of Seismic Evaluation Process

Seismic evaluation is concerned with determining whether buildings are seismically designed and adequately built in order to resist seismic actions (ASCE, 2014). Both ASCE 43-13 and ATC 40 mention that a performance objective should be selected at initiation. In addition, it is important that performance objectives are recognised as the representation of a certain level of performance under a specific level of seismic hazard (ATC, 1996). Building performance can be divided into six levels: immediate occupancy, damage control, life safety, limited safety, collapse presentation, and not considered. In FEMA 356, probabilistic earthquake hazard levels are applied in corresponding earthquake return periods. In Table 2-1 below, each cell represents a discrete rehabilitation objective, where these objectives are grouped into four sections, as shown in

Table 2-2 (FEMA, 2000).

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Table 2-1 Performance levels

Target performance level

Earthquake Mean Immediate Operational Collapse having return occupancy Life safety performance prevention probability of period performance performance level performance exceedance (year) level 50% in 50 72 a b c d years 20% in 50 225 e f g h years 10% in 50 474 i j k l years 2% in 50 years 2475 m n o P

Table 2-2: Rehabilitation objective

Basic safety Enhanced objectives Limited objectives objectives Discrete k + p + any of a, e, i, b, f, j, or n k alone or p alone k + p objectives o alone or n alone or m alone c, g, d, h, l

Information on buildings should be obtained as following (ASCE, 2014):

1. Field observations.

2. Design and construction information and maintenance histories, which could be obtained from the owner or code officials.

3. Codes and standards from the period of design and construction.

4. The testing of existing building materials and components.

Following the preparation steps, there are three stages inherent in a seismic evaluation: Tier 1 involves the screening procedure, which requires rapid evaluation for the potential deficiencies of buildings; Tier 2 is the deficiency-based evaluation, which identifies deficiencies from Tier 1; and Tier 3 is the systematic evaluation, which notably requires an advanced structural analysis. Consequently, the retrofit strategies will be determined based on the evaluation results.

Moreover, the Ministry of Housing and Urban-Rural Development (MOHURD) of China established an updated standard for the seismic evaluation of buildings (GB 50023-2009) following the Wenchuan earthquake. This standard clearly points out that a seismic evaluation

17 Chapter 2 Literature Review involves two levels, which focus on the seismic design requirement and seismic capacity of structural elements, respectively. In the first level, the overall quality of the buildings, such as material strength, structural system and the element connections of buildings, should be identified. The second level is concerned with evaluating the capacity of each element, with the results of the former check combined to conduct the seismic performance of the buildings. Furthermore, buildings are divided into three categories: A, B, and C; these are sorted by design life. The design life of Category A, B and C are 30, 40 and 50 years, separately, whilst the rehabilitation objectives for them are different (MOHURD, 2009). Notably, buildings constructed after 2001 are designed with the hope of them lasting for a period of 50 years. In view of the significant damage to school buildings in the wake of the Wenchuan earthquake, their evaluations require higher standards (Cheng, 2013).

In the Chinese seismic design code (GB50011-2010), the level of seismicity is generally delivered by precaution intensity, referring to peak ground acceleration (PGA) in 10% exceedance of 50 years. The relationship between intensity and PGA are presented in Table 2-3 below (MOHURD, 2010). In terms of the seismic evaluation, a response spectrum is applied, which can be seen as follows.

Table 2-3: Seismic intensities in GB50011-2010

Seismic precaution 6 7 8 9 10 intensity

Design PGA 0.05g 0.1g or 0.15g 0.2g or 0.3g 0.4g 1g

Fig. 2-7 Response spectrum from GB 50011-2010 where:

α is the spectral acceleration

αmax is the PGA in different intensities

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0.05−휉 푑푎푚푝푖푛푔⁡푓푎푐푡표푟 γ is the reduction factor, which can be computed by 훾 = 0.9 + , 휉 = 0.3+6휉 0.05

η1 is the slope correction factor, which takes 0.9 between Tg and 5Tg, and 0.2 between 5Tg and 6Tg 0.05−휉 η2 is the damping correction factor, which can be computed by 휂 = 1 + > 2 0.08+1.6휉 0.55, 표푟 = 0.55

Tg is the characteristic period, which is determined by distance to faults and soil type T is the fundamental period of a linear single degree of freedom (SDOF) system

2.4.2 Push-over Analysis

Based on the as-built information, the structural analysis could be conducted in order to assess the seismic performance of buildings. It is accepted that traditional elastic methods cannot capture many import aspects controlling the seismic performance of structures in the case of strong earthquakes. While non-linear time-history analysis (NLTHA) is complicated and time consuming, which is not practical for all designs (Giannopoulos, 2009). As a result, in order to estimate the seismic demands of buildings, non-linear static push-over analysis is used to derive structure response. Furthermore, this analysis can yield detailed member information, thus increasing the overall efficiency of design and retrofitting (Martino, et al., 2000).

Pushover analysis is a sequence of incremental static analysis carried out to generate the capacity curve of buildings, in terms of spectral acceleration versus roof displacement. This method requires the monitoring of progressive behaviour of structural components (Giannopoulos, 2009). According to FEMA 273, this analysis involves applying a vertical distribution of increasing horizontal loads to a model that monitors the non-linearity of the building (FEMA, 1997). For example, in the manual of SeismoStruct 2016, the applied incremental lateral load P is kept proportional to the pattern of nominal loads (P0), firstly defined by the user: P=λP0. The load factor λ is automatically increased by software until a user-defined limit—or numerical failure—is achieved. Further, the application procedures will be presented in Chapter 8.

However, the conventional pushover method does not clarify the progressive changes in the modal properties during non-linear yielding and cracking in buildings, which lead to period elongation and various spectral amplifications. This is because the constant horizontal load pattern ignores the hidden redistribution of inertia forces and higher mode influences, as cracking and yielding govern the inelastic structural behaviour (Papanikolaou & Elnashai, 2005). Since the structure is stiff and its first mode effects dominate its response, the pushover analysis is more suitable for symmetrical buildings (Martino, et al., 2000).

Therefore, pushover analysis can be recognised as an approach that could obtain deficiencies of buildings. This type of analysis is likely to provide good evaluations of global and local inelastic deformation demands. It also exposes weaknesses in buildings, such as storey mechanisms, excessive deformation demands, strength irregularities and overloading on possibly brittle elements, i.e. columns and beams (Krawinkler & Seneviratna, 1998).

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2.5 Seismic Rehabilitation and Retrofitting for RC Frames

2.5.1 Seismic Deficiencies and Affecting Factors

Regardless of the structural analysis method used, failure to meet the criteria will identify certain seismic deficiencies. These building deficiencies could be distributed into several categories. The categories of deficiency in buildings are pointed out in FEMA 547 as follows: global strength, global stiffness, configuration, load path, diaphragm, component detailing, and foundation. As is apparent, many structures could have more deficiencies in seismic evaluation. For instance, one-storey tilt-up building can be collected into diaphragm deficiency, inadequate global strength or inadequate global stiffness. On the other hand, the mitigation techniques can contribute more than one deficiency. Such categories may help to determine rehabilitation strategies (FEMA, 2006).

Inadequate global strength commonly happens in older buildings, either due to a severe lack of seismic design or the degradation of the strength of material. As can be seen in Chapter 8, the results of the site investigation and interview show that the degradation of concrete strength is common. Global strength refers to the lateral strength of the vertically oriented horizontal force resisting system at the effective global yield point, which could easily be monitored by the pushover analysis (FEMA, 2006).

Inadequate stiffness is frequently the result of the excessive drift demands of a building due to badly detailed components. Global stiffness is the stiffness of the entire lateral resisting system, even though inadequate of stiffness may not be critical at all floors. For instance, critical drift levels occur in the upper floor for buildings with narrow infill walls. At the opposite side, the maximum drift ratio frequently occurs at the lowest floors of frame buildings. Increasing the stiffness of a building is more effective than increasing the global strength when seeking to achieve adequate minimum strength and global displacements (FEMA, 2006).

The deficiency category of configuration refers to soft-storey, re-entrant corner, torsional layout and the failing of walls (FEMA, 2006). Older buildings usually demonstrate torsional response in earthquakes. In the study of the Kocaeli earthquake, as carried out by Sezen, many failures in buildings were found to be attributed to the soft-storey at the ground floor of RC moment-resisting frames (Sezen, et al., 2003), with the same result of the investigation seen in the case of the Beichuan county after the Wenchuan earthquake. Two examples of soft-storey mechanisms can be seen illustrated in Fig. 2-8. In addition, infill walls may also have an impact on soft-storey mechanism and brittle collapses. As a consequence of the analytical modelling after the 2009 L’Aquila earthquake, interaction with infill panels has been seen to lead to a dramatic difference in axial force, in addition to the increase of shear demand in RC columns (Verderame, et al., 2010).

Another important deficiency of RC frame is the inadequate ductility and poor confinement of concrete gravity columns. The component detailing controls the non-linear behaviour of the buildings. Additionally, in regards the category of load path and diaphragms, inadequate connections between structural elements and inadequate in-plain shear capacity, respectively, are seen to be the main deficiencies (FEMA, 2006).

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Fig. 2-8 Soft-storey mechanisms in old Beichuan town

A review of the seismic evaluation by Cheng points out several common deficiencies in old RC buildings in China. Primarily, the most common deficiency is the low strength of concrete due to derogation and the old version of design code. Secondly, the arrangement and strength of steel reinforcement are unable to satisfy the updated code. Furthermore, the connections between the structural elements or the non-structural element and structural element are lacking in strength.

The space between adjacent buildings is not sufficient in adapting the combined seismic deformations, with the collision of the two buildings leading to severe structural damage (FEMA, 2006). In a case study of the Wenchuan earthquake, a great number of damaged structures were subjected to the pounding of adjacent fixed-supported buildings; this phenomenon has since resulted in the modifications of the seismic design for mitigating this deficiency (Wang, 2008). Furthermore, the significant damage of beam-column joints was observed in Dujiangyan city, with its failure seen to be the result of insufficient shear reinforcement and confinement at the joints, as well as a lack of capacity design and irregular plan (Zhao, et al., 2009).

2.5.2 Strategies and Measures of Rehabilitation and Retrofitting

Rehabilitation and retrofitting underscore the mitigation of the deficiencies detailed in the previous subsection. However, the selection of seismic rehabilitation and retrofitting measures is complex and often involves more art than science simply owing to the fact that many factors, with their different attributes, play different roles. Aside from the deficiencies of the structure, other factors, namely cost, seismic performance, short-term disruption, long-term functionality and aesthetics, are also taken into considerations (FEMA, 2006; Thermou & Elnashai, 2006).

Based on the deficiencies, the specific rehabilitation techniques for RC moment-resisting structures are provided in FEMA 547. These techniques are divided into a number of categories, including the addition of new elements, enhancing existing elements, improving connections between two elements, reducing demand, and removing selected components. In Table 2-4, the specific rehabilitation measures of each category can be seen listed. Furthermore, there are several deficiencies that may be identified following the pushover analysis: Global strength, global stiffness, soft-storey mechanism, the ductility of columns and beams, and the shear strength of the column and beam (Krawinkler & Seneviratna, 1998).

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Table 2-4: Seismic rehabilitation measures for RC frames (FEMA, 2006)

Improve Remove Category of Enhance existing Reduce Deficiencies Add new elements connections selected deficiencies elements demand between elements components Remove upper Concrete and masonry stories, Insufficient number Increase size of Global shear walls, steel seismically of frames or weak columns and strength bracing, and concrete or isolate, and frames beams steel frame supplemental damping Insufficient number Increase size of Remove Global of frames or frames Shear walls, steel columns and Supplemental components or stiffness with inadequate bracing, and frames beams, FRP damping create lower stiffness wrapping storey Using the methods as increasing stiffness and Isolation is it Soft-storey strength, and match the possible balance of all floors Configuration provide chords in Re-entrant corner diaphragm Torsional layout Add balancing walls, Failing of walls braced frame, Add or strengthen Load path Inadequate collector collector Lack of ductile Joint Seismic Component detailing-general enhancement isolation detailing Lack of ductile Column jacketing detailing, strong (concrete and

Chapter 2 Literature Review

column and weak steel) beam Inadequate shear FRP wrapping, strength in column steel and concrete or beam jacketing FRP wrapping, Confinement for steel and concrete ductility or splices jacketing Shear walls, braced R/C topping slab Inadequate in-plane frame, and moment overlay, FRP shear capacity frame overlay Inadequate chord Diaphragm Add new chord element capacity Excessive stresses at openings and Add chords Infill openings irregularities Add shallow foundation besides existing shallow or deep foundation, Add Foundation Micropiles Adjacent to an Existing Strip Footing, Enlarge or Replace an Existing Spread Footing

23 Chapter 2 Literature Review

Based on the Chinese seismic design code, the retrofitting measures for RC frame are allocated to two main categories, with one focusing on the strengthening process of the structural elements, whilst the other emphasises the reduction of seismic demand. In the former category, concrete jacketing is one of the conventional measures, in addition to steel jacketing and FRP, which are commonly used in the construction market, as shown in Fig. 2-9. In the latter category, the rubber base isolation and the addition of dampers are recommended (Zhang & Pan, 2005). For example, a seismic evaluation on a four-storey moment-resisting RC structure shows an exceedance of storey drift. Accordingly, a comparison amongst three retrofitting is conducted, with the addition of shear wall as recommended, owing to the fact it can significantly increase the stiffness of structures and its mature construction process. Steel bracing can also effectively improve the stiffness of the structure, and it is commonly used for school buildings in Japan; however, it is not well accepted by Chinese designers as a result of its restricted application situation. In addition, conventional concrete jacketing can be simply applied, although the effects of reducing the maximum drift are not as good as the other two measures (Tian, et al., 2015).

Fig. 2-9 Steel jacketing and FRP wrapping

Shear wall is possible in terms of strengthening RC structures, notably in the cases of ductile beams and weak columns in severe seismic forces. The double use of shear walls can frequently be seen located in a line and linked with a short beam, which bears significant inelastic deformations; this allows the short beam to be enhanced. In addition, existing columns and beams may not be able to support additional shear walls, meaning the improvement of these elements is needed. Another important consideration is the location of the shear wall, which can be installed in either exterior or interior of the structure. The exterior location allows for easier construction, but it may impact the opening size. The challenge of shear wall installation is the connection between the top of the additional wall and the underside of the existing concrete slab. The construction of the joints should be tight without any gaps in order to guarantee the transfer of shear forces from diaphragm into the new shear walls (Higashi, et al., 1980; FEMA, 2006).

Adding steel bracing to an existing RC frame structure is an effective method of increasing the stiffness and strength of the structural system without changing the weight of structure significantly. Based on Eurocode 8, steel bracings are normally divided into two main types, namely concentric bracings and eccentric bracings. Fig. 2-10 presents several common configurations. An experiment on the bracing RC frame and moment-resisting RC frame proved that the addition of steel bracing does not require the change of detailing in the existing RC frame. Furthermore, the important thing about the design is that the failure of the dissipation energy section should precede the failure of connections between steel bracing and existing RC

Chapter 2 Literature Review elements; thus, the details of connections should be designed with care. Another researcher recognises that the connections can be designed by using similar procedures, as bracing is connected to steel structures through the use of adhesives, grout or mechanical anchors. An advanced study by the researcher presents two types of connection; these are shown in detail in Fig. 2-11 (FEMA, 2006; Youssef, et al., 2007; Maheri, et al., 2003; Maheri & Hadjipour, 2003).

Fig. 2-10 Bracing arrangements (FEMA, 2006)

Fig. 2-11 Bracing connections (Maheri & Hadjipour, 2003)

25 Chapter 2 Literature Review

A comparison of traditional and innovative seismic strengthening measures of China is summarised by Tongji University. The FRP is one of the conventional methods widely used for a variety of structural elements. The seismic damper and isolation are regarded as two innovative measures. There are three main different types of the seismic damper, namely viscoelastic damper, friction damper, and viscous fluid damper. The friction damper costs less and is easier to install; however, it reacts only to buildings when severe earthquake actions occur. Seismic isolation has two classifications: rubber isolation and sliding isolation; these can be seen in Fig. 2-12 (Liu, et al., 2012).

Fig. 2-12 Rubber isolation bearing and sliding isolation bearing (Liu, et al., 2012) 2.6 FRP for Retrofitting

2.6.1 FRP Bonding System and De-bonding Failure

Based on ACI 440.2R-08, externally bonded FRP systems come in three different forms: wet lay-up, pre-cured, and prepreg; a number of factors, such as the type of resin, fibre volume and quality of the construction, affect the characteristic of FRP (ACI Committee 440, 2008). A wet lay-up strengthening system is more and more commonly used for the rehabilitation of deterioration and for the understrength of concrete buildings. It contributes flexible application with a variety of width in the field; however, the use of a manual process may result in non- uniform compaction between layers and moisture-related deterioration (Abanilla, et al., 2006). In a pre-cured system, the pre-cured laminates can be bonded with the use of resins or mechanical fasteners. A fatigue experiment on FRP-strengthened beams shows that the use of anchor spikes increase ultimate strength, whilst mechanical fasteners can act as an alternative to epoxy bonded pre-cured laminate systems (Ekenel, et al., 2006). The commonly used resin types include epoxy, vinyl esters, and polyesters, and have been formulated for use in a variety of environmental situations (ACI Committee 440, 2008).

Chapter 2 Literature Review

Fig. 2-13 FRP bonding system (ACI Committee 440, 2008)

It has been proven through theoretical and experimental researches that externally bonded FRP can be effectively used to enhance seismic performance in terms of the stiffness and strength of structural elements. However, one of the challenges of the FRP application is the possible brittle debonding failure, which can significantly decrease the overall effectiveness of retrofitting. Failures normally happen in the region of high-stress concentrations, which are often along with the propagation of debonding. As shown in Fig. 2-14, debonding can occur within adhesives, usually between the concrete surface and adhesion, in the concrete itself, or otherwise between FRP layers. Moreover, failure modes due to plate end debonding and shear and flexural or shear cracks can occur, as shown in Fig. 2-15. There are many factors that can contribute to debonding failure (Buyukozturk, et al., 2004; Aram, et al., 2008; Büyüköztürk & Yu, 2006), with some noted as follows:

• Improper application, such as concrete surface preparation and high temperature gradients during curing, may lead to diagonal cracking and concrete cover separation, as shown in Fig. 2-15a.

• Laminate end and beam support are close, meaning high interfacial shear at the plate end may cause debonding, as shown in Fig. 2-15b.

• The span of the strengthened beam is adequately long and affects proper bond development. Moreover, unsuitable anchoring at plate ends can cause debonding to occur at flexure-shear cracks and propagate towards ends of the beam, as shown in Fig. 2-15c and Fig. 2-15d.

• Moisture at the bond surface can also lead to debonding at the adhesive layer.

Fig. 2-14 Layers of FRP systems (CNR-DT 200 R1/2013, 2013)

27 Chapter 2 Literature Review

Fig. 2-15 Debonding failure mode (Aram, et al., 2008)

In order to avoid debonding failures, proper preparation and workmanship need to be controlled with good quality. For common plate end debonding, the application of anchors or ‘U’-shape wrapping may mitigate this failure. Furthermore, mechanical anchors can change system failure from debonding to the rupture of the FRP (Teng & Chen, 2007).

According to CNR-DT 200 R1/2013, before the design of the flexural and shear, the assessment of the ultimate force moved from concrete to the FRP, whilst the shear normal stresses at the FRP-concrete interface are both required. The maximum value of force transferred to the FRP laminates before debonding is determined by the optimal bond length, led, whilst the estimation equation is shown as follows:

2 1 휋 ∙ 퐸푓 ∙ 푡푓 ∙ Γ퐹푑 푙푒푑 = 푚푎푥 { √ , ⁡200푚푚} 훾푅푑 ∙ 푓푏푑 2 where:

➢ Ef is the modulus of elasticity in the direction of force, tf is thickness of FRP

➢ Γ퐹푑 is the design value of the specific fracture energy

2∙Γ퐹푑 ➢ Fbd is the design bond strength between concrete and FRP, 푓푏푑 = 푠푢

➢ 훾푅푑 is a corrective factor, which is 1.25.

Chapter 2 Literature Review

The design value of specific fracture energy is calculated by:

푘푏 ∙ 푘퐺 Γ = ∙ √푓 ∙ 푓 퐹푑 퐹퐶 푐푚 푐푡푚 where:

➢ 푓푐푚 is the mean value of the concrete compressive strength.

➢ 푓푐푡푚 is the mean value of the concrete tensile strength. ➢ FC is the confidence factor.

➢ KG is an additional corrective factor adjusted from experimental results and equal to 0.023 mm or 0.037 mm for pre-cured and wet lay-up systems, respectively.

➢ Kb is the geometrical corrective factor and function of the ratio between the FRP and

2−푏푓/푏 concrete width, bf/b. kb is defined with 푘푏 = √ ≥ 1; if bf/b<0.25, kb is equal to 1+푏푓/푏 1.18.

Ultimate design strength can be estimated for the shear and flexural design in the following stages. As can be seen from this code, design debonding strengths as a result of two different failure modes are formulated, with their equations are given in Table 2-5:

In China, the design of the FRP system, with its concrete structures, is based on CECS 146:2003 ‘Technical specification for strengthening concrete structures with carbon fibre reinforced polymer laminate’ and GB 50608-2010 ‘Technical code for infrastructure application of FRP 퐸푓∙휀푓 composites’. The optimal bond length is computed by the equation: 푙푒푑 = ≥ 200푚푚, 휏푐푓푏푐푓 where 휏푐푓 = 0.5푀푝푎 is the coefficient of viscosity between FRP and concrete, and 푏푐푓 is the width of FRP laminate. A debonding failure experiment by Lu shows intermediate 0.492 0.086 debondings and computes a debonding strain equation: 휀푓,퐼퐶 = ( − ) 휏푚푎푥, 휏푚푎푥 = √퐸푓푡푓 퐿푑

푏푓 푏푓 1.5훽푤푓푐푡푚, 훽푤 = √(2.25 − )/(1.25 + )⁡, where bc is the width of beam (Lu, et al., 2004). 푏푐 푏푐 Then, it was accepted in updated FRP design code GB 50608-2010, but the coefficients were 1.1 0.2 adjusted. The code-based debonding strain equation is 휀푓,퐼퐶 = ( − ) 휏푚푎푥, 휏푚푎푥 = √퐸푓푡푓 퐿푑

푏푓 푏푓 훽푤푓푐푡푚, 훽푤 = √(2.25 − )/(1.25 + ). Furthermore, the code presents clear and acceptable 푏푐 푏푐 construction steps of FRP, which may mitigate the debonding failure because of the loss of adhesion (MCC, 2003).

29 Chapter 2 Literature Review

Table 2-5: Debonding modes and calculation in CNR-DT 200 R1/2013

Laminate/sheet end debonding (mode 1) Intermediate debonding (mode 2)

ퟏ ퟐ∙푬풇∙횪푭풅 풇풇풅풅 = √ , bond length (lb) longer 휸풇,풅 풕풇

풌 푬 ퟐ∙풌 ∙풌 than optimal bond length (led) 풒 풇 풃 푮,ퟐ 풇풇풇풇,ퟐ = √ ∙ √풇풄풎 ∙ 풇풄풕풎 휸풇,풅 풕풇 푭푪

풍풃 풍풃 풇풇풅풅,풓풊풅 = 풇풇풅풅 ∙ (ퟐ − ) , 풍풆풅 ≥ 풍풃 풍풆풅 풍풆풅

푘퐺,2 is corrective factor, 푘푞 is the

휸풇,풅⁡is between 1.2 and 1.5 coefficient that considers load distributions

2.6.2 Beam Strengthening

Externally bonding FRP reinforcement to the tension surface of a concrete member along its longitudinal length can increase its flexural capacity (Saadatmanesh & Ehsani, 1991). The increments of the overall flexural strength vary from 10% to 160%, which have been documented by several researchers (Meier & Kaiser, 1991; Ritchie, et al., 1991; Sharif, et al., 1991). However, conventional FRP is a lack of ductility and has dissimilar behaviours to steel reinforcement. As a result, the strengthened beam may exhibit a reduction in ductility, and limit the expected strengthening target (Grace, et al., 2002). Based on ACI 440.2R-08, up to 40% of strength increases are more adaptable (ACI Committee 440, 2008). A study presents the failure mechanism of the FRP-strengthened beams when loaded in bending: 1) steel yield–FRP rupture; 2) steel yield–concrete crushing; 3) compression failure; and 4) debonding. Further, the application of the FRP for the flexural resistance of the beam is shown in Fig. 2-16.

Chapter 2 Literature Review

Fig. 2-16 Flexural strengthening of beams

Based on CNR-DT 200 R1/2013, flexural design at the Ultimate Limit State (ULS) of FRP- strengthened members requires that the ultimate moment demand MSd is smaller than flexural capacity MRd. When 푀푆푑 ≥ 푀푅푑, flexural retrofitting is required. The analysis of RC members strengthened with FRP relies on the following fundamental assumptions:

• The plain section remains plain.

• The bond between concrete and FRP, as well concrete and steel reinforcement, is perfect.

• Concrete members do not take tension.

• The constitutive relationship for steel and concrete follows the current building codes.

• FRP is considered to behave as a linear-elastic material.

In the following procedure, FRP has dominant effects on the flexural strength of the strengthened concrete members. The flexural failure is triggered when either of the two conditions is achieved: 1) the maximum concrete compressive strain, 휀푐푢, as defined by the design code; and 2) the maximum FRP tensile strain, 휀푓푑, is met with computation by 휀푓푑 = 휀푓푘 푚𝑖푛 {휂푎 ∙ , 휀푓푑푑}, where 휀푓푘 is the characteristic strain at failure of the strengthened system, 훾푓 휂푎 and 훾푓 are the coefficients. 휀푓푑푑 is the maximum strain due to intermediate debonding, as formulated above. As such, the failure of strengthened members is divided into two regions, whilst the detail for finding the moment resistances is given in Table 2-6. Subsequently, whether or not the moment capacity is larger than the moment demand shall be checked.

According to CECS 146:2003 and GB 50608-2010, concrete strength shall be checked in advance. Should the concrete grade of a structural member be found to be less than C15, FRP cannot be used in the retrofitting of this member. Moreover, the code limits the maximum tension strain of FRP system, which should be less than 0.01. The calculation of moment resistance is also divided into different cases; however, the allocated measures differ from CNR-DT 200 R1/2013. The following figure shows the strain in the FRP-concrete system, with equations shown as follows:

31 Chapter 2 Literature Review

Table 2-6: Calculation of flexural strengthening for beams in CNR-DT 200 R1/2013

Region 1 Reaching the maximum FRP Region 2 Reaching maximum Failure tensile strain 휺풇풅 concrete compressive strain 휺풄풖

휀 FRP 휀 = 휀 휀 = 푐푢 ∙ (ℎ − 푥) − 휀 ≤ 휀 푓 푓푑 푓 푥 0 푓푑

Concrete in 푥 휀 = (휀 + 휀 ) ∙ ≤ 휀 휀 = 휀 compression 푐 푓푑 0 ℎ − 푥 푐푢 푐 푐푢

Steel in 푥 − 푑 푥 − 푑 휀 = (휀 + 휀 ) ∙ 2 ≤ 휀 휀 = 휀 ∙ 2 compression 푠2 푓푑 0 ℎ − 푥 푐푢 푠2 푐푢 푥

Steel in 푑 − 푥 푑 − 푥 휀 = (휀 + 휀 ) ∙ ≤ 휀 휀 = 휀 ∙ tension 푠1 푓푑 0 ℎ − 푥 푐푢 푠1 푐푢 푥

Neutral axis 0 = 휓 ∙ 푏 ∙ 푥 ∙ 푓푐푑 + 퐴푠2휎푠2 − 퐴푠1휎푠1 − 퐴푓휎푓

Moment 1 푀푅푑 = ∙ [휑 ∙ 푏 ∙ 푥 ∙ 푓푐푑 ∙ (푑 − 휆 ∙ 푥) + 퐴푠2휎푠2 ∙ (푑 − 푑2) + 퐴푓휎푓 ∙ 푑1] capacity 훾푅푑

Chapter 2 Literature Review

Fig. 2-17 Strains in the FRP-concrete system from GB 50608-2010

• When the compressive height x is larger than 휉푓푏ℎ, and less than 휉푏ℎ0, 푥 푀 ≤ 푓 푏푥 (ℎ − ) + 푓′퐴′ (ℎ − 푎′) + 퐸 휀 퐴 (ℎ − ℎ ) 푅푑 푐푑 0 2 푦 푎 0 푐푓 푐푓 푐푓 0

′ ′ 푓푐푑푏푥 = 푓푦푑퐴푠 − 푓푦푑퐴푠 + 퐸푐푓휀푐푓퐴푐푓 { 0.8휀 푥 = 푐푢 휀푐푢 + 휀푐푓 + 휀푖

• When the compressive height x is less than or equal to 휉푐푓푏ℎ,

휉푐푓푏ℎ 푀 ≤ 푓 퐴 (ℎ − ) + 퐸 [휀 ]퐴 ℎ(1 − 0.5휉 ) 푅푑 푦푑 푠 0 2 푐푓 푐푓 푐푓 푐푓푏

• When the compressive height x is less than 2a’,

휉푐푓푏ℎ 푀 ≤ 푓 퐴 (ℎ − ) + 퐸 [휀 ]퐴 ℎ(1 − 0.5휉 ) 푅푑 푦푑 푠 0 2 푐푓 푐푓 푐푓 푐푓푏 where: 휉푐푓푏 is the height coefficient of concrete compression failure and FRP fracture reached 0.8휀푐푢 at the same time, and the equation is 푥 = ; 휀푐푢 is the concrete ultimate strain, which 휀푐푢+[휀푓]+휀푖 is 0.0033; 휀푖 is the initial strain of concrete before retrofitting, and is considered 0 in normal 2 cases; [휀 ] is the ultimate tension strain, which is calculated by 푘 휀 ≤ 푚𝑖푛 { 휀 , 0.01}; 푓 푚 푓푢 3 푓푢 푛 퐸 푡 푘 = 1 − 푓 푓 푓, 푛 is the number of FRP layers, 푡 is the thickness of FRP. 푚 420000 푓 푓

Shear strength of RC beams can also be substantially increased through the use of externally bonded FRP system. Common shear-strengthening schemes are given by Chen’s reports and

33 Chapter 2 Literature Review shown in Fig. 2-18. Fibres can be paved in two directions, which effectively reinforce the shear cracks and best in two perpendicular directions. The shear failure modes depend on the FRP rupture and concrete–FRP debonding. The experiments indicate that most of the beams strengthened by FRP wrapping behave like FRP rupture at the failure cases. Moreover, part of the beams strengthened by the ‘U’-shape jacketing failed in this mode (Chajes, et al., 1995; Khalifa, et al., 1998; Teng & Chen, 2007).

Fig. 2-18 Shear strengthening schemes for RC beams by FRP (Teng & Chen, 2007)

According to CNR-DT 200 R1/2013, shear strengthening can be achieved with the application of one layer, or more than one layer, of FRP. Further, the externally bonded system can be placed in different directions.

Fig. 2-19 shows the lateral view of the ‘U-shape FRP system. In this design, the FRP thickness, width, strips’ spacing and fibre angles can affect strengthening. Design calculations are shown as follows:

Fig. 2-19 Lateral view of FRP shear strengthening system

Chapter 2 Literature Review

• The aim of the design is shear resistance greater than shear demand: 푉푅푑 ≥ 푉푠푑

• Shear capacity shall be the minimum value of the two below: 푉푅푑 = 푚𝑖푛{푉푅푑,푠 + 푉푅푑,푓, 푉푅푑,푐} where: 푉푅푑,푠, 푉푅푑,푓, 푉푅푑,푐 are the steel, FRP and concrete contributions to shear resistance, respectively. In the case of an RC beam with a rectangular cross-section and FRP side bond configuration, the equations of computing the three contributions are shown in Table 2-7.

Table 2-7: Calculation of shear strengthening for beams in CNR-DT 200 R1/2013

Contributions Equations and figures Notation

퐴푠푤 is area of steel stirrups

S is spacing of steel stirrups 퐴푠푤 푉푅푑,푠 = 0.9 ∙ 푑 ∙ ∙ 푓푦푤푑 ∙ (푐푡푔훼 + 푐푡푔휃) 푠 푓푐푑 is design concrete strength

푉푅푑,푠 푓푦푤푑 is design steel stirrups strength

d is the distance from the extreme compression fibre to the centroid of tension steel reinforcement

b is the minimum (푐푡푔훼 + 푐푡푔휃) width between 푉푅푑,푐 = 0.9 ∙ 푑 ∙ 푏 ∙ 훼푐 ∙ 0.5 ∙ 푓푐푑 ∙ 2 푉 1 + 푐푡푔휃 tension and 푅푑,푐 compression chords

α and θ

푏푓 is width of the 1 FRP strips 푉푅푑,푓 = 0.9 ∙ 푑 ∙ 푓푓푒푑 ∙ 2 ∙ 푡푓 훾푅푑 푉푅푑,푓 (푐표푡훼 + 푐표푡휃) ∙ 푏푓 푝푓 is spacing of the ∙ 휌푓 FRP strips Ffed is the effective

35 Chapter 2 Literature Review

design strength of FRP shear reinforcement

휸푹풅 = 1.2, for shear

According to CECS 146:2003 and GB 50608-2010, the FRP retrofitting system contains ‘U’- shaped wrapping and complete wrapping. If the condition permits, the completely wrapped FRP is recommended. The clear spacing between the two FRP strips is limited within the range—less than 0.7 times the spacing of transverse steel reinforcement. The design value of shear resistance is 푉푅푑 = 푉푅푑,푐 + 푉푅푑,푓 . The calculation of the shear capacity of reinforced concrete is based on GB 50010. The shear capacity of FRP is calculated by the equations shown below.

Table 2-8: Calculation of shear strengthening for beams in GB 50608-2010 ‘U’-shape FRP system Completely wrapped FRP system

ퟐ풃풇 푽푹풅,풇 = 흓풚흈풇풉풘 (풔풊풏휷 + 풑풇+풃풇 풉풘 푽푹풅,풇 = 푲흉풃풃풇 (풔풊풏휷 + 풄풐풔휷) 풄풐풔휷) 풑풇+풃풇

풔풊풏√푬풇풕풇 흈풇 = 풎풊풏{풇풇풅, 푬풇휺풇풆}, 푲 = 흓 ,흉풃 = ퟏ. ퟐ휷풘풇풄풕 풔풊풏휶√푬풇풕풇+ퟎ.ퟑ풉풘풇풄풕 ퟖ 휺풇풆 = 휺풇풅 √흀푬풇+ퟏퟎ 풃풇 풃풇 휷풘 = √(ퟐ. ퟐퟓ − )/(ퟏ. ퟐퟓ + ) 풑풇′+풃풇 풑풇′+풃풇 ퟐ풏풇풃풇풕풇 푬풇 흀푬풇 = ′ ∙ 풃(풑풇+풃풇) 풇풄풕

All notations are transferred to the

notation shown in CNR-DT 200 R1/2013. 흓풚 is the initial concrete behaviour

풉풘 is the wrapping height in two sides. φ coefficient, which is normally is the coefficient of shear retrofitting, considered as 1. which is considered as 1.3 in this case.

Chapter 2 Literature Review

2.6.3 Column Strengthening

Poorly reinforced columns to the seismic lateral forces are the most dangerous structural elements, which may fail due to flexure, shear, bond at lap splices, the compressive crushing of concrete and reinforcement bar buckling. The seismic retrofitting by FRP material can effectively avoid these failures except in the case of flexure. FRP, like jackets with the fibres in the circumferential direction of columns, largely carries shear-loading and provides confinement. However, the flexural strengthening of columns needs improvement in longitudinal reinforcement; this reinforcement should spread beyond the end of sections, where moments are normally maximum. Therefore, the FRP is not commonly suitable for the flexural strengthening (Bournas & Triantafillou, 2009). In this vein, several studies focus on the anchored FRP system to solve this barrier. The cantilever-type RC column with glass FRP- confining jackets is combined with steel spikes. The columns were tested under a monotonic lateral load, combined with the constant axial load. A comparison of the strength results for both unstrengthened and strengthened columns shows an increment in the range of 33% to 54% (Prota, et al., 2005). A study on the use of longitudinal carbon FRP (CFRP) sheets, combined with carbon-fibre anchors, shows that: 1) carbon fibre anchors flexibly increase the flexural capacity of RC columns subject to seismic actions; 2) the effectiveness of applied anchors depends on their weight; and 3) a fewer number of anchors may increase fixing stability (Vrettos, et al., 2013). Another study on carbon fibre spikes shows that doubling the number of spike anchors results in increasing the anchors’ FRP tensile capacity by 70% (Bournas, et al., 2015).

According to CNR-DT 200 R1/2013, confinement action becomes significant only after cracking of the concrete and yielding of the internal steel reinforcement due to the increased lateral expansion. The axial capacity of FRP-confined columns can be evaluated as follows.

Table 2-9: Calculation of confinement for columns in CNR-DT 200 R1/2013

Equations Notations

푁푆푑 ≤ 푁푅푐푐,푑

1 푓푐푐푑 is the design strength of confined 푁푅푐푐,푑 = 퐴푐푓푐푐푑 + 퐴푠푓푦푑 훾푅푑 concrete

푓1,푒푓푓 2/3 푓푐푚 푓1,푒푓푓 푓푐푐푑 = 푓푐푑 {1 + 2.6( ) }, 푓푐푑 = Confinement to be effective, > 0.05 푓푐푑 퐹푆 푓푐푑

푓1,푒푓푓 = 푘푒푓푓 ∙ 푓1 푘푉,⁡ 푘퐻⁡ 푘 are vertical, horizontal, and 1 훼 = 푘 ∙ ( ∙ 휌 ∙ 퐸 ∙ 휀 ) inclined effective coefficient, respectively 푒푓푓 2 푓 푓 푓푑,푟푖푑

37 Chapter 2 Literature Review

Continuous wrapping 풌푽=ퟏ 푘푒푓푓 = 푘퐻 ∙ 푘푉 ∙ 푘훼

푏′2 + ℎ′2 푘퐻 = 1 − 3 ∙ 퐴푔

퐴푔 = 푏ℎ

1 푘훼 = 2 1 + (푡푎푛훼푓) b is width and d is effective depth of the FRP strengthened columns and other parameters are shown in the two graphs 2 ∙ 푡푓 ∙ (푏 + ℎ) 휌 = 푓 푏ℎ

휂푎휀푓푘 휀푓푑,푟푖푑 = 푚𝑖푛 { ; 0.004} 훾푓

푓푓푘 휀푓푘 = 퐸푓

According to CNR-DT 200 R1/2013, flexural capacity of the FRP-strengthened columns when subjected to the combined axial and flexural force is calculated as follows:

Table 2-10: Calculation of flexural strengthening for columns in CNR-DT 200 R1/2013

Equations Notations

Flexural strengthening for columns should achieve: 푀 ≤ 푀 (푁 ) 푠푑 푅푑 푆푑

푀 (푁 ) is the 푀푒푐ℎ푎푛𝑖푐푎푙⁡푟푎푡𝑖표⁡푟푒푙푎푡푒푑⁡푡표⁡푡푒푛푠𝑖표푛⁡푠푡푒푒푙⁡푟푒𝑖푛푓표푟푐푒푚푒푛푡:⁡휇푠 푅푑 푆푑 flexural capacity 퐴푠1 ∙ 푓푦푑 = of the 푓 ∙ 푏 ∙ 푑 푐푐푑 strengthened column 푏푓 ∙ 푡푓 ∙ 푓푓푑 푀푒푐ℎ푎푛𝑖푐푎푙⁡푟푎푡𝑖표⁡푟푒푙푎푡푒푑⁡푡표⁡퐹푅푃⁡푠푦푠푡푒푚:⁡휇 = considering the 푓 푓 ∙ 푏 ∙ 푑 푐푐푑 design axial force 푁푠푑 푁푠푑 푁표푛 − 푑𝑖푚푒푛푠𝑖표푛푎푙⁡푒푞푢푎푡𝑖표푛⁡푓표푟⁡푎푥𝑖푎푙⁡푓표푟푐푒:⁡푛푠푑 = 푓푐푐푑 ∙ 푏 ∙ 푑 푓푓푑⁡ is the FRP 푀 ultimate design 푠푑 strength 푁표푛 − 푑𝑖푚푒푛푠𝑖표푛푎푙⁡푒푞푢푎푡𝑖표푛⁡푓표푟⁡푚표푚푒푛푡:⁡푚푠푑 = 2 푓푐푐푑 ∙ 푏 ∙ 푑

Chapter 2 Literature Review

A trial and error procedure to evaluate the thickness ( 푡푓 ) of FRP reinforcement: 휂 = 휂푠푑 + 휇푠 ∙ (1 − 휇) + 휇푓 The failure modes of the strengthened FRP system: Region Mode η Description

1 (the section is 1a η0≤η≤η1 FRP rupture and no under steel yielding reinforced)

1b η1≤η≤η2 FRP and steel yielding

2 (the section is 2 η2≤η≤η3 Yielding steel and over reinforced) concrete crushing

2 푟 1.75 ∙ 푟 휂 = −휇 ∙ 휇, 휂 = ∙ , 휂 = 0.8 ∙ , 휂 0 푠 1 3 푟 + 1 2 1.75 ∙ 푟 + 1 3 = 0.51 + 휇푓 ∙ (1 − 푟)

Failure η m(mr)(η) mode

1a η0≤η≤η1 1 휂1(1 − 휂1) − 휂0 푚1푎(η) = ∙ {휂0 + (휂 2 휂1 − 휂0

− 휂0)}

1b η1≤η≤η2 1 푚 (η) = ∙ {휂 ∙ 휂 + [1 − (휂 + 휂 )] ∙ 휂} 1푏 2 1 2 1 2

2 η2≤η≤η3 1 푚 (η) = ∙ {휂 (1 − 휂 ) 1푎 2 2 2 (0.75 − 휂 ) − 휂 (1 − 휂 ) + 3 2 2 (휂 휂3 − 휂2

− 휂0)}

푚푅푑(푛푠푑) = 푚1푏(η) + 0.5[휇푠(1 + 휇) + 휇푓] should exceed 푀푠푑

39 Chapter 2 Literature Review

According to CECS 146:2003 and GB 50608-2010, shear strengthening of columns is the same as in the case of beams, with the axial force capacity of columns is defined in the latter. The maximum strain of confinement for the FRP-strengthened system relates to the shape of the cross-section. Moreover, flexural strengthening by FRP is also clearly pointed out in the two codes. The flexural resistance design of the column is similar to the CNR-DT 200 R1/2013. Code (GB 50608-2010) also allocates these columns into large and small eccentricity groups. The only difference is the limitation of the Chinese design codes:

푁 푛 = 푓푐푐푑퐴푐

Where: N is the axial force acting on the column. Value n, which ranges from 0.65 to 0.90, should satisfy the design criteria in GB50011-2010.

2.6.4 Joint Strengthening

The poor detailing of the concrete beam-column joints is recognised as the principle cause of moment-resisting RC structures. Therefore, the aim of the rehabilitation of beam-column joints is to improve the shear strength of the joints and cut down the potential of the bond-slip of the bottom bars of beams (Al-Salloum & Almusallam, 2007). If the joint acquires high shear stress, diagonal tension is considered to be the major cause of joint failure. Furthermore, an increase in the column axial force leads to the increase of the shear capacity of joints (Almusallam & Al-Salloum, 2007). A variety of configurations of CFRP sheets considered to be the L-shape, X-shape, T-shape and strip combinations can be seen in Fig. 2-20. The result shows the X- shaped FRP wrapping behaves better than the others in terms of strength and ductility (Le- Trung, et al., 2010). According to ReLUIS 2009, the diagonal bands and L-shaped FRP- wrapped are used to rise the shear capacity of joints, as shown in Fig. 2-21. Moreover, fibre anchors can effectively prevent the premature delamination of FRP laminates. In this regard, it is recommended by the author that at least one of the anchors is installed as close as possible to the beam-column interface (Li & Kai, 2011).

Fig. 2-20 Examples of FRP wrapping in beam-column joints

Chapter 2 Literature Review

Fig. 2-21 FRP wrapping for beam-column joints in 3D view (ReLUIS, 2009)

2.7 Summary and Conclusions

In this chapter, the importance of the seismic fragility assessment of schools is emphasised in section 2.1. The assessment framework in different countries are introduced from section 2.1.1 to section 2.1.4. Although many studies have been conducted to investigate the collapse of school buildings during the great Wenchuan earthquake, there is no systematic framework for seismic fragility assessment of school buildings in China. Thus, this project aims to develop a well-rounded seismic fragility assessment framework based on the analytical fragility assessment guidelines detailed in section 2.2. The uncertainties associated with the structural analysis, damage states thresholds and fragility derivation are also discussed in section 2.2.1. Two nonlinear time history analyses methods employed in this study are also discussed in section 2.3.1 and section 2.3.2 respectively. Seismic evaluation process and structural analysis.methods are introduced in section 2.4, which are recognised as helping to determine retrofitting strategies. Retrofitting strategies have been presented for RC buildings in section 2.5 and section 2.6. The overall information will provide readers fundamental information for the upcoming analysis. The following chapters are organised into 3 sections, which are based on the three main objectives as are explained in Chapter 1. For each section, detailed information, including methodology, analysis process, discussion and conclusions, are presented.

41 Part I Seismic Exposure and Vulnerability Modelling

Section II Seismic Exposure and Vulnerability Modelling for SeismicSection Hazard II

Seismic Exposure and Vulnerability Modelling for Seismic Hazard

By Yan Shuang, Linghui Zhou, and Carmine Galasso

Chapter 3 Methodology for Section II

Methodology for Section II

In this project, a seismic fragility assessment framework for school buildings in China was developed. A two-week field investigation of school buildings in Sichuan, China was carried out. Based on the data collected from the field survey, an index building was designed according to current Chinese seismic code GB50011-2010. Following on that, a full-scale three- dimensional numerical model of the index building was developed using OpenSees where the hysteretic behaviours of structural materials were well-defined to capture the strength and stiffness degradation under dynamic loading. Subsequently, the seismic performance of the index building was evaluated through Cloud analysis and Incremental Dynamic Analysis (IDA). Furthermore, fragility functions for the index building were derived based on the performance points from Cloud analysis and IDA. Finally, the seismic resilience of the index building was evaluated in terms of the probability of achieving each damage states at different levels of design spectral accelerations. The current Chinese seismic code was proven to be adequate in seismic resistance to the designed earthquake intensity.

The primary aim of this project is to evaluate the seismic fragility of school buildings in New Beichuan Town and to assess if the school buildings designed to the current seismic code is resilient to earthquake hazard. Prior to the final goal, certain milestones have to be achieved:

➢ Collect data on school building inventory in Beichuan Town and its surround areas ➢ Design an index building based on the results of field investigation ➢ Define hysteretic curve of steel and concrete materials ➢ Build the 3D model of the index building in OpenSees and validate it ➢ Select a set of suitable ground motion records and scale them as appropriate ➢ Define damage states and EDP for the selected index building ➢ Perform nonlinear time history analysis of the model ➢ Perform Cloud analysis and incremental dynamic analysis to construct the fragility curve of the building ➢ Compare the fragility curves obtained from Cloud analysis and IDA

The flowchart of analytical fragility assessment of school buildings is presented in Fig. 3-1. The first step was to construct a database of school buildings in Beichuan.County. A field investigation was carried out in Sichuan, China including New Beichuan County, Old Beichuan County and Mianyang city. A rapid visual survey form was designed for data collection during the survey. A total of 18 school buildings were surveyed and photographed with regard to basic building information and detailed structural information. For framed structures, dimensions of columns and beams were measured and the material of infill walls was estimated. A confidence level was assigned to each assessment criterion.

Since sufficient data has been obtained from Beichuan County, a reinforced concrete index

43 Chapter 3 Methodology for Section II building was designed to the current Chinese seismic code. The geometry of the index building was determined on the basis of field investigation, i.e. average value was taken in terms of number of storeys, number of bays, the height of storey and span of bays.

Having identified the RC index building, the next step was to build a numerical model. To serve the purpose of non-linear time history analysis, hysteretic material behaviours were constructed for unconfined concrete, confined concrete and steel reinforcement. The model was then validated by building an identical model in SeismoStruct and to compare the pushover analysis results.

Having the model verified, two analyses methods were performed for the pre-defined index building, namely, Cloud analysis and Incremental Dynamic Analysis. The cloud analysis employed SIMBAD ground motion database and the incremental dynamic analysis employed FEMA P695 far-field records set. For the cloud analysis, 467 strong motion records were input without any scaling. While for the Incremental Dynamic Analysis, only 15 records from FEMA P695 methodology were selected as input ground motions. To avoid record-to-record variability. Each record was scaled incrementally to allow a complete analysis of index building from elasticity to final collapse. Finally, damage states were defined as per Chinese seismic code GB50011-2010 (2010) and the maximum inter-storey drift ratio was chosen as the engineering demand parameter. The fragility functions were derived from the performance points obtained from Cloud analysis and Incremental Dynamic Analysis using the least square fitting technique.

School building data collection

Defining index building

Selecting earthquake Building computational intensity measures model of structures Defining damage states

Selecting a suitable set of Defining characteristics Defining criteria for ground motion of structural parameters identifying each DS

Non-linear dynamic analysis

Fragility curves derivation

Fig. 3-1 Flowchart for the derivation of fragility functions with analytical method

Chapter 4 Seismic Exposure of School Buildings

Seismic Exposure of School

Buildings

4.1 Field Investigation

A field investigation was carried out in June 2017, where 12 schools located in Beichuan County and 5 schools located in Mianyang City were surveyed, consisting a total of 102 school buildings. During the field investigation based in Beichuan County, the survey team set up at Yongchang Town, then went north along 302 Provincial Rd across An’chang Town, Yong’ a Town, Malibu Village, Leigu Town, Relic Site of Beichuan Old County and Yuli Town. To go further north into the mountainous area, the survey team then took X119 County Rd. leading to more remote towns and villages. Based on field observation, the route into the mountainous area is suspicious to a landslide, for safety reasons, the survey team finalised their work at Kaipingxiang Village. The locations of all investigated schools are plotted in Fig. 4-1.

In both Beichuan County and Mianyang City, the majority of surveyed buildings are teaching buildings, with a small proportion of student accommodation, laboratory and dining hall. Detailed locations of surveyed schools are shown in Fig. 4-1. According to the investigation, all surveyed school buildings in Beichuan County were previously destroyed during 2008 Wenchuan earthquake. The new school buildings were relocated and reconstructed after the disaster, with financial support from either military region or municipal government outside Sichuan province. These schools were built after 2008 Wenchuan earthquake and were designed to the new seismic code. Although Mianyang City was located further away from the epicentre, many school buildings in Mianyang City were retrofitted after the 2008 Wenchuan earthquake; typical examples such as Fule Experimental Primary School and Fule Experimental Middle School.

For the purpose of data collection, a survey form has been designed as shown in the Appendix A. The rapid visual survey criteria from general building information to the detailed structural information are summarised in Table 4-1. Typical vulnerability factors for school buildings were considered in the rapid visual survey form including pounding, soft storey, opening corridors, built on slope or stilts. Structural irregularity is often observed for school buildings in terms of plan, elevation and openings. For framed structures, dimensions of columns and beams were measured and the material of infill walls were estimated. A confidence level was assigned to each criterion.

45 Chapter 4 Seismic Exposure of School Buildings

According to the survey results during field investigation, the most common type of primary structural system for Chinses school buildings is RC frame with floor material of RC slabs. Many school buildings exhibit irregular plan and elevation, typically for a teaching building, was in combination with many regular building blocks connected by expansion joints. As most buildings were newly built, the lateral load resisting system adopted both moment-resisting RC frame structure and RC shear walls for teaching buildings. While for student accommodation, RC shear walls are commonly adopted due to their regular plan and elevation views.

Fig. 4-1 Location of surveyed schools in Beichuan County and Mianyang City

Chapter 4 Seismic Exposure of School Buildings

In terms of building geometry, the average number of storey in Beichuan County was three, and the proportion for a various number of the storey was given Fig. 4-3. For each building block as part of teaching building, the average number of bays in x-direction was 7 and the average number of bays in y-direction was 2. It was later used as the reference to define index building. For the majority of the teaching buildings, the average classroom spans two bays in the x direction and one large bay in the y direction. Large opening was commonly observed at stairs.

With regard to seismic vulnerability, four common factors of structural vulnerability were observed in the surveyed school buildings. Fig. 4-4 illustrates the proportion of surveyed buildings exhibiting various vulnerability factors. According to the survey results, 14% of schools were built on a slope, suspicious to landslide triggered by earthquake events (example in Fig. 4-5 (d)). 43% of school buildings were suspicious of pounding problem, i.e. building closer than 0.2m (example in Fig. 4-5 (e)). However, the critical factor for all surveyed school buildings is opening corridors outside classrooms, which inherently exhibits high irregularity in the plan and elevation, as well as eccentricity in mass and stiffness. Examples of opening corridors are shown in Fig. 4-5 (a) – (h).

Table 4-1 Rapid visual survey criteria for seismic hazard assessment

General Information Building Information Structural Information

School Name No. of Storey Primary Structural System

Building ID No. of Bay Floor Material

GPS Coordinates Storey Height Lateral Load Resisting System

Position Length of Bay Structural Condition

Construction Year Largest Opening Size Connection Quality

Aseismic Devices Distance to Nearby Dimension of Average Rivers Classroom Modifications

Retrofitting Distance to Nearby Dimension of Largest Faults Classroom Vulnerability Factors

47 Chapter 4 Seismic Exposure of School Buildings

7% 7% 14%

29% 64% 79%

Double Storey Three Storey Four Storey Excellent Fair Deteriorated

Fig. 4-2 Structural condition for surveyed Fig. 4-3 Number of storey for surveyed school buildings school buildings

14% 43% 57% 86%

YES NO YES NO

(a) Built on Slope (b) Pounding

21% 0%

79% 100%

YES NO YES NO

Fig. 4-4 Vulnerability factors (a) built on slope (b) pounding (c) opening irregular (d) opening corridor for surveyed school buildings

Chapter 4 Seismic Exposure of School Buildings

(a) Nationality Middle School (b) Xiyuan Middle School

(e) Yuli Elementary School (f) Leigu Bayi Middle School

49 Chapter 4 Seismic Exposure of School Buildings

(g) Yuli Elementary School (h) Leigu Bayi Elementary School

Fig. 4-5 Surveyed school buildings in Beichuan County 4.2 Design of the Index Building

Having conducted the field investigation, a three-storey reinforced concrete index building was therefore designed to be located in the New Beichuan County. Stairs were located in the middle bay, leaving the overall structure symmetrical.

The design strictly followed the current Chinses seismic design code GB50011-2010. According to the updated seismic zonation, the index building was designed to a precautionary seismic intensity of XII corresponding to a design PGA of 0.15g. The total length of building in the x-direction is 31.5m and the total length of building in the y-direction is 9.5m. A perspective view of the index building is shown in Fig. 4-6.

Fig. 4-6 Perspective view of index building

Chapter 4 Seismic Exposure of School Buildings

4.2.1 General structural information

The geometry, load calculation and material properties of the index building are summarised in Table 4-2. Concrete C30 and Grade 3 steel HRB400 are chosen for the entire design of index building. According to Chinese seismic design code GB20011-2010, the design compressive strength of concrete C30 is 14.3MPa and the design tensile strength of steel HRB400 is 360MPa. The 1st mode of vibration (in x-dir) corresponds to a period of 0.23 sec and the 2nd mode of vibration (in y-dir) corresponds to a period of 0.20 sec. It makes sense since the stiffness of the index building in the y-direction is larger than the stiffness in the x-direction.

Table 4-2 General information of the index building

Geometry information

Storey height 3.5m Number of stories 3

Length of x-bay 4.5m Number of x-bay 7 Length of y-bay 7.0m 2.5m Number of y-bay 2 Beam & Column dimensions Column (mm) 500 500 Beam (x) (mm) 300 500 Beam (y) (mm) 300 560 Self-weight and load applied Floor Dead (tons)* Live (tons) Effective area (m2) 3 363.8 7.5 299.25 2 425.9 42.2 265.50 1 425.9 42.2 265.50 Total (tons) 1215.6 92.0 830.25 Material properties Concrete C30 Steel HRB400 Stiffness (kN/m) Floor x-dir y-dir Load above (kN) 3 3.986E+005 4.292E+005 4574 2 4.364E+005 4.950E+005 10868 1 6.205E+005 6.853E+005 17162

Note: dead load includes both self-weight of the structure and code defined dead load.

4.2.2 Earthquake load calculation and seismic check

In order to calculate seismic load, Eurocode 8 requires the determination of design elastic spectrum in expression of spectral acceleration Sa(T1) against vibration period T. While Chinese seismic design code shares similar concept, instead of using spectral acceleration, it is referred as seismic influence coefficient α (in the unit of g), which describes the maximum acceleration of a structure in response to an earthquake. As stated in GB50011-2010 5.1.4, the

51 Chapter 4 Seismic Exposure of School Buildings seismic influence coefficient of a building structure shall be determined according to the seismic precautionary intensity, characteristic period, natural vibration period and damping ratio of structures, among which, the characteristic period is based on the site class and design earthquake group.

New Beichuan County is categorised into site class II and earthquake group 2, corresponding to a characteristic period of 0.40 sec. Since its seismic precautionary intensity is XII with a design PGA of 0.15g, the maximum horizontal seismic influence coefficient is 0.12 for frequent earthquake (return period = 60 years) and 0.72 for rare earthquake (return period = 2475 years). The design spectrum is therefore derived, shown in Fig. 4-7, where four response spectrum curves are plotted, representing different levels of earthquake returning period. In the case of designing index building, the response spectrum for frequent EQ was used as per code. In terms of seismic check, according to GB50011-2010 5.2.5, for the structures with fundamental period less than 3.5s and a site classified as Intensity 7, the seismic shear force coefficient of a storey should be larger than 2.4%, where the coefficient is defined as the ratio between storey shear and the gravity load of the storey. Thus, the earthquake action and seismic check are summarised in Table 4-3. Design capacity check in terms of shear and moment resistance are also shown in Table 4-4 and Table 4-5.

0.8

0.7 T=60 yrs α 0.6 T=476 yrs 0.5 T=2475 yrs 0.4 0.3 0.2

Seismic influence coef. influenceSeismic 0.1 0 0 1 2 3 4 5 6 Period T (s)

Fig. 4-7 Design response spectrum for index building

Table 4-3 EQ load distribution and seismic shear force check

EQ load Seismic shear force coefficient Shear resistance (kN) Floor distribution (kN) check x-dir y-dir x-dir y-dir x-dir y-dir 3 510.71 568.21 553.92 587.99 14.920% 15.838% 2 496.44 544.10 1054.85 1113.74 12.567% 13.268% 1 230.43 256.37 1303.82 1378.90 9.971% 10.546%

Chapter 4 Seismic Exposure of School Buildings

Table 4-4 Shear capacity of index building

Shear capacity (kN) Ratio Floor x-dir y-dir x-dir y-dir 3 2.8655E+003 3.0163E+003 1.00 1.00 2 3.5629E+003 3.8302E+003 1.24 1.27 1 4.5369E+003 5.4736E+003 1.27 1.43

Table 4-5 Moment capacity of index building

Capacity Mr (kNm) Resistance Mov (kNm) Ratio X 2.097E+005 4.113E+002 509.91 Wind Y 6.361E+004 1.345E+003 47.29 X 2.040E+005 9.127E+003 22.35 EQ Y 6.187E+004 9.652E+003 6.41

4.2.3 Detailed cross-section design of columns

All the RC columns have a cross-section of 500mm*500mm. The arrangement of columns at ground floor is shown in Table 4-6 and the arrangement of columns located above ground floor is shown in Fig. 4-9. Each column has its assigned number corresponding to the detailed design of reinforcement shown in Table 4-6 and Table 4-7.

Table 4-6 Detailed cross-section design of columns at ground floor

Cross-section

No. KZ1 KZ2 KZ2a Longitudinal rebars 4Ф22 (corner) + 8Ф20 12Ф18 12Ф20 Transverse rebars Ф8@100 Ф8@100/200

Cross-section

No. KZ3 KZ4 Longitudinal rebars 4Ф20 (corner) + 10Ф20 12Ф16 Transverse rebars Ф8@100/200 Ф8@100

53 Chapter 4 Seismic Exposure of School Buildings

Table 4-7 Detailed cross-section design of columns at first and second floor

Cross-section

No. KZ1 KZ2 Longitudinal rebars 4Ф18 (corner) + 8Ф16 12Ф16 Transverse rebars Ф8@100 Ф8@100/200

Cross-section

No. KZ3 KZ4 Longitudinal rebars 12Ф18 12Ф16 Transverse rebars Ф8@100/200 Ф8@100

4.2.4 Detailed cross-section design of beams

All the RC beam in x-direction have a cross section of 300mm*500mm and all the RC beams in y-direction have a cross section of 300mm*650mm. The arrangement of beams at floor levels is shown in Fig. 4-10 and the arrangement of beams at roof level is shown in Fig. 4-11. Each beam has its assigned number corresponding to the detailed design of reinforcement shown in Table 4-8 and Table 4-9.

Table 4-8 Detailed cross-section design of beams at first and second floor

No. Longitudinal rebars Transverse rebars Ends Mid-span / Top Bottom Top Bottom / KL1 4Ф18 3Ф18 2Ф18 3Ф18 Ф8@100/200 KL2 4Ф20 4Ф20 2Ф20 4Ф20 Ф8@100/200 KL3 4Ф18 3Ф16 2Ф18 3Ф16 Ф8@100/200 KL4 3Ф18 2Ф18 2Ф18 2Ф18 Ф8@100/200

Chapter 4 Seismic Exposure of School Buildings

Table 4-9 Detailed cross-section design of beams at roof level

No. Longitudinal rebars Transverse rebars Ends Mid-span / Top Bottom Top Bottom / WKL1 3Ф16 3Ф16 2Ф16 3Ф16 Ф8@100/200 WKL2 3Ф18 4Ф20 2Ф18 4Ф20 Ф8@100/200 WKL3 2Ф18 2Ф18 2Ф18 2Ф18 Ф8@100/200

55 Chapter 4 Seismic Exposure of School Buildings

Fig. 4-8 design of columns at ground floor

Chapter 4 Seismic Exposure of School Buildings

Fig. 4-9 Detailed design of columns at first and second floor

57 Chapter 4 Seismic Exposure of School Buildings

Fig. 4-10 Detailed design of beams at first and second floor

Chapter 4 Seismic Exposure of School Buildings

Fig. 4-11 Detailed design of beams at roof level

59 Chapter 5 Seismic Vulnerability Assessment

Seismic Vulnerability Assessment

5.1 Numerical modelling of the Index Building

The three-dimensional numerical model of the index building was constructed using the Open System for Earthquake Engineering Simulation v2.5 (OpenSees, 2000). OpenSees has advanced capability for modelling and analysing the nonlinear response of systems using a wide range of constitutive material models, elements, sections and solution algorithms (McKenna, 2009). Its software architecture and open-source approach provided many advantages over commercial software. First, OpenSees provides high flexibility in element and material formulations, along with different approximations of kinematics to account for large displacement and P-delta effects. Second, OpenSees offers an extensive selection of solution procedures and algorithms that researchers can adapt to solve nonlinear static or dynamic problems. The OpenSees script for modelling the index building is attached in the Appendix E. To ease the 3D modelling of the index building, Build-X (2017) was adopted to efficiently generate the 3D model and visualise the action effects of the structure under ground motion.

The configuration of the model and the load distribution strictly followed the design of the index building. The beam and column elements were all defined using fibre sections to allow plastic distribution along the elements. Three uniaxial material models were selected for unconfined concrete, confined concrete and steel reinforcement. The characteristics of the material are detailed in section 5.1.2. The modelling of each beam consists of three elements with different steel reinforcement arrangement, i.e. for elements in the middle section of the beam, bottom reinforcements are larger than top reinforcements, while for end sections of the beam, the situation reversed. It is as expected since beams were designed to resist hogging moment in the end and to resist sagging moment at the middle of the beam.

In terms of load assignment, code-specified loads were uniformly distributed along beam elements. The unit weight of slab was assumed to be 3.75 kN/m2. The dead and live load from corridors and classrooms, as well as the permanent load from infills and parapets were summarised in Table 5-1.

Have the model built, it was then compared to an identical model built in SeismoStruct (2014). The fundamental periods derived from the Eigen analyses of both models are identical and equal to 0.23 sec. In addition, from the pushover analyses of the index buildings modelled in OpenSees and SeismoStruct, the elastic regions of pushover curves were exactly the same.

Chapter 5 Seismic Vulnerability Assessment

Table 5-1 Uniform load distribution in Chinese design code

Dead (kN/m) Live (kN/m) Corridor (1st & 2nd floor) 2 3.5 Classroom (1st & 2nd floor) 2 2.5 Roof 4 0.5 Dead load from infills and parapets (kN/m) Outer infills 15 Corridor parapet 5 Inner infills 13 Roof parapet 4

5.1.1 Simplifications and limitations

Due to the complexity of building a three-dimensional model using OpenSees, the model was simplified to some extent without sacrificing the accuracy in predicting the performance of the index building. Since stirrup legs are designed for columns for the purpose of confinement, the concrete within stirrup legs are modelled by confined concrete material model. For beam elements, stirrup legs are not modelled since the confinement of beam does not affect the seismic performance of the index building. Furthermore, the presence of infill walls and stair core are also neglected to reduce computational effects. Although as mentioned in section 2.2.1, ignoring non-structural components such as infills will underestimate the risk of damage, due to the inherent instability associated with the modelling of infill walls, there are not considered in this project in order to maintain the numerical stability of the analysis using OpenSees.

5.1.2 Hysteretic behaviour of material

In nonlinear time history analysis, the hysteretic behaviour of materials is required to capture the strength and stiffness degradation under seismic actions. Therefore, in this project, the hysteretic loop of concrete and steel were identified using OpenSees uniaxial material model Concrete01 – Zero Tensile Strength (Karsan and Jirsa, 1969) and Steel02 – Giuffré-Menegotto- Pinto Model with Isotropic Strain Hardening (Filippou et al., 1983). The stress-strain relationships are illustrated in Fig. 5-1. The control parameters for concrete C30 and steel HRB400 are shown in Table 5-2, where the concrete compressive strength and the yield strength of the steel are taken as the median value rather than the design value to better model the actual behaviour of the index building.

61 Chapter 5 Seismic Vulnerability Assessment

(a) Concrete01 (b) Steel02

Fig. 5-1 Stress-strain relationships for (a) concrete and (b) steel material models adopted from OpenSees (2000)

Table 5-2 Parameters for uniaxial material model of concrete and steel

Concrete properties (C30) Compressive strength (MPa) 28 Crushing strength (MPa) 6.3 Strain at maximum strength -0.002 Strain at crushing strength 0.0135 Steel properties (HRB400) Yield strength (MPa) 360 Initial elastic tangent (GPa) 200 Strain-hardening ratio 0.02 R0 20 cR1 0.925 cR2 0.15

Notes: R0, cR1 and cR2 are parameters to control the transition from elastic to plastic branches. The values are recommended by the OpenSees guideline.

5.2 Non-linear Dynamic Analysis

5.2.1 Ground Motion Selection

As mentioned in section 2.2.1, the record-to-record variation has a major impact on the results of nonlinear time history analysis. Thus, it is essential to select a set of ground motion records that reflects the seismic characteristics of the construction site. For the Cloud analysis, a large strong ground motion database consisting at least 100 records is required to ensure that sufficient performance points are available for the derivation of fragility functions. For Incremental Dynamic Analysis, only 15 strong ground motion records are required. However, the quality of these records should be carefully controlled to ensure the reliability of analyses results. Therefore, in this study, the SIMBAD database was implemented for cloud analysis and the FEMA p695 specified ground motion set was applied for incremental dynamic analysis. Both databases provide strong ground motion records since these events dominate collapse risk

Chapter 5 Seismic Vulnerability Assessment and generally have longer durations of shaking, which is essential for the fragility assessment of a structure using nonlinear hysteretic models. Spectral acceleration of all the ground motion records in the database was computed corresponding to a 1st mode vibration period of the index building. Spectral acceleration was used as the selection criteria for SIMBAD database for Cloud analysis.

Input ground motion for cloud analysis

The SIMBAD database (Selected Input Motions for displacement-Based Assessment and Design; Smerzini et al., 2014) was used for the cloud analysis of the index building. It consists of 467 pairs of ground motion records, generated by 130 seismic activities that occurred around the world. The majority of the records come from Japanese strong motion networks K-NET and KiK-net of the National Research Institute for Earth Science and Disaster Prevention (NIED), which is a reliable source of high-quality ground motion database. The rest of ground motion records were obtained from Italy and U.S. etc., as detailed in Table 5-3. The characteristics for selected records were summarised as follows: a) Shallow crustal earthquake worldwide with moment magnitude Mw ranging from 5 to 7.3 and epicentral distance Repi approximately less than 30 km. b) Good quality at long periods so that only records for which the high-pass cut-off frequency used by the data provider is below 0.15 Hz. Therefore, most records are from digital instruments, while from digital instruments only those records with a good signal to noise ratios at long periods, typically from large magnitude earthquakes, were retained. c) Availability of VS30 measurements or definition of the Eurocode 8 site class based on quantitative criteria.

Table 5-3 Source of GM records in the SIMBAD database (Smerzini et al., 2014)

No. of Country Data provider records K-NET Japan 220 KiK-net Italy 83 ITalian ACceleromtric Archive ITACA New Zealand 77 Institute of Geological and Nuclear Sciences: GNS 1. Center for Engineering Strong Ground Motion Data United States 44 2. PEER Strong Motion Database 3. U.S. Geological Survey National Strong Motion Project Europe 18 European Strong-Motion Data Base: ESMD Turkey 15 Turkish National Strong Motion Project: T-NSMP Greece 7 Institute of Engineering Seismology and Earthquake Engineering Iran 3 Iran Strong Motion Network ISMN

Input ground motion for incremental dynamic analysis

A comprehensive study of ground motion selection for collapse assessment using nonlinear

63 Chapter 5 Seismic Vulnerability Assessment dynamic analysis has been prepared by FEMA P695 (2009). Although both far-field and near- field ground motion record sets are provided, the methodology specified the use of far-filed record set for collapse evaluation across all applicable Seismic Design Categories, regardless of the location of seismic regions and the classification of soil site. Therefore, the incremental dynamic analysis in this study implemented far-field ground motion records, which includes twenty-two component pairs of horizontal ground motions from sites located greater than or equal to 10 km from fault rupture.

All the strong motion records recommended in FEMA P695 were obtained from the Pacific Earthquake Engineering Research Centre (PEER) Next Generation Attenuation (NGA) database. The records have a PGA ranging from 0.21g to 0.82g with an average value of 0.43g. In terms of magnitude, these records range from Mw 6.5 to Mw 7.6 with an average magnitude of Mw 7.0. These records were obtained from soft rock and stiff soil sites, and from shallow crustal sources (predominantly strike-slip and thrust mechanisms). To avoid event bias, no more than two of the strongest records are taken from each earthquake. In order to reduce computational efforts, only 15 ground motion records were employed, for which the detailed characteristics are shown in Table 5-4 and the response spectrum of employed earthquake records are shown in Fig. 5-2. The spectrum shape of employed ground motions was not a selection criterion as the FEMA p695 far-field record sets are independent of site-specific hazard.

0.1 Spectral AccelerationSpectral (g)

0.01 0.04 0.4 4 Period (s)

Fig. 5-2 Response spectrum of employed ground motion records from FEMA p695

5.2.2 Ground motion scaling

For cloud analysis, SIMBAD database was employed without any scaling. In order to reduce computational efforts, 100 earthquake records with highest spectral acceleration were

Chapter 5 Seismic Vulnerability Assessment employed in cloud analysis. For incremental dynamic analysis, 15 selected FEMA p695 earthquake records were scaled to different spectral acceleration (Sa) levels, ranging from 0.1g to 2.0g with 0.1g incremental steps, to cover a complete performance of structures from elasticity to yielding and final collapse.

Table 5-4 Detailed information and characteristics of employed earthquake records

Recorded Earthquake Recording Station Site- Motions Source EQ ID Distance PGA PGV M Year Name Name (km) (g) (cm/s.)

12012 6.7 1994 Northridge Canyon Country-WLC 26.5 0.48 45

12041 7.1 1999 Duzce, Turkey Bolu 41.3 0.82 62

12061 6.5 1979 Imperial Valley Delta 33.7 0.35 33

12062 6.5 1979 Imperial Valley El Centro Array #11 29.4 0.38 42

12071 6.9 1995 Kobe, Japan Nishi-Akashi 8.7 0.51 37

12082 7.5 1999 Kocaeli, Turkey Arcelik 53.7 0.22 40

12091 7.3 1992 Landers Yermo Fire Station 86 0.24 52

12101 6.9 1989 Loma Prieta Capitola 9.8 0.53 35

12102 6.9 1989 Loma Prieta Gilroy Array #3 31.4 0.56 45

12111 7.4 1990 Manjil, Iran Abbar 40.4 0.51 54

12121 6.5 1987 Superstition Hills El Centro Imp. Co. 35.8 0.36 46

12122 6.5 1987 Superstition Hills Poe Road (temp) 11.2 0.45 36

12132 7.0 1992 Cape Mendocino Rio Dell Overpass 22.7 0.55 44

12141 7.6 1999 Chi-Chi, Taiwan CHY101 32 0.44 115

12151 6.6 1971 San Fernando LA - Hollywood Stor 39.5 0.21 19

65 Chapter 5 Seismic Vulnerability Assessment

5.2.3 Non-linear Dynamic Analysis of the Index Building

Nonlinear time history analysis of the index building was carried out with two sets of ground motion inputs for cloud analysis and incremental dynamic analysis. For the purpose of measuring the performance of the index building, different engineering demand parameters exist to measure the structural response to seismic activities. Since the choice of EDPs can be case-specific, the maximum inter-storey drift ratio (MIDR) was chosen in this project. According to Fig. 5-3, the MIDR and roof drift ratio from cloud analysis were compared. At lower spectral acceleration level, the difference between this two EDPs is negligible. However, at a spectral acceleration higher than 1.5g, the difference can be significant. Thus, in this project, MIDR is believed to best capture the soft-storey failure mechanism and describe the global instability of the structure.

3.0 2.1

2.5 1.8

1.5 2.0 1.2 1.5 0.9 1.0 Sa (T1, 5%) (g) 5%) (T1, Sa Sa (T1, 5%) (g) 5%) (T1, Sa 0.6 MIDR 0.5 IDA Roof drift ratio 0.3 Cloud analysis 0.0 0 0 3 6 9 12 15 0 3 6 9 12 15 Drift ratio (%) MIDR (%)

Fig. 5-3 MIDR and roof drift ratio of the index Fig. 5-4 Comparison between cloud analysis building from cloud analysis and IDA results

Fig. 5-4 presented the structural response in terms of MIDR [%] at different spectral acceleration levels at the fist-mode period of the structure (Sa (T1) [g]) from cloud analyses and incremental dynamic analyses. According to which, the scatter performance points show a good match between the results from IDA and cloud analysis. It can be observed that for both cloud analysis and incremental dynamic analysis, the structure is basically exhibiting elastic behaviour before the MIDR reaches 1% since there is a direct correlation between IM and EDP in the elastic region. Dispersion starts to occur in the inelastic regions and mainly under ground motions imposing large spectral acceleration. In both analyses, collapse damage state was defined either due to the occurrence of numerical instability or an unusually large increase in the storey drift associated with a small increment in spectral acceleration.

Chapter 5 Seismic Vulnerability Assessment

3.0 2.1

2.5 1.8 1.5 2.0 1.2 1.5 0.9 1.0

Sa (T1, 5%) (g) 5%) (T1, Sa 0.6 Sa (T1, 5%) (g) 5%) (T1, Sa 0.5 0.3

0.0 0.0 0 3 6 9 12 15 0 3 6 9 12 15 MIDR (%) MIDR (%)

Fig. 5-5 Cloud analysis results for the index Fig. 5-6 Incremental dynamic analysis results building for the index building

For cloud analysis presented in Fig. 5-5, scatter data concentrated at spectral acceleration ranging between 0.7g to 1.5g. No data was plotted at lower spectral acceleration level as records with Sa (T1) less than 0.7g were filtered at the ground motion selection process. Less data was available at higher capacities due to lack of earthquake records with extreme magnitude. The index building may fail at high intensity levels as Sa(T1) larger than 2g. The structural response of the model is not capable of covering the full range of spectral acceleration.

According to Fig. 5-6, the incremental dynamic analysis yields a full spectrum of structural response up to a spectral acceleration of 2g as the index building was subjected to an incrementally scaled set of ground motion records with an incremental step of 0.1g. The IDA curve initiated as a straight line with minor dispersion at a lower value of MIDR. Dispersion propagates as spectral acceleration increases up to 2g. For the selected ground motion with Sa(T1) of 2g, the MIDR of the index building varies between 2% and 13%.

5.3 Fragility Assessment

5.3.1 Defining Damage-State Thresholds

In order to derive the fragility curves for the index building, a damage scale should be tailored for the index building. There are many past studies and guidelines exist in the literature in terms of defining damage states thresholds for ductile reinforced concrete structures.

FEMA 356 (2000) gives a qualitative guideline on defining damage states based on vertical structural elements. For concrete frames with ductile columns, it suggests that slight damage could be represented by minor hairline cracking and extensive damage could exhibit in shear cracking in columns. If severe joint damage and reinforcing buckling occur, the structural is suspicious to collapse. The transient drift limits are 1%, 2% and 4% for immediate occupancy, life safety and collapse prevention performance level respectively.

67 Chapter 5 Seismic Vulnerability Assessment

The HRC-scale damage state proposed by Rossetto and Elnashai (2003) is considered to be a reliable source for defining the damage states threshold for general RC structures. Six damage states are defined in terms of maximum inter-storey drift ratio (MIDR), from which, slight damage is defined as 0.13% MIDR and the collapse damage is defined as exceeding 4.78% MIDR. The detailed MIDR for each damage states are summarised in Table 5-6.

According to GB 50011-2010 (2010), the reference for achieving performance-based seismic design is discussed in detail. For rare earthquake events, the design code recommends inter- storey drift (ISD) for different performance level during and after earthquake events as illustrated in Table 5-5, where ΔUe represents elastic deformation.

Furthermore, it was the first time that the current Chinese code for seismic design of buildings (GB 50011-2010, 2010) recommended the performance-based seismic design procedure. Four performance levels for RC frame structures were defined as the MIDR equals 0.18%, 0.4%, 0.83% and 2.00%. These drift limits are rather conservative compared with the damage states thresholds stated in FEMA 356 (2000) and Rossetto and Elnashai (2003). However, in this study, since the index building is a representative of all surveyed school buildings, the damage state threshold defined in GB 50011-2010 (2010) was employed.

Table 5-5 Inter-storey drift for different performance level for rare earthquake events

DS1 DS2 DS3 DS4 Transient ΔUe 2ΔUe 4~5ΔUe 7~8ΔUe Permanent Negligible ISD<0.5ΔUe ΔUe 2ΔUe

Table 5-6 Damage states thresholds for general RC frame structures

Reference Operational Life safety Collapse prevention Transient 1% 2% 4% FEMA 356 (2000) Permanent Negligible 1% 4% Slight Moderate Extensive Collapse damage damage damage Rossetto & Permanent 0.13% 0.56% 1.63% 4.78% Elnashai (2003) GB 50011-2010 Permanent 0.18% 0.4% 0.83% 2% (2010)

5.3.2 Derivation of Fragility Curves

The analytical fragility functions for the index building are derived by fitting a parametric model to the performance points obtained from cloud analysis and IDA, presented in Fig. 5-7 and Fig. 5-8. The fragility curves were expressed as the probability of exceeding each damage state given a certain level of ground motion intensity (P(ds ≥ 퐷푖⁡|퐼푀) ) versus the spectral acceleration at the first-mode period of the structure (Sa(T1) [g]). It can be observed that the fragility curves for the same damage state derived from Cloud analysis and IDA are nearly identical.

Chapter 5 Seismic Vulnerability Assessment

Fig. 5-7 Fragility function for the index Fig. 5-8 Fragility function for the index building derived from cloud analysis building derived from IDA

The common fitting techniques used are Maximum Likelihood method (Shinozuka et al., 2000), Least Square method (Baker, 2015) and Generalised Linear Regression method (Basoz and Kiremidjian, 1998). Due to the adequate performance points obtained from Cloud analysis and IDA, the variance within resultant fragility functions using different fitting techniques is negligible. Thus, in this study, Least Square fitting method was used for the fragility derivation. By adopting least square regression, the median (휇) and dispersion (훽) of the fragility curves were computed as shown below.

푚 2 푧푖 푙푛⁡(푥푖⁄휇) 휇, 훽 = 푎푟푔푚𝑖푛휇,훽 ∑ ⁡[( ) − 훷 ( )] ⁡ 푖=1 푛푖 훽 where,

푚 is the number of IM levels

푧푖 is the observed ratio of the structure exceeding a specified damage state at given intensity 푛푖 measure 퐼푀 = 푥푖

훷 represents the standard normal cumulative distribution function (CDF)

In such a way, the sum of squared errors (SSE) between the prediction presented by fragility functions and the fractions obtained from performance points is minimised. The median and dispersion of each fragility curve were computed as shown in Table 5-7. It can be seen that at the slight, moderate and extensive damage states, the median and dispersion of fragility curves derived from Cloud analysis and IDA are extremely close, as also exhibited in Fig. 5-7 and Fig. 5-8. However, the minor difference is observed at complete collapse damage state, as it should be expected. Referring to section 0, the performance points yield by Cloud analysis was mainly concentrated at lower intensity measures. Thus, unlike the uniformly distributed data obtained from IDA, the Cloud data cannot accurately represent the fragility curve of collapse damage

69 Chapter 5 Seismic Vulnerability Assessment state for the index building due to lack of sufficient data at higher intensity measure.

Table 5-7 Median and dispersion of damage state curves derived from cloud analysis and IDA

Damage states Slight Moderate Extensive Collapse

Median 0.21 0.37 0.64 1.22 Cloud Analysis Dispersion 0.31 0.32 0.46 0.67

Median 0.21 0.36 0.61 1.15 IDA Dispersion 0.28 0.29 0.43 0.60

5.3.3 Seismic Resistance Evaluation of the Index Building

As previously mentioned in section 4.2.1, the 1st mode vibration period of the index building in the direction of the weaker axis (x-axis) is 0.23 sec. According to the design response spectrum shown in Fig. 4-7 in section 4.2.2, The design spectral acceleration of the index building can then be determined with respect to different return periods of earthquake events. Therefore, the probabilities of exceeding each damage state at given intensity measure (P(ds ≥ 퐷푖⁡|퐼푀)) are found from the fragility curves and summarised in Table 5-8 and Table 5-9.

Table 5-8 The probability of exceeding each damage states according to the fragility functions derived from Cloud Analysis

퐏(퐝퐬 ≥ 푫풊⁡|푰푴) Earthquake Prob. of occurrence Slight Moderate Extensive Collapse (DS4) return period in 50 years (DS1) (DS2) (DS3) 60 yrs 63% (frequent) 0.02 0 0 0 476 yrs 10% (precautionary) 0.55 0.37 0.03 0 2475 yrs 2% (rare) 0.02 0.43 0.60 0.05

Table 5-9 The probability of exceeding each damage states according to the fragility functions derived from IDA

퐏(퐝퐬 ≥ 푫풊⁡|푰푴) Earthquake Prob. of occurrence Slight Moderate Extensive Collapse (DS4) return period in 50 years (DS1) (DS2) (DS3) 60 yrs 63% (frequent) 0.01 0 0 0 476 yrs 10% (precautionary) 0.52 0.40 0.03 0 2475 yrs 2% (rare) 0.02 0.38 0.65 0.05 Since the incremental dynamic analysis was believed to yield more reliable results compared with Cloud analysis, Table 5-9 is used as the reference in assessing the seismic resilience of the designed index building, and consequently, judging if the current Chinese seismic code (GB 50011-2010) is adequate or not in terms of seismic resistance of school buildings. According to Table 5-9, for frequent earthquake events, i.e. earthquakes with a return period of 60 years, the probability of causing any damage to the index building is zero. For earthquakes with a

Chapter 5 Seismic Vulnerability Assessment probability of occurrence of 10% in 50 years, i.e. return period of 476 years, there is 52% of chance resulting slight damage and 40% of chance causing moderate damage. The probability of causing extensive or even collapse damage is negligible. However, for rare earthquake events, i.e. earthquake with return period of 2475 years, there is a 65% chance that the building will suffer extensive damage. The probability of causing moderate damage is 38%. Although there is a large chance of the building undergoes extensive damage for rare earthquake events, the predicted probability of building collapse is nearly zero. Hence, it may be concluded that the index building presents excellent seismic performance and the design to current Chinese code (GB 50011-2010) is adequate in resisting designed earthquake intensity.

71 Chapter 6 Conclusions for Section II Conclusion

Conclusions for Section II

In this part, a well-developed framework for the fragility assessment of school building in Sichuan, China is presented. The overall framework consists of initial field investigation, the definition of an index building, numerical modelling and non-linear time history analysis of the index building, and finally the derivation of fragility functions.

During the field investigation, 17 schools located in Beichuan County and its surrounding areas were surveyed consisting of 102 school buildings in total. Data was collected by a rapid visual survey of various school buildings in terms of the general configuration, primary structural system, vulnerability factors etc. Measurements were taken for the dimension of beams and columns, the length of bays and the storey heights.

Based on the survey results from field investigation, an index building was designed according to current Chinses code for Seismic Design of Buildings GB50011-2010 (2010). The index building was assumed to be located in the New Beichuan County, and designed to a seismic precautionary intensity of XII.

Having defined the index building, the numerical modelling of the index building employed the state-of-the-art three-dimensional modelling technique using OpenSees. For the seismic performance evaluation of the structure, advanced analysis methods were used and compared, namely Cloud analysis and Incremental Dynamic Analysis. A large set of ground motion records were selected to obtained sufficient performance points of the index building under a spectra of strong ground motion with a wide range of intensity. 100 ground motion records were selected from SIMBAD database for Cloud analysis and 15 far-field ground motion records were selected from FEMA p695 and incrementally scaled up to Sa=2g for Incremental Dynamic Analysis.

For the derivation of fragility functions, four damage states (slight, moderate, extensive and complete collapse) were defined as per code GB50011-2010 (2010). Fragility curves were expressed as the probability of exceeding each damage state given a certain level of ground motion intensity (P(ds ≥ 퐷푖⁡|퐼푀)) versus the spectral acceleration at the first-mode period of the structure (Sa(T1) [g]). The curve was fitted using least square techniques based on the performance points derived from Cloud analysis and Incremental Dynamic Analysis of the index building.

The fragility curves derived from Cloud analysis and Incremental Dynamic Analysis are almost identical with minor difference at collapse damage state. It is within expectation due to naturally lack of extreme strong ground motion inputs for Cloud analysis. At the design spectral acceleration for rare earthquake events, i.e. return period of 2475 years, there is only 5% probability of collapse for the designed index building. At the design spectral acceleration for

Chapter 6 Conclusions for Section II Conclusion frequent and precautionary earthquake events, the chance of incurring building collapse is even negligible. Although variation exists in the surveyed school buildings in Beichuan County, it may be concluded that the representative building presents excellent seismic performance and the design to current Chinese code (GB 50011-2010) is adequate in resisting designed earthquake intensity.

Improvements on the Framework

In this study, only one index building was defined as the representative building of the surveyed school buildings. Ideally, more index buildings with different typologies for elementary school buildings and middle-school buildings would be necessary. Furthermore, the median value of the concrete compressive strength and steel yield strength were used for the modelling of the materials. However, Monte-Carlo simulation is suggested to account for the variation of material properties. Following on that, the lower bound and upper bound of the fragility curves can then be derived.

73 Part II Resilience Improving Solutions

Section III Resilience Improving Solutions Section III

Resilience Improving Solutions

— Structural Seismic Retrofitting

By Zeyue Xue, Linghui Zhou and Dina D’Ayala

Chapter 7 Methodology for Section III

Methodology for Section III

This part mainly focuses on finding out the most commonly used structural strengthening methods in Sichuan Province and designing seismic retrofitting strategies for the index building. As a consequence, seismic risk can be reduced at a certain level. As is introduced in Section II, through the field investigation, an index building is determined and designed based on the latest seismic design code and followed the local construction practice. 3D building model is simulated in OpenSees. Although OpenSees is a very powerful finite element software which is widely used in earthquake engineering, there is no interface in it. In other words, it is impossible to observe the building model’s step by step structure response under seismic forces, which makes it difficult to find out the deficiencies of the building. As a result, interface based finite element software SeismoStruct is used to simulate the 3D model. Different performance criterial can be defined in SeismoStruct. Users can easily see building’s deformation along with the increase of seismic force. Non-linear static pushover analysis is used to obtain capacity curve. Capacity curves are used as the reference to do preliminary design of retrofitting strategies.

The next step focuses on finding a retrofitting strategy of index buildings. The index building is designed through an updated seismic code, meaning the retrofitted aim will concentrate on increasing resistance to large-scale earthquakes, such as that of the 2008 Wenchuan earthquake. During the field investigation in Sichuan, several interviews are carried out amongst a site engineer, design engineer and Chinse university professor, all of whom have retrofitting experience in Sichuan Province. The summary of interviews contributes to the decision of retrofitting the index building. Two main methods for this retrofitting, FRP and concrete jacketing, are selected. There is a loop for retrofitting the index building until the improvement of structure satisfied, as shown in the following flow chart. The failure mechanism of the index building and retrofitted building shall be identified. The improvement of capacity curves can be used as an initial tool to observe the effects of retrofitting. Apart from this, fragility curves obtained by a simplified method are also used to justify the effectiveness of different strengthening methods. In order to have a suitable analytical analysis method to obtain fragility curves, the available analytical methods have been introduced in Chapter 2. However, fragility curves derived from nonlinear static analysis haven’t accounted for the specific seismic ground motion, while nonlinear dynamic analysis involves in large amount of computational work and lots of time and effort. As a result, in this part, a new method is proposed to obtain performance points for each index building, combining both non-linear static and dynamic analysis. Using the capacity curves derived from the detailed 3D models, equivalent simplified single-degree of freedom (SDoF) inelastic oscillator system is built in OpenSees to carry out nonlinear time history analysis (NLTHA) to reduce the operation time. Detailed information will be explained in Chapter 9.

75 Chapter 7 Methodology for Section III

Interviews on retrofitting to obtain strategies 1. Site investigation of schools in Sichuan Province Type of structures Geometry of building

PKPM

2. Design index building Index building design Design Codes GB 50011 – 2010 and GB 50010 – 2010

3D model by SeismoStruct 2016 version

X-direction 3. 3D modelling and pushover Pushover analysis analysis Y-direction

Pushover curves Define damage state

SDoF capacity curves, with damage states

Identify failure mechanism

Apply retrofitting measures

No Check performance of design 4. Determine effective and Satisfy target potential retrofitting strategies No Quantify improvement

Satisfy target

End

Idealised capacity curves

Ground motion selection 5. Fragility analysis Cloud method Fragility analysis Simplified model in OpenSees

Comparison of different retrofitting strategies, 6. Discussion and conclusion choice of best solution for index building

Fig. 7-1 Flow chart of Seismic retrofitting

Chapter 8 Seismic Retrofitting

Seismic Retrofitting of Index

Building

8.1 Structural Analysis in SeimsmoStruct

8.1.1 Non-linear Static Push-over analysis

A three-dimensional RC building is built in SeismoStruct with the information presented in Section II. The structure is subject to vertical and lateral forces, which can represent the phenomenon of an earthquake. In the case of the static pushover analysis, the structure is applied to both sides of the index building using uniform rectangular and inverted triangular load patterns. A phenomenon was found by Rossetto et al. (2016a) and Silva et al. (2014), where the pushover analysis results induce little difference between the two loading patterns, as shown in Fig. 8-1 (Rossetto, et al., 2016; Silva, et al., 2014). The focus of the analysis is the retrofitting strategies of the RC buildings, where the change of the stiffness and strength may affect the triangular loading patterns. Therefore, the quick pushover analysis for a variety of buildings with different methods is needed. Consequently, the pushover curves will be generated through the adoption of a uniform rectangular loading pattern that means all nodes in one direction are subjected to the same value, such as 10kN.

Fig. 8-1 Pushover analysis results for the school building model in x- and y- directions, using triangular and uniform lateral loading patterns

77 Chapter 8 Seismic Retrofitting

After all the forces on the buildings are determined , the pushover analysis of the building is completed. This is the non-linear method, where the building is subjected to a static lateral force and gravity loading. This analysis is displacement-controlled through elastic and inelastic behaviour until the target or ultimate condition. The structure is pushed in two perpendicular directions, with the pushover analysis for the two directions carried out separately. This analysis is implemented with 100 loading steps to achieve the target displacement on the top floor or roof. The pushover curves are plotted by tracking the displacements of the top node, such as node 114 on the model roof.

These pushover curves are concerned with recorded top displacement. In order to define the capacity curves of the structure, multiple degrees of freedom (MDoF) pushover curve shall be converted to a single degree of freedom (SDoF) pushover curve, performed in advance, with the conversion calculation steps presented below. The calculated transformation factor is 1.2534 and 1.2566 for the x and y-direction, respectively.

The computation of equivalent mass (m*) for SDoF, total of mass times mode shape

∗ 푚 = ∑ 푚푖휑푖

Transformation factor

휑푇 ∙ 푀 ∙ 퐽 Γ = 휑푇 ∙ 푀 ∙ 휑

The base shears (Vb) of MDoF are obtained from the analysis results of SeismoStruct. The base shears of SDoF are divided by the transformation factor.

푉 퐹∗ = 푏 Γ The displacements of MDoF are also derived from SeismoStruct. The displacements of SDoF are also divided by the transformation factor.

Δ푑 푑∗ = Γ Subsequently, the pushover curve of SDoF can be plotted in terms of base shear (F*) and displacement (d*). Consequently, the SDoF pushover curves are transferred to the capacity 퐹 curves of the structure by the equations: 푆 = ⁡푎푛푑⁡푆 = 푑∗. The capacity curve shows the 푎 푚∗ 푑 non-linear behaviour of the building in terms of spectral displacement (Sd) versus spectral acceleration (Sa). As per the results, the capacity curves of the two directions can be plotted.

8.1.2 Damage Criteria

As recommended by D’Ayala et al. (2014), fragility analysis is better positioned to obtain four damage sates: slight damage (DS1), moderate (DS2), extensive damage (DS3) and complete

Chapter 8 Seismic Retrofitting damage (DS4), as shown in Figure 30. The thresholds of the four damage states are derived by adopting inter-storey drift ratio (ISD). Each damage state is associated with different structural response characteristics at three levels: the member level (material), the storey level, and the global level. In this study, the global level is combined with the member level, which focuses on the elements’ behaviour in the entire building. The ISD of each damage state will be determined by the first occurrence amongst the two levels. In the results of the pushover analysis, the behaviour of the material can be tracked, as well as the maximum drift ratio. The performance criteria of concrete and steel can be derived from GB 50010-2010 and follows restraints outlined by the SesimoStrcut Manual:

푓푦푑 360 Yield of Steel HRB 400 can be calculated by 휀푦푑 = = = 0.0018 from GB 50010- 퐸푠 200000 2010 section 4.2.

For the design concrete C30, the strain of cracking and spalling of unconfined concrete is – 0.00164, which can be found in GB 50010-2010 Appendix C 2.4.

The maximum strain of concrete, which is less than C50, is 0.0033 from GB 50010-2010 Section 4.1.

Fracture of steel is 0.01, which is pointed out in GB 50010-2002; however, the design focuses on the steel strain 0.01, which can substitute the maximum strain of concrete. It may not represent the fracture strain of steel in a real situation. Following the Sesimostcut Manual, the fracture strain of steel can be assumed as being 0.06.

In Seismostruct, the material of each failure mode shall be coated with different colours, which can assist in determining damage thresholds, as shown in following table.

Table 8-1: Material performance criteria

Material Strain Colour Steel yielding 0.0018 Green Unconfined concrete cracking -0.00164 Yellow Confined concrete crush -0.0033 Orange Steel fracture 0.06 Red

Fig. 8-2 Damage views of building (D’Ayala, et al., 2014)

79 Chapter 8 Seismic Retrofitting

According to ATC-58-2, the damage states at the member level are divided into four states: minor cracking of and yielding at a few locations can be determined as slight damage; the spalling of concrete cover in ductile columns can be described as moderate damage; limited cracking in columns can be defined as extensive damage; and partial or total cracking of columns and beams can be determined as collapse damage. According to Rossetto et al. (2016), moderate damage is determined by cracking in most beams and columns, along with some yielding members, with extensive damage described as some elements reach ultimate strength, whereas collapse is defined by the failure of some columns (Rossetto, et al., 2016). In addition, the Chinese seismic design code (GB 50011-2010) describes the four damage states of buildings with relevant deformation values, as given in Table 8-2. ΔUe represents the ultimate elastic deformation of structures, which can be regarded as maximum ISD for elastic range. ΔUp is the ultimate deformation of non-linear behaviour, which can be defined as ISD of complete damage. This deformation criterion belongs to the storey level.

Table 8-2: Damage states from GB 50011-2010

Deformation Damage states Descriptions (ISD) 5% of structural elements have minor cracking; Slight damage (1.5~2)ΔU minor damage to non-structural components. e Moderate 30% of structural elements have extensive cracking; (3~4)ΔU damage severe damage to non-structural components. e Extensive More than 50% structural elements have extensive <0.9ΔU damage damage or partially failure of columns and beams. p

Collapse Significant failure of columns and beams >ΔUp

Table 8-3: Existing ISD related to damage states for RC buildings (D’Ayala, et al., 2014; Rossetto & Elnashai, 2003)

Slight Moderate Extensive Reference ISD Collapse damage damage damage 0.5%- 1.5%- Version 200 (SEAOC Transient 0.5%2.5% 1.5% 2.5% 1995); ATC-58-2 0.5%- (ATC 3003) Permanent Negligible ISD<0.5% ISD>2.5% 2.5% FEMA 356 (ASCE Transient 1% 2% 4% - 3000) Permanent Negligible 1% 4% - Rossetto & Elnashai Transient 0.13% 1.36% 3.2% 5.68% (2003) GB 50011-2010 - 0.18% 0.4% 0.83% 2%

At the storey level, the ISD represents the performance of the structure. In GEM Guideline, there is a summary of existing descriptions of damage states in terms of ISD, as shown in Table 8-3 (D’Ayala, et al., 2014). A homogenised reinforced concrete damage scale, as developed by Rossetto & Elnashai (2003), is used for index building and bare frames, as given in Table 19. The pushover force is a transient action on the structure; therefore, the transient ISD is considered in this analysis. In GB 50011-2010, the ISDs of the RC frames are also described, also as given in Table 8-3. It is clear that the ISD limitation of GB 50011-2010 is more

Chapter 8 Seismic Retrofitting conservative and relevant to this index building. However, this is considered as criteria in the seismic design, which may not well represent the developed buildings. Also, it is recommended by Zhang that the requirements of ISD are better to be relaxed (Zhang, Zhang, & Liu, 2011).

In summary of these damage criteria, ISD is selected in order to become a unified EDP for the following study. Damage state criteria, as given in Table 8-2, are selected for the following analysis. Moreover, the damage states will be defined for each pushover analysis considering two structural response levels. The member level is controlled by the description of Table 8-2, whilst storey level is controlled by deformation, as also shown in Table 8-2.

8.1.3 Structural Performance of the Index Building

In the x-direction of the building, the beams of the first floor are deformed and failed before other members, meaning the definition of each damage state focuses on the failure of beams. Combined with the criteria summarised above, the damage states at the member level can be determined, and thus are given in Table 8-4 below. The behaviour of elements can be observed in the SeismoStruct deformation view. When the structure reaches each damage sate, its ISD can be derived from SeismoStruct as a result of the displacement of each node. At the storey level, the elastic deformation and collapse deformation shall be determined at the first stage. The former is considered the behaviour before the first yielding of structural elements. The latter is the same as the collapse at the member level. Using the criteria of deformation, as shown in Table 18, the ISD of each damage state at the storey level can be obtained. The results of the ISDs chosen for the damage thresholds can be seen in Table 8-4. Furthermore, the deformation view for each damage state is given in Appendix F.

Table 8-4: Definition of damage thresholds of x-direction of index building

Structural Chosen ISD for Damage response Structural response properties ISD damage state level thresholds Member First cracking of beams 0.61% Slight 0.59% Storey ISD by deformation 0.59% Concrete spalling in some Member 0.74% Moderate beams 0.74% Storey ISD by deformation 1.17% Concrete crushing at partial Member 1.00% Extensive beams 1.00% Storey ISD by deformation 1.18% Member Failure of most beams 1.31% Complete 1.31% Storey ISD by deformation 1.31%

In y-direction the failure mode of the structure differs to the x-direction. The first yielding of the structure happens in the columns on the first floor, and it behaves in soft-storey. Therefore, the maximum ISD shall be calculated by the displacement of a Floor 1 node, such as Node 112, is divided by the height of the storey. With focus on the failure of columns and the beams of soft-storey, the damage criteria at the member level are conducted and shown in the following table. Using the same methodology as in the x-direction, the damage thresholds can be obtained and shown in the following table.

81 Chapter 8 Seismic Retrofitting

Table 8-5: Definition of damage thresholds of y-direction of index building

Structural Chosen ISD for Damage response Structural response properties ISD damage state level thresholds First cracking of columns or Member 0.53% Slight beams 0.42% Storey ISD by deformation 0.42% Some columns or beams occur Member 1.05% Moderate cracking or spalling 0.84% Storey ISD by deformation 0.84% More than half of the columns Member and beams occur cracking, or 1.65% Extensive 1.65% several columns fails Storey ISD by deformation 2.11% Member 50% of columns fail 2.35% Complete 2.35% Storey ISD by deformation 2.35%

Using the pushover analysis procedure as described above, the capacity curves of both directions in terms of spectral displacement (Sd) versus spectral acceleration (Sa) can be obtained. These capacity curves are shown below with different damage thresholds.

3 Sa (m/s^2) 2

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Sd (m) Capacity Curve Slight damage Moderate damage Extensive Damage Collapse

Fig. 8-3 Capacity curve of index building in x-direction

Chapter 8 Seismic Retrofitting

3 Sa (m/s^2)

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Sd (m) Capacity curve Slight damage Moderate damage Extensive Damage Collapse

Fig. 8-4 Capacity curve of index building in y-direction

According to the obtained capacity curves and structural performances, apart from the deficiencies described in Section 2.5.1, several specific deficiencies of the index building are identified during the structural analysis. The adequate strength of the material and component detailing avoid the deficiencies in terms of global strength and stiffness. However, there is a deficiency in the configuration of the y-direction analysis. The increase of strength and stiffness is necessary to match the balance of all floors. Another deficiency is the lack of strength in the partial horizontal components, such as weak beams.

Failure mechanisms in two different directions differ, where one is local failure on the ground floor whilst the other direction behaves in global failure. Hence, it is not sufficient that structural analysis considers only one direction. In the x-direction analysis, the index building behaves with an extremely strong column and weak beam as the seismic design considers resisting the lateral force. In the y-direction, the deficiency of the index building is the weak columns at the first floor. 8.2 Designing of Retrofitting Strategies

8.2.1 Selection of Retrofitting methods

Based on the site interviews during the 2 week field investigation, the following summary can be concluded in terms of seismic retrofitting of RC buildings in Sichuan, China:

83 Chapter 8 Seismic Retrofitting

➢ The design codes applied are mature and reliabl e for retrofitting RC buildings;

➢ Beams are normally retrofitted by FRP and steel-jacketing (bonded steel plate and wrapped steel), whilst columns are usually retrofitted by concrete-jacketing. Embedded steel bars are normally used for the joints, with the NSM commonly used for slabs and beams. In retrofitting RC walls, steel mesh with ferrocement is normally applied.

➢ The common materials of retrofitting are CFRP, steel plate, steel bar, and high strength concrete. The widely used concrete is C30, C35 and C40.

➢ The FRP system applied cannot exceed three layers.

When comparing the pushover curves between two directions, the x-direction behaves with less ductility than the y-direction. As such, the retrofitting methods for these two directions differ in the following stage. The retrofitting of this index building aims at increasing the life safety performance of the school building and avoiding collapse, which can reduce casualties when largescale earthquakes happen. The failure of each element provides ultimate moments and shear forces for designing retrofitting system. Furthermore, improvement in retrofitting will initially be evaluated by comparing pushover curves and their damage thresholds between the index building and the retrofitted building. Moreover, damage states will be an important factor in judging the overall effectiveness of retrofitting. As a retrofitting target, the collapse threshold should be expended by 30% of the original threshold by tracking the roof displacement.

Based on the survey above, FRP and concrete-jacketing are selected for retrofitting this structure. Concrete-jacketing will be applied for columns. FRP will be applied to both beam and columns. FRP is considered a light material, which will not increase the seismic mass of the structure, meaning it is conveniently used in upper stories. There are a number of FRPs in the Chinese construction industry. Based on site investigations and interviews, commonly used FRP is CFRP, which normally can achieve 95% design strength. Grade 2 CFRP and Grade 3 FRP have tensile strength of 3000Mpa and 3500Mpa, respectively, with corresponding tensile modulus at 210Gpa. Furthermore, the laminate properties vary in tensile modulus, with the properties of the two chosen CFRP given in Table 8-6. The different thicknesses of CFRPs are selected to retrofit the index building.

Table 8-6: Properties of applied CFRP

Thickness Tensile Ultimate Tensile of CFRP modulus of strain of strength of Elongation Type of CFRP -tf（mm CFRP -Ef CFRP CFRP ffk (%) ） (N/mm2) εfu (Mpa) 300g CFRP 0.167 210000 0.016 3000 1.4 3g CFRP laminate 1.2 165000 0.017 3000 1.4

Chapter 8 Seismic Retrofitting

8.2.2 Retrofitting of Index Building in x-direction

In the x-direction, initially, failure happens at first-storey beams. It is considered a global failure mechanism; therefore, applying an FRP system is the action necessary to retrofit beams. These beams fail at similar displacements, meaning FRP is applied for all beams at the first floor with the same system, as shown in Fig. 8-5. The design code of FRP (CECS 146-2003 and GB 50608-2010) will be used in the following stages owing to the fact that the two codes relate to the seismic design code of China, and are widely used in Chinse construction industry. CNR- DT 200 R1/2013 is also used to check whether the Chinese code is suitable for applying the FRP system in SeismoStruct as the Chinese design code may be unsuitable for the software developed on Eurocodes.

Fig. 8-5 Failure mode of index building in x-direction

Fig. 8-6 View of applied FRP system in x-direction

From SeismoStruct, the shear forces and bending moment of the beams of the first floor at failure can be obtained and shown as above. The shear capacity of the beams satisfies the demand; therefore, FRP is only designed for increasing flexural capacity. Based on the calculation steps presented in Section 2.6, an Excel file is developed to calculate the required FRP system. With trial and error for the design, the ‘3g CFRP’ is selected for retrofitting all first-floor beams with one layer. The capacities of retrofitted beams will differ from each

85 Chapter 8 Seismic Retrofitting

other, with the increments of beams presented in the following table. It is obvious that the flexural capacities of retrofitted beams increase in great number, as shown in

Table 8-8. Moreover, the moment capacity of their supporting columns was found to be 504.6kNm and 432.7kNm, meaning the capacity design of the retrofitted structure still works.

Table 8-7: Reactions of beams at failure

Beams along x- Maximum shear force Maximum bending Elements direction (kN) (kNm) Exterior side of KL4 175 348 classrooms Interior side of KL3 229 424 classrooms Exterior side of KL3 222 416 corridors

Table 8-8: Flexural capacity of beams before and after retrofitting

Beams along x- Flexural capacity before Flexural capacity after Element direction retrofitting (kNm) retrofitting (kNm) Exterior side of KL4 98.8 382.78 classroom Interior side of KL3 144.7 423.76 classroom Exterior side of KL3 144.7 423.76 corridors

The computed FRP system is applied to the index structures. The retrofitted structure still behaves in global failure and fails at the same elements; however, there is a significant improvement in collapse capacity. The specific amounts are shown in pushover curves. Its damage thresholds are determined by the same criteria as that of the original structure. Subsequently, the ISD of each damage states is determined, as shown in Table 8-9. The elements’ behaviour for each damage state can be seen in Appendix A. The comparison of the retrofitted and original pushover curves with their damage states are shown in Fig. 8-7. The collapse threshold using roof displacement has a 53.6% increment, which is satisfied.

Table 8-9: Damage thresholds for FRP retrofitted frame in x-direction

Structural Chosen ISD for Damage Structural response response ISD damage state properties level thresholds Member First cracking of beams 0.61% Slight 0.59% Storey ISD by deformation 0.59% Concrete spalling in some Member 0.82% Moderate beams 0.82% Storey ISD by deformation 0.9%

Chapter 8 Seismic Retrofitting

Concrete crushing at partial Member 1.71% Extensive beams 1.71% Storey ISD by deformation 1.75% Member Failure of most beams 1.94% Complete 1.94% Storey ISD by deformation 1.94%

6000

5000

4000

3000

2000 Base shear (kN)

1000

0 0 0.05 0.1 0.15 0.2 Sd(m) Original Retrofitted Slight damage Moderate damage Extensive damage Collapse

Fig. 8-7 Comparison of pushover curves between original building and FRP retrofitted building in x- direction

8.2.3Retrofitting of Index building in y-direction

In the y-direction, the soft-storey mechanism occurs at the ground floor. The columns supporting the classrooms fail before the other columns supporting the flexible sides of the corridor, as shown in Fig. 8-8 below. This is because the design considers the weight of the infill walls around the classrooms. Therefore, the retrofitting actions should be applied firstly for the failed columns, as shown in orange in Fig. 8-8. For the failure columns, the maximum bending moment is 533kNm whilst axial force is 918kN, as used in retrofitting.

87 Chapter 8 Seismic Retrofitting

Fig. 8-8 Failure view of index building in y-direction

Fig. 8-9 FRP retrofitted columns in y-direction analysis

Initially, FRP is applied for their confinement and flexural strengthening, as shown in Fig. 8-9. Based on the design codes selected and Table 8-6 above, ‘300g CFRP’ is applied for the retrofitting of the columns, with two layers not satisfying the demand, whereas three layers do. Since FRP confines the behaviour of the concrete, unconfined concrete becomes confined concrete after the confinement strengthening of concrete columns, whereas the ultimate compressive strain increases. Based on the equations below, the ultimate strain and design compressive strength of the concrete are 0.00429 and 19.46Mpa, respectively, whilst the updated performance criteria are shown as follows. In SeismoStruct, the material criteria are applied to all elements, meaning the updated criteria cannot represent the behaviour of other elements without retrofit. This problem is solved by the development of two models: one model with updated criteria; the other with original criteria. The combination views can describe the behaviour of all elements. The results of the pushover analysis show that the failure mode still occurs at the ground floor storey. Thus, when the enhanced FRP system was applied to the index building, the failure mode was still seen to happen at ground floor columns. Even concrete strength was found to increase by 100%, checked by CNR-DT 200 R1/2013, with the local failure not able to be avoided.

0.8 1.45 휀푐푐푢 = 0.0033 + 0.6푘푠푐훽푗 휀푓푢

Chapter 8 Seismic Retrofitting

퐸푓푡푓 6.5 푓푐푐푑 = 푓푐푑 + 3.5 (푘푠푐 − ) 푅 훽푗

푟 푟 ℎ 0.5 퐸 푡 푐 푐 푓 푓 2 2 푘푠푐 = ( + 1.5 + 0.3) ( ) , 훽푗 = , 푅 = 0.5√푏 + ℎ 푏 ℎ 푏 푓푐푘푅

Table 8-10: Updated material performance criteria Material Strain Colour Steel yielding 0.0018 Green Unconfined concrete -0.00429 Yellow cracking Confined concrete crush -0.00429 Orange Steel fracture 0.06 Red

However, the increment of the collapse point was 31.6% through tracking roof displacement, using ‘300g CFRP’ with three layers for retrofitting the columns. Accordingly, it is recognised as satisfied. The pushover curve of this retrofitting method is shown in Fig. 8-10, along with the original pushover curve, whilst the detail of the damage states is shown in Appendix F. Following the same criteria for defining the ISD thresholds for the damage states, the ISDs of the FRP retrofitted building are shown in Table 8-11below. Moreover, the behaviour of the elements are added in Appendix F.

Table 8-11: Damage thresholds for FRP retrofitted frame in y-direction

Structural Chosen ISD for Damage Structural response response ISD damage state properties level thresholds First cracking of columns or Member 2.11% Slight beams 0.47% Storey ISD by deformation 0.47% Some columns or beams occur Member 2.62% Moderate cracking or spalling 0.93% Storey ISD by deformation 0.93% More than half of the columns Member and beams occur cracking, or 3.14% Extensive 3.00% several columns fails Storey ISD by deformation 3.00% Member 50% of columns fail 3.34% Complete 3.34% Storey ISD by deformation 3.34%

89 Chapter 8 Seismic Retrofitting

6000

5000

4000

3000

Base shear (kN) 2000

1000

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Sd(m) Slight damage-original Moderate damage-original Extensive damage-original Collapse-original Slight damge-retrofitted Moderate damage-retrofitted Extensive damage-retrofitted Collapse-retrofitted Retrofitted Original

Fig. 8-10 Comparison of pushover curves between original building and FRP retrofitted building in y- direction

Table 8-12: Cross-section of retrofitted columns

Additional concrete Additional steel and Column Cross-section view and thickness number

KZ1 (ground C30, 100mm HRB 400, 4@16mm floor)

KZ2 (ground C30, 100mm HRB 400, 6@16mm floor)

KZ2a (ground C30, 100mm HRB 400, 6@16mm floor)

Secondly, the concrete-jacketing is applied for retrofitting the index building. Because the soft- storey mechanism occurs at the ground floor, the increase in their column size will not affect the demand of other elements. According to the Code for Design of Strengthening Concrete

Chapter 8 Seismic Retrofitting

Structures (GB 50367-2006), the additional concrete thickness and reinforcement can be computed. In order to satisfy the same demand as the FRP retrofitting, the concrete-jacketing for columns is calculated as shown in Table 8-12. Additionally, the spacing and size of the transverse reinforcements are recognised as the same as in the original column.

The pushover analysis shows the local failure moves from ground floor to first storey. In fact, the measures for the first floor columns are to be applied. However, the improvement is satisfied that the maximum roof displacement increases by 34.3%. The ISD for damage thresholds is obtained using the same method as above, as shown in the following table. Detail pertaining to element behaviour is also given in Appendix F.

Table 8-13: Damage thresholds for concrete jacketing retrofitted frame in y-direction

Structural Chosen ISD for Damage Structural response response ISD damage state properties level thresholds First cracking of columns or Member 0.62% Slight beams 0.47% Storey ISD by deformation 0.47% Some columns or beams occur Member 1.23% Moderate cracking or spalling 0.93% Storey ISD by deformation 0.93% More than half of the columns Member and beams occur cracking, or 3.25% Extensive 3.25% several columns fails Storey ISD by deformation 3.375% Member 50% of columns fail 3.75% Complete 3.75% Storey ISD by deformation 3.75%

91 Chapter 8 Seismic Retrofitting

7000

6000

5000

4000

3000 Base shear (kN) 2000

1000

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Slight damage-original Sd(m) Moderate damage-original Extensive damage-original Collapse-original Slight damge-retrofitted Moderate damage-retrofitted Extensive damage-retrofitted Collapse-retrofitted Original Retrofitted

Fig. 8-11 Comparison of pushover curves between original building and concrete jacketing retrofitted building in y-direction

8.2.4 Combination of Two Directions

Combined retrofitted strategies in two directions are also implemented owing to the fact they are recognised as potentially able to represent the real effects of retrofitting. One combination approach is through the FRP retrofitted beam and concrete-jacketing retrofitted columns. The other is FRP retrofitted beams and columns. The pushover analysis of the two combinations is only applied in the x-direction as the FRP retrofitted beams of the x-direction do not influence lateral resistance of the structure and thus do not show failures in the y-direction analysis. The combined strategies are then tested in the x-direction. Using the same process as above, a combination of FRP retrofitted beams and concrete-jacketing retrofitted, as well as a combination of FRP retrofitted beams and columns, are evaluated, with their pushover curves obtained, as shown in Fig. 8-12 and Fig. 8-13.

Overall, in the x-direction analysis, the index building is retrofitted by the FRP for beams. In the y-direction analysis, the building is retrofitted through two methods, namely FRP and concrete-jacketing. Both measures are applied for the same columns. Moreover, the combination of the methods defined in different directions is also assessed. In addition, a fragility analysis is also applied for the retrofitted buildings in an effort to define the effectiveness of these retrofitting methods

Chapter 8 Seismic Retrofitting

6000

5000

4000

3000

Sa(m/s^2) 2000

1000

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Sd(m) Original Retrofitted Slight damage Moderate damage Extensive damage Collapse Fig. 8-12 Comparison of pushover curves between original building and combined FRP retrofitted building in the x-direction analysis

7000

6000

5000

4000

3000

Base shear (kN) 2000

1000

0 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Sd(m) Original Retrofitted Slight damage Modreate damage Extensive damage Collapse

Fig. 8-13 Comparison of pushover curves between original building and combined FRP and concrete retrofitted building in the x-direction analysis

93 Chapter 9 Fragility Assessment

Fragility Assessment without and with Seismic Retrofitting

9.1 Fragility Curves

As is introduced at the beginning, in this part, a new simplified SPO-based cloud method is used to develop fragility curves. Cloud method, which is based on simple regression of structural response versus seismic intensity for a set of registered records, is applied to get fragility functions. Instead of doing hundreds of full non-linear time history analysis (NLTHA) on the detailed 3D simulated finite-element structural models, which is quite time consuming, a simplified method is derived. An equivalent single-degree of freedom (SDoF) inelastic oscillator system is used to represent the structural response of the original multiple-degree of freedom (MDoF) structure under static push-over (SPO) analysis. The force-deformation law governing the SDoF’s response to monotonic later loading will be implemented by idealizing the MDoF push-over curve into backbone curve as is shown in Fig. 9-1.

Fig. 9-1 Backbone curve for hysteretic models (Ibarra, Medina and Krawinkler, 2005).

With the structural capacity of each index buildings by equivalent SDoF oscillators modelled in the open-source software for nonlinear structural analysis OpenSEES and the seismic demand by a certain amount of ground motion records, a series NLTHA can be carried out to obtain a distribution of damage conditional on a level of seismic intensity within a very short time. Here, SIMBAD database, which has been introduced and used in Section II. is used to do cloud analysis. With all the derived performance points, least-square regression is done to derive fragility curve with the new defined damage states.

Chapter 9 Fragility Assessment

Taking advantage of this simplified method, fragility curves are derived for both the original building model and those strengthened by different methods. They are shown in Figure 9.2. to Fig. 9.5.

100% 90% 80% 70% 60% 50% 40% 30% 20% Probability Probability of Exceedance (%) 10% 0% 0 1 2 3 4 5 Sa (g)

Slight Moderate Extensive Complete

Slight FRP Moderate FRP Extensive FRP Collapse FRP

Fig. 9-2 Fragility curve of FRP retrofitted and original building in x-direction

100%

90%

80%

70%

60%

50%

40%

30%

20% Probability Probability of Exceedance (%) 10%

0% 0 1 2 3 4 5 Sa (g) Slight Moderate Extensive Complete Slight Concrete Jacketing Sight FRP Moderate CJ Moderate FRP Extensive CJ Extensive FRP Complete damage CJ Complete damage FRP

Fig. 9-3 Fragility curve of FRP, concrete jacketing retrofitted and original building in y-direction

95 Chapter 9 Fragility Assessment

100%

90% 80%

70%

60%

50%

40%

30%

20% Probability Probability of Exceedance (%) 10%

0% 0 1 2 3 4 5 Sa (g) Slight Moderate Extensive Complete Slight retrofitted Moderate retrofitted Extensive retrofitted Complete retrofitted Fig. 9-4 Fragility curve of combined FRP and concrete jacketing retrofitted and original building in x- direction

100%

90%

80%

70%

60%

50%

40%

30%

20% Probability Probability of Exceedance (%) 10%

0% 0 1 2 3 4 5 Sa (g) Slight Moderate Extensive Complete Slight FRP Moderate FRP Extensive FRP Collapse FRP

Fig. 9-5 Fragility curve of combined FRP retrofitting and original building in x-direction

Chapter 9 Fragility Assessment

9.2 Effectiveness of Applied Retrofitting Methods

R esponse spectrum is used to define the seismic behaviour of the index building. Response spectra are provided in GB50011-2010 and have been explained in Section 2.1. There are several parameters required to be determined. The characteristic period is 0.4s, which is determined by the distance to faults and soil type. Distance to fault is distributed to three zones: close (Zone 1), middle (Zone 2) and long distance (Zone 3). Based on the zone map, Mianyang City is in Zone 2. The soil type is considered medium soft, which has shear wave velocity of between 140 and 250 m/s. Damping ratio 휉 is assumed as 5%. αmax is designed PGA, which is 0.15g in the design. However, response spectrum based on 2%–3% exceedance in 50 years is used to characterise seismic behaviour and retrofitting effectiveness. αmax of intensity 8 is 1.2g will be selected for the response spectrum, as derived from Table 9-1. Moreover, the response spectrum is given in Fig. 9-6.

Table 9-1 Maximum spectral acceleration of response spectra from GB 50011-2010

Earthquake having probability of Intensity Intensity Intensity Intensity 6 exceedance 7 8 9 63% in 50 years 0.04g 0.12g 0.24g 0.32g 2~3% in 50 years 0.28g 0.72g 1.2g 1.4g

1.4

1.2

0.8

0.6

0.4 Spectral acceleration Sa (g) 0.2

0 0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Period T1 (s)

Fig. 9-6 Response spectrum for 2%–3% exceedance in 50 years at intensity 8

The fundamental period of index-building and retrofitted building are given in Table 9-2. They are all close 0.2s; hence, spectral accelerations are defined as 1.2g from Fig. 9-6above. Moreover, a study on the strong ground motion characteristics of the Wenchuan earthquake demonstrates that the buildings with a natural period 0.2s reacted in spectral acceleration 1g

97 Chapter 9 Fragility Assessment with distance to fault 10km, as shown in Fig. 9-7 below. Consequently, the spectral acceleration 1.2g is selected in checking the overall effectiveness of the applied retrofitting methods.

Table 9-2 Fundamental period of index building and retrofitted structures

Concrete FRP FRP Index Index jacketing retrofitted retrofitted Period building x- building x- retrofitted building x- building y- direction direction building y- direction direction direction T1 0.233s 0.233s 0.212s 0.212s 0.198s

Fig. 9-7 The spectral acceleration over various fault distances, together with the attenuation models for period of 0.2s (Wen, et al., 2010)

From the fragility curves in Section 9.1, the probability of collapse and other damage can be obtained in spectral acceleration 1.2g. There is a summary shown in Table 9-3 below. In terms of applying the FRP system for beams in the x-direction analysis, the collapse probability is reduced by 14%. In the y-direction, there are two retrofitting methods applied: FRP and concrete-jacketing. The improvement of FRP for collapse capacity is less than the enhancement by concrete-jacketing because the design demand for the two methods is identical. Only 4% probability of collapse reflects that retrofitting by concrete-jacketing is efficient. The reduction of extensive damage, which can be described as life safety, can reduce the causalities of students and staff because there is high human density across China. Improvement for resisting extensive damage is achieved through such methods.

Chapter 9 Fragility Assessment

Table 9-3: Summary of probability of all damage states in spectral acceleration 1.2g

Probability of exceedance Over Over Over Collapse (%) Slight Moderate Extensive Original in x-direction 75 59 38 24 FRP retrofitted in x-direction 71 48 22 10 Original in y-direction 80 52 24 14 FRP retrofitted in y-direction 84 54 12 9 Concrete jacketing retrofitted 76 39 6 4 in y-direction Combined FRP retrofitted beams and concrete retrofitted 72 48 22 11 columns in x-direction Combined FRP retrofitted beams and columns in x- 71 48 22 10 direction

However, the resistance for slight and moderate damage is negligibly enhanced. The limited increments in slight and moderate damage thresholds are due to the definition given by GB 50011-2010. In the building with large lateral resistance, the storey level criteria (ISD) will control the thresholds. The two damage thresholds are as 1.5 and 3 times the maximum ISD before structures yields. Should the retrofit aim to improve the resistance of slight and moderate damage, the solution is to increase the strength and stiffness of all elements, which is extremely solid to be achieved.

From Table 9-3 above, the retrofitted method of the x-direction is not affected by applying FRP to the columns of the ground floor, although there is minor influence by concrete-jacketing to these columns. This shows that the retrofitting strategies can be evaluated in two directions and combined. However, this is just a specific case; in other words, there are no actions on lateral resistance for retrofitting of the x-direction analysis.

Summary

In retrofitting the local failure of the y-direction, the application of FRP cannot prevent the soft-storey at the ground floor—even a significant development for compressive strength of confinement concrete. However, concrete-jacketing helped to avoid the failure of the index building at the ground and to move the soft-storey to the first floor. It shows an increasing stiffness of a building is more effective than increased global strength when seeking to achieve adequate storey displacements.

Another important action is the matching of the balance of all floors. The concrete-jacketing transferred the soft-storey mechanism from the ground floor to the upper floor, as a consequence of which the retrofitting shall apply for the upper in order to avoid its failure. As a matter of fact, this method still setbacks local failure. If it can achieve the retrofitting target, the soft-storey mechanism is still adopted. Thus, in some cases, achievement for balance may result in an over-design situation.

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Conclusions for Section III

Firstly, the deficiencies of a school building cannot be assessed only by screening. The deficiencies may result in unexpected design and strength irregularities, such as local failure and very weak beams. Moreover, there are many irregular school buildings in China, which considered aspect of aesthetics. Therefore, it is necessary to conduct a structural analysis of existing school buildings in both directions—even if it has been designed in consideration to a very strong ground motion.

The modern seismic design code can achieve high lateral resistance. The excessive design in school buildings is adoptive and recommendable. When columns are designed with an adequate reinforcement, both in transverse and longitudinal directions, the addition of FRP is not as effective as adding concrete-jacketing to improve its lateral resistance. Another finding of Chinese seismic design code is the conservative criteria for slight damage and moderate damage in terms of ISD.

Moreover, prevention for the failure of corridors will be one of the most important considerations when conducting retrofitting measures. The enhancement of beams, which support corridors, is required and recommended. When retrofitting strategies are evaluated in two directions, the combination strategy shall also be analysed. The FRP system provides less influence on other measures when compared with concrete-jacketing. As a result, FRP and concrete-jacketing can both increase the capacity of life safety and avoid collapse for school buildings, which achieves the aim. Further, concrete-jacketing is more effective in preventing soft-storey mechanisms.

In addition, the application of FRP and concreting can significantly prevent school buildings from collapse, whilst also improving the life safety performance of the building. These two methods can be recommended in retrofitting schools in China.

Consequently, the FRP is considered an effective rehabilitation measure for horizontal components in the effort to increase flexural capacity. Meanwhile, concrete-jacketing is recommended to retrofit ductile columns, whilst FRP is not suggested.

For future works, there should be focus on a wide range of other retrofitting measures for school buildings, which should undergo assessment, such as bracing and shear walls. The addition of shear walls is widely used in school buildings, and buckling-resistance braces recognised as an innovative measure is gradually being adopted in China. Moreover, RC structures with infill walls should be examined, as this approach may give different responses of retrofitting structures.

Part III Social Science Analysis

Section IV Social Science Analysis Section IV

Social-Science Analysis

— Risk perceptions, perspectives and hazard adjustments

By Yanru Wang, and Carmine Galasso

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Social Vulnerability Assessment

This project focuses on establishing a seismic database with respect to engineering assessment and social science analysis. This hopes to improve the understanding of seismic risk, and overcome the barriers to increase resilience of schools to earthquakes in Sichuan province. This part will show the methodology and processes of the social science analysis. 11.1 Impact of the 2008 and 2013 earthquakes on students

As is mentioned in the former chapters, over the past 10 years, two large earthquakes which have occurred in Sichuan, China caused a great deal of physical harm to students, with 87,587 deaths and 374,643 injuries (Daniell, 2011). The Ms 8.0 Wenchuan earthquake is estimated to have caused 10,000 students deaths due to school building collapse. In addition, 7,000 classrooms and student hall rooms are officially reported to have collapsed during the earthquake (Wong, 2009). Five years later, the Ms 7.0 Ya’an earthquake led to 193 deaths and 15,554 injuries, of which 14 students died, and 42 students got injured (Li, 2013).

There are both physical harm, and negative aspects to psychological influences. On the one hand, many findings reported young groups, from children to college students, were likely to suffer from PTSD in the aftermath of the 2008 earthquake (Zhang et al., 2015; Wang et al., 2012; Zhang et al., 2011; Fu et al., 2013), some also found a common suicidal ideation among high school students (Ran et al., 2015). After the 2013 earthquake, out of the 3271 students from 21 primary and secondary schools in Baoxing County, 1166 of them were in the probable PTSD group, with the majority suffering from various somatic symptoms (Zhang et al., 2015). On the other hand, PTG which refers to developing a positive attitude and outlook in the aftermath, can also be found (Jin et al., 2014; Jia et al., 2017; Ying et al., 2016; Zhou et al., 2015). 11.2 Risk perception, hazard adjustments and disaster resilience

Risk perception explains how people form opinions and views on disastrous events (Slovic, 1987), it concerns their preconception and perception on risk. Risk taxonomy makes risk perception quantifiable and predictable. Risks can be put into model with three factors: dread risk (refers to be lack of control, dread, catastrophic potential, fatal consequences, and the inequitable distribution of risks and benefits), unknown risk (refers to be unobservable, unknown, new, and delayed in their manifestation of harm), and number of people exposed to the risk. Lay people’s risk perception, attitude and where does a hazard lies in the factor space show close correlation. More importantly, the higher the “dread risk”, the higher its perceived risk, the more people want to see the risks’ reduction, and the more want to see strict rules

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Chapter 11 Social Vulnerability Assessment deployed to reduce the risk (Slovic, 1987). Generally, risk perceptions can be measured in many ways: some researchers tested how respondents estimated the likelihood of an earthquake (Mulilis and Lippa, 1990), how they worried about the incoming earthquake and its consequences (Dooley et al., 1992; Showalter, 1993), and their perceived characteristics of hazard adjustments (Lindell and Whitney, 2000).

Hazard adjustments is mainly based on four parts: hazard mitigation, disaster preparedness, emergency response and disaster recovery. Compared to the substantial studies conducted concerning risk perception of an earthquake itself, people’s perceptions regarding hazard adjustments need lots of attentions (Lindell and Perry, 2000). Fishbein and Ajzen (1975) revealed that the given attributes (e.g., uncertainty, damage) of a certain objective (e.g., an earthquake) may not have any effect on people’s willingness to perform any behaviours towards it (e.g., earthquake preparedness). In addition, Weinstein and Nicolich (1993) also found that people’s risk behaviour and their adopting precautions change, when their status of risk perception changes. And people’s adoption of hazard adjustments will become increasingly correlated with the effectiveness of the adoption of the adjustments, so it is as important to know about people’s attitude towards hazard adjustments as understanding risk perception itself.

The goal of understanding risk perception and hazard adjustments is to increase disaster resilience. Public risk perceptions towards specific hazards are of great importance in the process of policy making (Frewer, 2004). The agreement of public perceptions concerning hazard adjustments attributes is the key in risk communication decision-making (Lindell et al. 2009), and facilitating effective risk management, thus establishing public confidence to address risks (Vincent and Covello, 2008). Risk perceptions and people’s attitude towards hazard adjustments are tools to test community’s resilience regarding hazards. 444 residents in Dhaka City finished a questionnaire regarding their seismic risk perceptions, and the results show that the majority of participants were ill-prepared for a major earthquake, hence earthquake awareness and preparedness became a priority (Paul and Bhuiyan, 2010). Similarly, a survey conducted in two districts in Istanbul indicates inadequate safe planning for residences, however with the help of data, public awareness and seismic risk preparations may improve (Eraybar et al., 2009). 11.3 Risk perceptions and demographic variables

There is considerable evidence showing that personal risk perception and hazard adjustments concerning hazards are closely related to demographic characteristics. Lindell and Hwang’s research (2008) revealed that people’s disaster preparedness to flood, hurricane and toxic chemical release show close correlation with gender, age, income and ethnicity. Gender is one factor that leads to different outcomes. For Mexican schoolchildren aged between 7-14, males are less afraid and more confident in their ability to overcome earthquakes than females (Santos-Reyes et al., 2016). Boholm (1998) found that the effect of gender varies worldwide. Chinese researchers reported that female schoolchildren perceived and experienced more risks and psychological stress than their male counterparts (Xu and Wang, 2012). The odds of female children suffering from anxiety, worries and depression were approximately twice as high as male children (Tolin and Foa, 2006), but it remains unclear whether it’s associated with biological differences (e.g., hormone; Yehuda, 1999) or psychosocial variables, or some interactions of the two (Furr et al., 2010). Chinese female adolescents are also more likely to

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10 Chapter 11 Social Vulnerability Assessment show anxiety-related symptoms than their male counterparts, the odds increase as their age increases (Fan et al., 2011), so are adolescents in the U.S. (Adams et al., 2014); in U.K., being female young adults are also found to be significantly associated with developing PTSD (Udwin et al., 2000). With all that being said, female students are very likely to feel anxious, worrying, and fearful for a perceived risk. Furthermore, women also reported more PTG than men: they are more active in coping with the aftermath of a traumatic event (Jin et al., 2014), they get greater level of supports from their friends (Jia et al., 2017), adopting more hazard adjustments to lessen their risk of being hurt by an incoming hazard, and to recover from the past damage. Hence, women are also more positive and active in preparing for their perceived risks.

Experience is another main factor in influencing people’s risk perception. It’s found that hazard experience show high level of correlations with risk perceptions, which can predict people’s adoption of hazard adjustments (Lindell and Hwang, 2008). The most recent earthquake with a Ms of 7.0 or larger before 2008 occurred in 1976, known as the 1976 Songpan-Pingwu earthquake, with a Ms of 7.0 (Wikipedia, 2017). Hence, the 2008 earthquake is the first large earthquake all participants in this study have ever experienced. In comparison to the public, survivors were found to be more threatened and fearful about earthquakes, hence were influenced more after an earthquake (Kung and Chen, 2012). They were also more concerned about the disastrous impact it might bring to the next generation (Guo and Li, 2016). Those who were involved in floods and landslides also experienced more fears, worries and threats, as they perceived higher risks than the public (Ho et al., 2008). Studies also suggested that past experience of disasters can motivate the individuals to take mitigation activities to reduce future risks (Siegrist and Gutscher, 2008; Siegel et al., 2003), which can be partially supported by Lindell and Perry’s research review from 23 similar studies (2000). Previous experience can predict one’s level of preparation (Sattler et al., 2000), people who had experienced loss or damage can be better prepared for future earthquakes (Yeşil et al., 2010), and it facilitates the public policy making regarding risk management and disaster mitigation (Kung and Chen, 2012).

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Chapter 12 Methodology for Section IV

Methodology for Section IV

12.1 Online questionnaire

With the aim of improving the understanding of seismic risk, and overcome the barriers to increase resilience of schools and communities, the following three objectives facilitate achieving the goals: Objective 1, to get a general view of the experience from undergraduate students in Sichuan province concerning the past two earthquakes (in 2008 and 2013 respectively), and their thoughts about university and community earthquake hazard adjustments (e.g., preparation and education); Objective 2, to identify deep personal perceptions and perspectives of the students regarding the two earthquakes; Objective 3, to analyse the effects of gender and earthquake experience on earthquake perceptions, perspectives and hazard adjustments.

To achieve Objective 1, a questionnaire consisting of 32 questions (Appendix G) is designed on https://www.sojump.com, which is a questionnaire designing and management website. The first version of the questionnaire is adapted from Lei’s questionnaire in 2014. After modifications have been made, the current questionnaire consists of three parts: perceptions, perspectives and hazard adjustments. Perceptions describes how people perceive risks during and after the earthquakes, and emotional factors are used to represent their feelings. Perspectives show how well or badly they think of the roles schools and communities play before, during and after the earthquakes. Hazard adjustments record what have they done after the earthquakes.

Specifically, Q6 and Q13 are used to divide the whole samples into the “survivors’ group”, and the “general group”. Those who have witnessed injuries and deaths, or were physically hurt or felt mentally hurt, are deemed as survivors. Those who have never experienced the terms mentioned above, are all considered to be in the general group. However, as Q6 and Q13 are both multiple-choice questions, students may have experienced at least one situation, so the total votes of each option may add up to be more than 206. People who chose “the above are all not applicable” could not choose other options. Likert scale is used in Q7&Q14, Q8&Q15, Q10&Q17, to quantify people’s attitudes towards an issue, and the statements in the survey did not appear consecutively, the measurement of attitudes could never mean anything more than the determination of an amount of one’s tendency at present, it could never define what the tendency really is (Likert, 1932). In options where there are “Not applicable”, no points would be distributed to the option. For Q19-Q28, different questions would pop out for different options they choose, for example, if one chooses “Yes” for “Are there any activities concerning earthquake reduction and mitigation being held within one year in your university or community?”, a related “Have you participated in the activities?” would appear, otherwise, they will skip this to the next question. Similarly, if one chooses “Not necessary” for “How do

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10 Chapter 12 Methodology for Section IV you think of hosting earthquake knowledge and mitigation propagation activities regularly?”, a mandatory “Why?” will pop out for more detailed reasons. The last two optional questions are designed for suggestions and provision of contact information.

The target group consists of undergraduate students, as well as university graduates in 2016, who were located in Sichuan province during both the 2008 Wenchuan earthquake and the 2013 Ya’an earthquakes. The online questionnaires could be accessed by scanning a QR code or using the website address directly. Ethical approval was given by UCL-IRDR Ethics Committee before the research began. The questionnaire was anonymous, and all participants would be informed that they could quit whenever they want without any permission. Only completed questionnaires can be recorded on the website and were considered valid, 206 online questionnaires have been collected eventually.

IBM® SPSS Statistics® Version 24 for Mac is the main tool to do the statistics in this study. First, Cronbach’s Alpha is an estimate of the correlation between two random samples, it’s found to be applicable to lots of items in past studies (Cronbach, 1951). To test the internal reliability of measurements, a reliability coefficient is used to test the precise measure of real differences in the measures (Kelley, 1942). Rules of the relationship between the value of alpha and the interpretations are provided (George and Mallery, 2003): >0.9 is Excellent, >0.8 is Good, >0.7 is Acceptable, >0.6 is Questionable, >0.5 is Unacceptable. Subsequently, the Shapiro-Wilk test was used to check the normality of each option, which resulted in a lack of normal distribution for any of the answers. Hence, non-parametric tests are chosen to test if there is significant difference between two groups of data. The two sets of groups are female group and male group, the survivors’ group and the general group, since the subjects are not paired, the Mann-Whitney test is used. Samples choosing “Not applicable” would be excluded during statistical analysis, since the two variables must be both ordinal or nominal. For those using the Likert scale, they are ordinal variables, but “Not applicable” is a nominal variable. Asymptotic significance (Asymp.Sig.) represents two-tailed results, which is commonly considered the p-value. When Asymp.Sig. is <.05, the null hypothesis should be rejected, denoting that there is a significant difference between the two groups of variables. In contrast, when Asymp.Sig. is not <.05, it means that the null hypothesis cannot be rejected, there is no sufficient evidence to conclude that there is a significant difference between the two groups. If the null hypothesis is true, there will be a 5% probability of getting a p-value lower than 5% alone, so the null in 5% of the experiment will be incorrectly rejected, which is acceptable. 12.2 In-depth interviews

Objective 2 focuses on detailed and personal perceptions and perspectives. Of those who left their contact information and volunteered to be interviewed, 5 females and 5 males were randomly chosen. Of the females, 3 of them were freshmen who went to different universities, and other 2 were juniors at the same university. The male participants ranged from freshman to senior, including a university graduate. They were informed of being recorded beforehand, and permission was granted. There were 12 basic questions in total, however interview questions (Appendix H) are flexible and more details would be explored if the interviewees were willing to say more. The average interviewing time was 22 minutes. All audio records have been converted into Word files formatted in Chinese. Python was used to calculate word frequency to help analyse the interviews.

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Results

13.1 General result of the online questionnaires

Table 13-1 Internal reliability analysis of the online questionnaire

Cronbach’s Scale Sub-scale Year Variable Name Item N Alpha Negative feelings/ 2008 Q8.1-Q8.10 0.831 10 behaviours 2013 Q15.1-Q15.10 0.940 10 Perceptions Positive feelings/ 2008 Q8.11-Q8.15 0.846 5 behaviours 2013 Q15.11-Q15.15 0.920 5 School/Community 2008 Q7.1-Q7.6 0.871 6 EQ preparation 2013 Q14.1-Q14.6 0.967 6 Perspectives School/Community 2008 Q10.1-Q10.6 0.951 6 EQ aftermath 2013 Q17.1-Q17.6 0.958 6 2008 Q9.1-Q9.4 0.762 4 Student hazard adjustments 2013 Q16.1-Q16.4 0.840 4

As shown in Table 13-1, the Cronbach’s Alpha of the scale “Student hazard adjustments” is acceptable. 5 of them are excellent and 4 of them are good, demonstrating that this online questionnaire is internally reliable and it can be used for further research.

13.1.1Results in the context of different genders

The results are shown in Table 13-2. If considering the average score alone, there are great differences between female and male choices. Q8.1-Q8.3, and Q15.1-Q15.3 show that females are on average more anxious, sad and fearful than males. Males’ results show they feel more angry, silent and isolated when compared to females, meanwhile, the same results show they feel more peaceful, safe, secure, and hopeful in comparison to females. However, when the p- value is taken into consideration, only Q8.3 demonstrates a significant difference between female and male choices, meaning that females do feel more fearful than males after the 2008 Wenchuan earthquake. The non-significance means that not most of the scores given by females are higher than those of males, some relatively high scores influence the average score greatly.

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Table 13-2 Genders and perceptions

2008 2013 Average Variable Variable Score Female Male Female Male Name Name

Q8.1(.650) 5.26 5.00 Q15.1(.822) 2.43 2.42

Q8.2(.076) 5.09 4.18 Q15.2(.188) 2.57 2.09

Q8.3(.031)* 5.91 4.73 Q15.3(.408) 2.52 2.25 Q8.4(.800) 1.26 1.37 Q15.4(.278) 0.87 1.32 Q8.5(.162) 0.74 1.26 Q15.5(.502) 0.83 1.15 Negative Q8.6(.116) 4.13 3.30 Q15.6(.526) 1.48 1.26 Q8.7(.319) 1.39 1.81 Q15.7(.304) 0.83 1.21 Q8.8(.924) 1.22 1.26 Q15.8(.080) 0.65 1.26 Q8.9(.727) 0.91 1.15 Q15.9(.313) 0.74 1.15 Q8.10(.764) 1.04 0.93 Q15.10(.239) 0.74 1.15 Q8.11(.422) 4.74 5.11 Q15.11(.120) 5.00 5.77 Q8.12(.307) 3.78 4.23 Q15.12(.077) 4.22 5.11 Positive Q8.13(.872) 4.87 4.95 Q15.13(.681) 4.48 4.67 Q8.14(.326) 4.96 4.45 Q15.14(.456) 4.35 4.73 Q8.15(.054) 5.57 4.62 Q15.15(.787) 4.61 4.73

(Asymp.Sig.), *Asymp.Sig. <0.05

Table 13-3 Results of Mann-Whitney test on student perceptions between 2008 and 2013

Asymp.Sig.(2-tailed) Female Male Q8.1&Q15.1 .000** .000** Q8.2&Q15.2 .000** .000** Q8.3&Q15.3 .000** .000** Q8.4&Q15.4 .093 .870 Q8.5&Q15.5 .593 .637 Q8.6&Q15.6 .000** .000** Q8.7&Q15.7 .012* .058 Q8.8&Q15.8 .005* 1.000 Q8.9&Q15.9 .206 .967 Q8.10&Q15.10 .071 .248 Q8.11&Q15.11 .481 .045* Q8.12&Q15.12 .247 .025* Asymp.Sig.(2-tailed) Female Male Q8.13&Q15.13 .241 .534 Q8.14&Q15.14 .076 .496 Q8.15&Q15.15 .005* .748

(Asymp.Sig.), *Asymp.Sig. <0.05, ** Asymp.Sig. <0.0005

Both females and males experience significant emotional change such as anxious, sad, fearful and helpless. As can be seen from Table 13-3, the average score of these emotions have significant drops between 2008 and 2013. In addition, female moods of complaining, silence,

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and optimism have a distinct change during this time. Whereas for males, the peaceful, safe and secure score increases are obvious.

Table 13-4 Genders and perspectives

2008 2013 Average Score Variable Variable Female Male Female Male Name Name Q7.1(.010)* -0.41 0.10 Q14.1(.081) 0.81 0.90 Q7.2(.089) -0.49 -0.13 Q14.2(.637) 0.77 0.85 School/Community Q7.3(.006)* 0.10 0.49 Q14.3(.050)* 0.68 0.93 EQ preparation Q7.4(.003)* 0.16 0.60 Q14.4(.018)* 0.67 0.98 Q7.5(.057) 0.04 0.34 Q14.5(.626) 0.77 0.86 Q7.6(.031)* 0.26 0.60 Q14.6(.338) 0.78 0.92 Q10.1(.636) 0.43 0.49 Q17.1(.157) 0.75 0.92 Q10.2(.638) 0.39 0.43 Q17.2(.269) 0.72 0.88 School/Community Q10.3(.472) 0.23 0.36 Q17.3(.163) 0.65 0.83 EQ aftermath Q10.4(.603) 0.73 0.61 Q17.4(.481) 0.90 0.96 Q10.5(.512) 0.57 0.59 Q17.5(.423) 0.78 0.85 Q10.6(.386) 0.58 0.64 Q17.6(.247) 0.78 0.90

(Asymp.Sig.), *Asymp.Sig. <0.05

As shown in Table 13-4, females are very dissatisfied about the pre-earthquake knowledge learning, whereas both females and males gave a lowest score for pre-earthquake drills. Although the other scores are all above 0, they are far from being pleased (1 point). In 2008, females and males have significantly contrasting views on pre-earthquake knowledge learning, quality of the buildings and facilities, as well as command and evacuation instructions. In 2013, their diverse opinions upon quality of the buildings and facilities are still significant.

Table 13-5 Genders and hazard adjustments

2008 2013 Votes Variable Variable (Percentage) Female Male Female Male Name Name Yes 110(95.65%) 86(94.51%) 106(92.17%) 85(93.41%) Q9.1(.704) Q16.1(.736) No 5(4.35%) 5(5.49%) 9(7.83%) 6(6.59%) Yes 109(94.78%) 83(91.21%) 105(91.3%) 85(93.41%) Q9.2(.313) Q16.2(.577) No 6(5.22%) 8(8.79%) 10(8.7%) 6(6.59%) Yes 107(93.04%) 83(91.21%) 104(90.43%) 78(85.71%) Q9.3(.626) Q16.3(.295) No 8(6.96%) 8(8.79%) 11(9.57%) 13(14.29%) Yes 100(86.96%) 75(82.42%) 99(86.09%) 70(76.92%) Q9.4(.367) Q16.4(.090) No 15(13.04%) 16(17.58%) 16(13.91%) 21(23.08%)

(Asymp.Sig.)

The results show that females are more active on average after the earthquake in paying attention to the news and knowledge, as well as communicating and sharing information with the others. With the exception that males paid more attention to risk mitigation and reduction

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10 Chapter 13 Results knowledge in 2013 than in 2008, the participation in all other activities dropped after the 2008 earthquake. However, the statistical results do not show significant differences between females and males.

Table 13-6 Results of Mann-Whitney test on student hazard adjustments between 2008 and 2013

Asymp.Sig.(2-tailed) Female Male Q9.1&Q16.1 .102 .705 Q9.2&Q16.2 .102 .480 Q9.3&Q16.3 .257 .197 Q9.4&Q16.4 .782 .275

The results show no significant differences between female and males’ hazard adjustments between 2008 and 2013.

13.1.2 Results in the context of different groups of students with different disaster experience

Table 13-7 Earthquake experience and perceptions

2008 2013 Average Variable Survivors’ General Survivors’ General Score Variable Name Name group group group group Q8.1(.000)** 7.14 4.01 Q15.1(.000)** 6.39 1.90 Q8.2(.001)* 6.32 3.92 Q15.2(.000)** 6.11 1.75 Q8.3(.000)** 7.53 4.14 Q15.3(.000)** 5.73 1.75 Q8.4(.023)* 2.51 0.95 Q15.4(.000)** 3.33 0.63 Q8.5(.254) 1.92 0.77 Q15.5(.000)** 3.62 0.48 Negative Q8.6(.004)* 5.78 3.06 Q15.6(.000)** 4.03 0.90 Q8.7(.012)* 3.81 1.13 Q15.7(.000)** 3.18 0.54 Q8.8(.005)* 1.83 0.81 Q15.8(.000)** 2.44 0.54 Q8.9(.010)* 1.60 0.63 Q15.9(.000)** 3.04 0.48 Q8.10(.119) 1.37 0.81 Q15.10(.000)** 2.79 0.51 Q8.11(.977) 4.45 4.91 Q15.11(.995) 6.14 5.33 Q8.12(.138) 3.62 4.28 Q15.12(.075) 6.24 4.40 Positive Q8.13(.160) 5.28 4.59 Q15.13(.001)* 6.57 4.16 Q8.14(.510) 5.16 4.59 Q15.14(.001)* 6.93 4.16 Q8.15(.397) 5.91 4.95 Q15.15(.002)* 6.30 4.28

(Asymp.Sig.), *Asymp.Sig. <0.05, ** Asymp.Sig. <0.0005

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Generally, the responses of the survivors’ group and the general group are significantly different in many options. It’s obvious in the negative perception variables, survivors are more anxious, sad, fearful, inferior, helpless, complaining, silent and isolated after the 2008 earthquake. After the 2013 Ya’an earthquake, the survivors’ group show more intense feelings of anxious, sad, fearful, inferior, angry, helpless, complaining, silent, isolated, world and school -weary, than the general group. There are almost no significant differences between the two groups in feeling peaceful, safe and secure, both after the 2008 and 2013 earthquakes. Surprisingly, survivors feel distinctly more hopeful, motivated and optimistic in 2013 than the general group, meanwhile, all positive perceptions become more intense after the Ya’an earthquake than after the Wenchuan earthquake.

Table 13-8 Earthquake experience and perspectives

2008 2013 Average Score Variable Survivors’ General Variable Survivor General Name group group Name s’ group group Q7.1(.184) -0.52 -0.08 Q14.1(.704) 0.85 0.83 Q7.2(.006)* -0.73 -0.09 Q14.2(.687) 0.79 0.79 School/Community Q7.3(.294) -0.17 0.35 Q14.3(.957) 0.69 0.79 EQ preparation Q7.4(.635) -0.09 0.38 Q14.4(.498) 1.00 0.78 Q7.5(.179) -0.15 0.28 Q14.5(.989) 0.69 0.81 Q7.6(.422) 0.55 0.36 Q14.6(.945) 0.71 0.84 Q10.1(.421) 0.32 0.41 Q17.1(.135) 0.97 0.78 Q10.2(.843) 0.17 0.42 Q17.2(.080) 1.01 0.73 School/Community Q10.3(.921) 0.24 0.28 Q17.3(.163) 0.97 0.69 EQ aftermath Q10.4(.725) 0.48 0.67 Q17.4(.076) 1.10 0.88 Q10.5(.374) 0.58 0.53 Q17.5(.176) 1.08 0.77 Q10.6(.187) 0.57 0.53 Q17.6(.121) 1.05 0.79

(Asymp.Sig.), *Asymp.Sig. <0.05

The average scores given by the survivors group are relatively low compared to those given by the general group, especially for the 2008 earthquake, both two groups are not pleased about the pre-earthquake knowledge learning and drills. Specifically, survivors are also dissatisfied about the quality of the buildings and the facilities, planning and environment, as well as command and environment. However, only a significant difference can be seen from their attitudes towards pre-earthquake drills between the survivors’ group and the general group after the Wenchuan earthquake.

Table 13-9 Earthquake experience and hazard adjustments

2008 2013 Votes Variable Survivors’ General Variable Survivors’ General (Percentage) Name group group Name group group Yes 96.08% 93.69% 98.39% 92.17% Q9.1(.296) Q16.1(.537) No 3.92% 6.31% 1.62% 7.83% Yes 93.05% 92.79% 89.56% 92.17% Q9.2(.800) Q16.2(.944) No 6.95% 7.21% 10.44% 7.83% Yes Q9.3(.473) 93.32% 90.99% Q16.3(.145) 98.39% 86.75%

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No 6.69% 9.01% 1.62% 13.25% Yes 94.38% 81.98% 95.76% 79.52% Q9.4(.199) Q16.4(.055) No 5.63% 18.02% 4.24% 20.48%

(Asymp.Sig.)

There is a decline in the participation of students in the general group, for both the aftermath knowledge-learning and information sharing, from 2008 to 2013. While those in the survivors’ group, except that regarding Q16.2 show reduced participation paying attention to risk mitigation and reduction knowledge when compared to the general group. The attention paid to Q16.1, Q16.3 and Q16.4 have all increased when compared to their counterparts in 2008. However, no significant differences between the two groups are found.

Table 13-10 Results of the online questionnaire from Q19 to Q30

Votes .1 .2 .3 .4 .5 .6 (percentage) Q19 128(62.14%) 39(18.93%) 39(18.93) N/A N/A N/A Q20 76(59.38%) 30(23.44%) 14(10.94%) 8(6.25%) N/A N/A Q21 134(65.05%) 68(33.01%) 0(0%) 3(1.46%) 1(0.49%) N/A Q22 181(89.6%) 87(43.07%) 123(60.89%) 43(21.29%) 33(16.34%) 1(0.5%) Q24 93(45.15%) 79(38.35%) 34(16.5%) N/A N/A N/A Q25 64(68.82%) 23(24.73%) 4(4.3%) 2(2.15%) N/A N/A Q26 120(58.25%) 80(38.83%) 1(0.49%) 3(1.46%) 2(0.97%) N/A Q27 176(88%) 77(38.5%) 117(58.5%) 42(21%) 33(16.5%) 1(0.5%) Q29 55(26.7%) 98(47.57%) 46(22.33%) 7(3.4%) N/A N/A Q30 153(74.27%) 151(73.3%) 172(83.5%) 136(66.02%) 65(31.55%) 6(2.91%)

The universities participants attend are all based in Sichuan province. Hence, they all live in the earthquake prone areas yet. Q19 and Q24 show that neither drills nor related knowledge- learning is sufficient. For those who have participated in these activities, their satisfaction levels are all below 70%(Q20 and Q25). Generally, people consider drills and knowledge learning a waste of time, and are often superficial since they do not think they are not in danger. Meanwhile, schools and governments are trusted to host seismic education activities and drills, other bodies only obtain votes from less than half of the participants (Q22 and Q27). Though all participants were in Sichuan province during the past two earthquakes till present, 7 of them said they never learned anything about earthquake risk reduction and mitigation (Q29). Q30 shows that students care most about what to do before and during an earthquake. One student even pointed out the importance of knowledge about living in post-seismic event, which was neglected during the questionnaire design.

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13.2 General results of the in-depth interviews

Python was used to calculate the word frequency of the interviews. Words like “we”, “our”, “ kind of” in Chinese have been deleted, nouns like “Sichuan”, “Wenchuan” and “Ya’an” have also been deleted from the word frequency list. After the elimination, Chinese words (word that consists of at least two Chinese characters), which were mentioned more than 10 times are as follows with its frequency in the bracket behind it: earthquake (109), school (79), teacher (58), drill (40), classmates (37), education (24), having classes (18), knowledge (17), playground (15), teaching building (15), to organise (14), severity (13), parents (11), impact (11), news (10), careful (10). On average, the above words were mentioned at least once in each of the 10 interviews and it shows to which did the interviewees pay their attention.

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11 Chapter 14 Discussion

Discussion on Perceptions ,

Perspectives and Hazard Adjustment

14.1 In the context of different genders

14.1.1 Student perceptions

Kung and Chen (2012) point out that females are impacted more than males with regards to risk perceptions. Conclusions can be drawn from Table 2 and Table 3, and the results correspond well to their study. Females feel more intense negative feelings than males on average, and their negative feelings changed more significantly from 2008 to 2013 with respect to males. Five reasons are found to explain this: firstly, females are more susceptible due to long-term dysregulation of the hypothalamic-pituitary-adrenal axis, it is produced by early stressors, it can be an experience of being hurt or badly influenced, which puts them at a higher risk of being affected afterwards, leading to vulnerability (Weiss et al., 1999), and their feelings noticeably changed after the 2013 earthquake; secondly, the theory of “risk as feelings” (Slovic and Weber, 2002; Slovic et al., 2004, Loewenstein et al., 2001) suggests that the feelings that become salient in a judgement depend on characteristics of the individual, hence, females could be more susceptible to take on risk personally; thirdly, men are concerned about being judged if seen to be powerless, hence they suppress their feelings of being weak (Timmers et al., 1998), in this context, they suppress their negative feelings that may demonstrate weakness; fourthly, our society has greater acceptance for women to express emotions, particularly stress-related emotions than for men (Anderson and Manuel, 1994); finally, women are found to be more expressive of feelings of depression and anxiety as well (Aneshensel and Pearlin, 1987). Therefore, females show significantly more fears than males after the 2008 earthquake in Table 13-2, and their feelings are more easily affected by disastrous events (Table 13-3). Therefore, on average males perceive fewer risks than their female counterparts (Flynn et al., 1994), which makes them seem less negatively affected by earthquakes.

It’s also found that when someone wants to show their controls over something, or they can change the situation, they are very likely to experience anger or contempt, which means males are more likely to show their anger than females (Timmers et al., 1998). Though the anger level of males is slightly higher than females both after the 2008 and 2013 earthquakes (Table 13-2), the results in this study show no significant differences between them, because not many demographic and personal variables are included in this study. Thus, gender difference alone cannot lead to significant differences (Kung and Chen, 2012).

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On average, males are more silent and isolated than females in the study. One reason may be that they would not like to express themselves, and this can be supported by a study indicating that compared to women, men have less willingness to express their emotions openly (Timmers et al., 1998). Moreover, males demonstrate the ability to keep something under control by expressing moods of peace, safety and hope, which are emotions that display power (Timmers et al., 1998). As displayed in Table 13-3, these emotions changed significantly between the two events, so the earthquakes do have an impact on this. However, no distinct gender differences are found.

Females are scored to be more motivated and optimistic. This can be explained by previous research, saying that although women and men are all motivated by the same thing, the increased level in women’s motivation would put them in an advantageous level even which already exceeded that of males from the very beginning (Ciarrochi et al., 2005). Although women are surely more responsive, and they act significantly different after the two events (Table 13-3), the differences between the two genders are not that obvious in this study.

14.1.2 Student perspectives

Only three scores below 0 are all in the context of the 2008 earthquake, which shows that females are not pleased about the pre-earthquake learning, and both females and males are not happy about the pre-earthquake drills. Eight out of ten interviewees said that they had neither been in a drill nor been taught about earthquake evacuation or self-protection before the 2008 Wenchuan earthquake. In fact, most of them did not know what an earthquake was in 2008. Research also shows that in Beichuan, which was one of the most seriously stricken areas during the 2008 Wenchuan earthquake, most schools had no disaster plan and practiced for students and teachers alike (Lei, 2014). A study reveals that education on the relationship between preparedness and disaster risk reduction can help students adopt preparatory measures, and educated students may have better disaster intuition (Hoffmann and Muttarak, 2017). This is particularly important for earthquakes, as public education plays a vital role in reducing earthquake’s impact (Kung and Chen, 2012).

It’s worth noticing that females seem to be more difficult to be pleased when compared to males regarding school and community earthquake preparation. Females are especially dissatisfied with the quality of both buildings and facilities. It’s reasonable to believe that both males and females are dissatisfied, however, it is more obvious with females because they display more emotional awareness than males (Barrett et al., 2000). However, lower level of satisfaction can also be a result of the greater stresses to women in response to earthquakes (Anderson and Manuel, 1994).

Although the average scores increased after the 2008 earthquake, all options in the 2013 earthquake are still less than 1 point; it is a long way from being “pleased”.

14.1.3 Student hazard adjustments

The results show no significant differences between different genders, this may be that schools have large impact on student hazard adjustments in the aftermath of earthquakes. Since students spend most of their time in schools, so with the companionship of their peers, they are

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11 Chapter 14 Discussion influenced unintentionally, and they are also asked to take actions in schools as instructed by their teachers.

Connection between student perceptions and hazard adjustments can be made through a research revealing that though fear and anger are both negative emotions, they are both related to risk perception in different ways. Fearful people (females) are found to conceive negative perceptions of future events, while angry people (males) think more positively about the future (Lerner and Keltner, 2000). This can also be reflected on their actions, since fearful people (females) tend to choose risk-averse actions, so they pay attention to risk reduction and mitigation knowledge to improve their risk resilience, as results shown in Table 13-5. In addition, they also avoid high-level risk. Meanwhile, angry people (males) favour riskier choices (Lerner and Keltner, 2001), this does not explain the outcome of this research, since people are unlikely to seek for risk themselves during an earthquake.

The behaviours of “talked about the news”, and “propagated knowledge” seem to be more common in females than in males on average. In the theory of planned behaviour, namely, hazard adjustments in this study, the central factor is intention, which is found to be one of the personal dispositions to show willingness to take actions to reduce risk (Sinatra et al., 2011), it’s a superior predictor of behaviour (Armitage and Conner, 2001). The results in 14.1.1 also shows that females were more responsive (Weiss et al., 1999), so they would not only take effective measures to enhance their own resilience. They also would like to share and communicate, which also reflects more desire to express themselves more intensely than males (Grossman and Wood, 1993). Moreover, as can be concluded from 14.1.1, females are more likely, and better at expressing their emotions, emotion-focused coping strategies is closely related to the key mechanism in the PTG (Jin et al., 2014), so females’ hazard adjustments like communicating with the others after the earthquakes, will facilitate their recoveries. 14.2 In the context of people with and without earthquake experience

14.2.1 Student perceptions

There are significant differences between the two groups both after the 2008 Wenchuan earthquake and the 2013 Ya’an earthquake (Table 13-7). The average scores of the survivors’ group were much higher than that of the general group. Considering the results in 14.1.1, we can infer that female survivors could feel more intense feelings of anxiety, sadness and fear than the male survivors’ group, as supported by many similar studies (Udwin et al., 2000; Ho et al., 2008). Specifically, it can be found in Kung and Chen’s (2012) study that survivors had greater impact than the general public in terms of risk perceptions. In accordance to the risk- as-feelings hypothesis (Slovic and Weber, 2002; Slovic et al., 2004, Loewenstein et al., 2001), people’s emotional reactions to risk depends on many factors that may affect cognitive evaluation of risk, including vividness that can facilitate them imagining the disaster’s consequences, direct exposure to or experience, as well as memories of making the situation better (Loewenstein et al., 2001). Compared to the general group, survivors possess more memories about them experiencing the earthquakes, from being physically hurt themselves,

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Chapter 14 Discussion witnessing deaths of their relatives or friends or their witnessing some people they did not know being injured. These exposures to the earthquakes can be more graphic in their minds, so these could have a larger impact on survivors’ group emotional changes, the huge gaps between the average scores of the two groups in many negative emotions are supported by the explanation.

However, some studies also indicate that even people who recognise the consequence of an event as highly unlikely or not objectively terrible, such as watching about earthquakes on televisions or reading newspapers articles, it’s common that they experience extreme fears. While people show less fears when experiencing hazards that are more severe, and more likely (Loewenstein et al., 2001). This contradicts to the results about earthquake experience for survivors shown in Table 13-7.

The score of being angry in 2013 increased to nearly twice of that in 2008 of the survivors’ group, it may be related to the sense that governments did not try to avoid the same mistakes of the 2008 disaster (Timmers et al., 1998). Compared to people in the general group, the survivors’ lack of sole control over the outcomes of the earthquakes makes them feel powerless and helpless (Fiske et al., 1996), because the outcomes are not contingent on their own efforts. Interestingly, the survivors’ group votes for “peaceful”, “safe and secure” far exceeded those in the general group. For survivors’ themselves, they were used to risk invading their thinking (Tversky and Kahneman, 1974), so when people did not experience serious damage or loss from a disaster anymore, a “normalisation bias” occurs. Survivors would underestimate the risk of a hazard, unintentionally being forced to be positive, even optimistic (Mileti and O’Brien, 1992). Another study shows that only few people thought a damaging earthquake would happen in a short-term future, many did believe it as happening in a long-term (Mileti and Darlington, 1995). Therefore, normalisation bias may reduce future preparedness to some degree (Johnston et al., 1999).

Disasters like earthquakes are full of uncertainties, and can trigger public curiosity to some extent. Before the development of the Internet, people mainly accessed information from televisions, radios, newspapers and other people. These tools can guide the public in either a negative or a positive way.

While non-personal media such as newspapers and televisions mainly broadcast publicity, the personal word-of-mouth information is disseminated with much more confidentiality via mouths (Hoye and Lievens, 2005). One of the female interviewees mentioned she heard that the reconstruction funds allocated by the national government were illegally possessed by many officials, which led to many buildings being built poorly. This information was an open secret in their neighbourhood, which made the residents very angry but helpless. A study demonstrated that the word-of-mouth information possesses vividness that is more accessible than the information disseminated by news media (Collins and Stevens, 2002). Moreover, the nature of public media means that it cannot be as casual as word-of-mouth information, and the uncertainties caused by earthquakes are catalysts for the dissemination of word-of-mouth

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11 Chapter 14 Discussion among people (Zhu et al., 2011). People see information as credible and instructive if it’s consistent with their initial beliefs, and tend to see inconsistent information as unreliable and incorrect (Nisbett and Ross, 1980). When people have no strong prior views, and are exposed to contradictory opinions, people decide which to believe themselves. In the first place, when sufficient information was unavailable, the survivors’ group was very likely to guess and cause public panic, which may encourage negativity bias and make survivors focus more on the negative side rather than the positive side (Cacioppo and Berntson, 1994; Rozin and Poyzman, 2001). However, people in the general group usually learned about the situation gradually by mass media, so they might hear much less negative “rumours” than people did in the survivors’ group. Hence, people in the general group would be less influenced by negative information.

Moreover, when mass media is available, it can also affect people positively. One female interviewee in the survivors’ group mentioned that after the 2008 Wenchuan earthquake, they compared their own losses to the others’ and felt much better. The main source of that knowledge was media, especially since the word “news” was mentioned ten times in the interviews. “When I saw the soldiers arriving at the stricken areas, I felt so safe, united and touched.”, said one female interviewee of the general group. Therefore, mass media coverage might also comfort the public and give them confidence that solidarity will overcome the disaster. Before 25 May 2008, mass media was most concerned in who was responsible for the large-scale of school buildings’ collapses, which subsequently shifted to the rescue and recovery progress. As Wenchuan earthquake had a nationwide focus, this shift would move public attention from the damages onto the government’s role, to rescue, this dominated public feelings and perceptions (Yardley and Hooker, 2008). Therefore, it’s not surprising that the scores of positive perceptions of both the survivors’ group and the general group reached high levels after the earthquakes, denoting that the mass media delivers positivity to the public.

However, the high rate of being “optimistic” is considered the highest bias for rare events, such as earthquakes (Eiser et al., 2001). This is often difficult to change (Weinstein and Klein, 1995), and is also found in a study demonstrating that people often prioritise high-frequency-but- minor-impact events over low-frequency-but-large-impact events like earthquakes when they prepare for disasters (Vassie et al., 2005), which results in a lack of preparedness. For those who chose “not necessary” and “not necessary at all” in Q21 and Q26, they felt it was safe, because earthquakes occur infrequently, and their locations were far from nearby epicentres. A similar study reports that students felt that earthquake was less likely to happen when they were asked 3 months after the Loma Prieta earthquake than they were asked one week after the earthquake (Burger and Palmer, 1992), these all show unrealistic optimism (Shepperd et al., 2013).

14.2.2 Student perspectives

Q7.1-Q7.5 (Table 13-8) revealed the survivors felt very unhappy about the pre-earthquake preparation and post-earthquake responses of their schools and communities. It’s also understandable that the great improvement to the surroundings after the earthquakes can increase the survivors’ happiness and satisfaction greatly. For the general group, they may not

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Chapter 14 Discussion need that much improvement, so they marked Q10.5 and Q10.6, Q17.5 and Q17.6 a bit lower. This could also be applicable to other options when the survivors’ group gave a higher mark than the general group where they could see greater improvement from the previous poor condition.

In addition, the role of public education is discussed in many articles, since education can reduce the probability to be affected by a disaster by improving student preparedness (Hoffmann and Muttarak, 2017; Kung and Chen, 2012). It’s also proved to protect students against depression in the survivors’ group (Paykel, 1994; Xu et al., 2013). Though student satisfaction increased greatly from 2008 to 2013, no options of the general group gained scored over 1. This reveals that the general group felt a lack of social support from schools and communities.

Furthermore, the results from Q19-Q28 concludes that (Table 13-10) that students trust schools and governments very much and would like the two bodies to take responsibility in educating them and helping them. Research conducted in Beichuan revealed that teachers in schools had realised the importance of disaster education and school-based disaster management plans after the Wenchuan earthquake, however, there needs to be a great deal of improvement (Lei, 2014).

14.2.3 Student hazard adjustments

Table 13-7 shows that the survivors feel significantly helpless compared to the general group. They apparently have less control over the outcomes of a disastrous earthquake than the general public and feel powerless. As a result, they try to increase their control by paying attention to the news, as well as risk mitigation and reduction knowledge (Q9.1 and Q9.2 in Table 13-9), so they could have some more controls (Fiske et al., 1996) over the outcomes of an earthquake (e.g., they know about knowledge regarding self-protection when earthquake happens), making them feel more powerful.

As previously mentioned in 14.2.1, instant word-of-mouth information dissemination after an earthquake might bring negative effects to the public. When there is scarce public information, discussion in public places in the aftermath such as “sharing” and “communicating” can also be a good thing. After an earthquake, survivors in the stricken areas can easily get involved in the aftermath activities and compared to the general public, they concentrate more on the earthquake itself and develop social risk strategies (Kung and Chen, 2012). Hence, survivors tend to find someone to talk and to disseminate information, to form their own perspectives by gathering different views from people with similar experience. This could also help to improve the resilience of the whole community (Vicente et al., 2014). 14.3 Strengths and weakness

The strengths of this research are mainly in the following three aspects: firstly, the participants were all in Sichuan province when the 2008 and 2013 earthquake happened, and have direct or indirect experience of the largest two earthquakes within the past 40 years, hence they have more genuine understandings of earthquakes than other people, which provides me with many unique perspectives; secondly, the participants are all undergraduate students or graduated students, they can understand the questions and express their feelings in a good way, which

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11 Chapter 14 Discussion increases the depth and credibility of the study; thirdly, “perceptions ”, “perspectives” and “hazard adjustments” are different from each other, but closely related, the three parts unfold an image of how people were influenced by the earthquakes and how they responded to the earthquakes with the assistance of the in-depth interviews, the “current situation” Section IIs mixed with the analysis of the mentioned three parts, demonstrating student’s thoughts about the status quo and expectations for the future. The whole study is logically designed with a clear composition.

Meanwhile, there are some inherent weaknesses, too: firstly, the sampling is not strictly random, as it was initially distributed to some friends in Sichuan, which were then distributed to their friends or classmates, females were 24 more than males, and there is a 11% gap between them, which is huge; secondly, the questionnaire is quite long, hence people can easily lose patience and it’s impossible to make sure that they have completed carefully, which could reflect their true thoughts; thirdly, only two variables, gender and earthquake experience are tested in this study, some other main factors like proximity to the epicentre, and family background may have larger effect on the individuals; fourthly, this project is a simplification of previous research, many of the questions are explored horizontally instead of vertically, so it’s not deep and thorough enough; finally, the strict regulation, policy and security reasons in China, and the overseas background placed many obstacles during the study, which means that no official cooperation and connections have been established with local schools, so all participants were volunteers, this increased the uncertainties of the target group, who were not in Sichuan in 2008 and 2013 may also take part in the study online due to curiosity or other reasons. 14.4 Suggestions for improvement

A larger sample size is favourable in this topic, and the ratio of different variables is better to be close to the population’s ratio in the research area. Establishing official relationships with local schools and communities, can help eliminate the noise of unsuitable participants and improve the credibility of the study. One of the goals of this study is to explore how people from different cities and regions think and respond to hazards, but the sample size divided by each city and region (except for Chengdu) is too small to be reliable. City and region restrictions are necessary for further research, and the questions under each category would be further extended.

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Chapter 15 Conclusions for Section IV

Conclusions for Section IV

This study shows that both earthquake experience and gender are main factors in influencing student perceptions, perspectives and hazard adjustments regarding earthquakes. In accordance to previous studies, the following results have been found: firstly, earthquake experience demonstrates a larger impact than gender on perceptions, female survivors are most easily affected by negative feelings like anxiety, fear and helplessness; secondly, gender shows a larger impact than earthquake experience on perspectives, where female survivors have lower satisfaction for schools and community’s preparation and responses; thirdly, neither gender nor earthquake experience can cause significant differences in student hazard adjustments with regard to earthquakes. These information are of rather importance which can be used as reference in developing materials for disaster prevention and reduction education.

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Chapter 16 Summary and Impact

Summary and Impact

16.1 Summary and Conclusions

The Comprehensive School Safety Framework (2017) has consisted of three pillars: ‘Safe Learning Facilities’, ‘School Disaster Management’, and ‘Risk Reduction and Resilience Education’. With the aim of investigating how the three pillars underpinned the process of school infrastructure recovery post-2008 Wenchuan earthquake in China, which caused more than 5,000 school children deaths, CROSSH, has been carried out with a case study in Beichuan Qiang Autonomous County, Sichuan. This report has presented 1) a methodology for physical vulnerability assessment and its application to school buildings in the Sichuan Province; 2) a preliminary investigation on the enhancement of seismic resilience of schools through retrofitting of school buildings and disaster risk reduction education; 3) a framework for social vulnerability assessment to increase resilience of schools and communities to earthquakes through investigating perspectives and hazard adjustments of students regarding earthquakes.

Physical vulnerability assessment of school buildings has been carried out through a) systematic collection of data on school infrastructure location and characteristics across the County, considering both old (pre-Wenchuan) and new (post-Wenchuan) school sites, for a total of 102 school buildings; b) development of a comprehensive database of typical and systematically defined structural typologies, including main structural and non-structural characteristics (age of construction, number of story, lateral load resisting system and materials, number of occupants, etc.); c) development of fragility functions for the most common structural typology, with different damage states, combining nonlinear static analysis (detailed 3D nonlinear model) and nonlinear time history analysis (on simplified, equivalent SDoF systems to each 3D frame) using SIMBAD ground motion records from world wide earthquake events. Based on results from interviews with experienced local engineers, professors and industry professionals, carbon fiber reinforced polymer (CFRP) and concrete jacketing have been finally selected as possible retrofitting strategies to improve the structural performance. The resulting reduction of school fragility has been assessed. These tasks have been carried out through a desk-study combined with targeted 2-week field surveys in Beichuan County. Perceptions, perspectives and hazard adjustments of students regarding earthquakes have been assessed through results from online questionnaires and in-depth interview. 206 university students who have gone through both the 2008 Wenchuan earthquake and the 2013 Ya’an earthquake, have participated in online questionnaires and 10 participants have been selected to be interviewed.

The results of the illustrative applications show that most of the school buildings in Beichuan

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Chapter 16 Summary and Impact

County are designed and built according to the most recent seismic design code (post-2008), and they are characterized by good overall seismic performance (across a range of different ground shaking intensities). With the aim of maintaining the functionality of school buildings and ensuring the education continuity, a preliminary strategy of structural retrofitting has been investigated. Research on “perceptions, perspectives and hazard adjustments of students regarding earthquakes” reveals that: earthquake experience has a larger impact than gender reg arding perceptions; gender factor is advantageous in dominating people’s perspectives; neither gender factor nor earthquake experience alone can lead to significant differences in student hazard adjustments with regard to earthquakes.

16.2 Impact

In this project a robust and original methodological framework has been developed for seismic risk assessment of school buildings in China, with emphasises on the derivation of both physical and social vulnerability assessment. The project has investigated the fragility functions for the most representative school building in Beichuan County, the effectiveness of building retrofit measures, and social preparedness measures as means of preventing casualties, reducing economic losses and maintaining functionality of the school infrastructure and its role within the community in the event of natural disasters.

As a particularly vulnerable sector of society, the safety and protection of school children have always been emphasised by organisations and governments. Risk management of schools in earthquake-prone areas has gained increasing attention worldwide. Governments, private sectors, as well as academic institutions have collaborated to address the issue of school safety and resilience (GFDRR, 2016). The overall project framework and results from the study not only underpinned the three pillars in the Comprehensive School Safety Framework (2017), but also can be effectively used by Civil Protection and School Authorities in Sichuan to inform their preparedness actions planning and implementation to improve school resilience towards future earthquakes.

The impact of this research is remarkable. By applying the whole framework to the community, it can help to form safer schools, which is essential to minimize the disruption of education activities and provide space for children’s learning and healthy development. Besides, safer schools can be severed as community centers to coordinate response and recovery efforts in the aftermath of a disaster. It can also sever as emergency shelters to protect not just the school population but the community. Moreover, the application of this framework will engage the broader community in the integration of new knowledge and the acquisition of disaster prevention skills with physical vulnerability assessment of school buildings and structural seismic retrofitting. Besides, the whole framework can have an impact that reaches beyond the school infrastructure and serves as a model for the improvement of seismic resilience of residential buildings, community health centers and other public and private buildings.

Apart from applying the framework to local communities in the Sichuan Province, the whole framework can also be applied to other seismic-prone areas in China and other countries. The knowledge built has great potential to help local and international understanding of the research topics. This could form a foundation for further international collaboration.

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16.3 Recommendation for future work

Future works for each section have been addressed separately in aforementioned chapters. With the aim of further developing this framework, recommendations for future work are given as follow:

• This framework has only been applied to Beichuan County. It has great potential to help improve school resilience in other seismic prone areas in China and other developing countries, especially Nepal and Bangladesh. Adjustments should be made based on different construction environments and authority policies. By applying this framework in China, Nepal and Bangladesh, it can help to build dynamic research collaboration between those countries;

• Vulnerability models in regarding of seismic loss, causality and downtime should be developed in the next step, as well as including the behaviour and influence of nun- structural components. The accuracy of these models can be validated by comparing the loss data from past earthquakes, for instance, 2008 the Wenchuan Earthquake and 2013 the Lushan Earthquake;

• The impact of earthquake cascading hazards on school buildings are not assessed in detail yet due to time limitation. It could be analysed in the next step.

List of final documents:

1st Conference paper 11 NCEE: 11th National Conference on Earthquake Engineering, Los Angeles, California, USA 2nd Conference paper 16 ECEE: 16th European Conference on Earthquake Engineering, Thessaloniki, Greece 3rd Conference paper 4 ICCE: 4th International Conference on Continental Earthquakes, Chengdu, China Title CROSSH - China Resilience of Schools to Seismic Hazard: a case study in Beichuan Qiang Autonomous County, Sichuan Authors Linghui Zhou, Carmine Galasso, Shuang Yan, Zeyue Xue, Dina D’Ayala Status The paper has been accepted for the three conferences mentioned above and will be presented orally.

1st Journal Paper CROSSH - China Resilience of Schools to Seismic Hazard: a case study in Beichuan Qiang Autonomous County, Sichuan Authors Linghui Zhou, Carmine Galasso, Shuang Yan, Zeyue Xue, Dina D’Ayala Target Journal International Journal of Disaster Risk Reduction Status Under review

2nd Journal Paper Development of Fragility Functions and retrofitting strategies for school buildings in Sichuan Authors Linghui Zhou, Carmine Galasso, Shuang Yan, Zeyue Xue, Dina D’Ayala Target Journal Earthquake Spectra Status In preparation (expected submission in May 2018)

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Weinstein, N. and Klein, W. (1995). Resistance of personal risk perceptions to debiasing interventions. Health Psychology, 14(2), pp.132-140. Weinstein, N. and Nicolich, M. (1993). Correct and incorrect interpretations of correlations between risk perceptions and risk behaviors. Health Psychology, 12(3), pp.235-245. Weiss, E., Longhurst, J. and Mazure, C. (1999). Childhood Sexual Abuse as a Risk Factor for Depression in Women: Psychosocial and Neurobiological Correlates. American Journal of Psychiatry, 156(6), pp.816-828. Wenchuan earthquake memorial museum. (2017). [Exhibition]. Yingxiu Town, Sichuan, China. December 2010 – Current. Wen YK, Ellingwood BR and Bracci J (2004) Vulnerability Function Framework for Consequence- Based Engineering, Mid-America Earthquake Center, CD Release 04-04. Wu, Q., 2012. Seismic perfromance assessment on some frame structures. Juornal of vibration and shock, 31(N15), pp. 158-164. Wong, A. (2009). China Releases Official Toll of Students in Sichuan Quake. [online] Nytimes.com. Available at: http://www.nytimes.com/2009/05/08/world/asia/08china.html [Accessed 13 Aug. 2017]. Xu, J., Mo, L. and Wu, Z. (2013). A Cross-Sectional Study on Risk Factors of Depression Severity Among Survivors of the 2008 Sichuan Earthquake. Community Mental Health Journal, 49(6), pp.847-856. Xu, J. and Wang, P. (2012). Social Support and Level of Survivors' Psychological Stress After the Wenchuan Earthquake. Social Behavior and Personality: an international journal, 40(10), pp.1625-1631. Yehuda, R. (1999). Biological Factors Associated with Susceptibility to Posttraumatic Stress Disorder. The Canadian Journal of Psychiatry, 44(1), pp.34-39. Ye, L., Lu, X., Zhao, S. & Li, Y., 2009. Seismic collapse resistance of RC frame structures. Journal of Building Structures, 30(1000-6869 (2009) 06-0067-10), pp. 67-76. Ying, L., Wang, Y., Lin, C. and Chen, C. (2016). Trait resilience moderated the relationships between PTG and adolescent academic burnout in a post-disaster context. Personality and Individual Differences, 90, pp.108-112. Youssef, M., Ghaffarzadeh, H. & Nehdi, M., 2007. Seismic performance of RC frames with concentric internal steel bracing. Engineering Structures, Volume 29, pp. 1561-1568. Yun, B., Zhang, X., Cao, J. & Chen, J., 2010. Seismic strengthening design and construction in primary and secondary schools buildings after earthquake. Buidling Structures, Volume 40, pp. 153- 155. Zhao, B., Taucher, F., & Lu, X. (2010). Lesson learned from Wenchuan earthquake of 12 May 2008. EU-China 14-16 June 2010 Science and Technology Week. Zhao, B., Taucer, F., & Rossetto, T. (2009). Field investigation on the performance of building structures during the 12 May 2008 Wenchuan earthquake in China. Engineering Structures, 31(8), 1707- 1723 Zhang, J. & Pan, B., 2005. Methods and development of present aseismic retrofitting. Earthquake Resistance Engineering and Retrofitting, Volume 27 No.1, pp. 56-62.

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13 Appendix

Appendix A – Visual Survey form for the field investigation

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Appendix

Appendix B – Matlab script for Cloud Analysis

clear all; Files=dir( 'C:\Users\lenovo\Desktop\Matlab\GMfiles_for_cloud'); for i=1:100 i a=i+2; FileNames=Files(a).name sx=['load C:\Users\lenovo\Desktop\Matlab\GMfiles_for_cloud\' FileNames,]; eval(sx); nPts = record(1); dt = record(2); % create ground motion input files fid= fopen('C:\Users\lenovo\Desktop\Matlab\current.txt','w'); for k=3:length(record) p=num2str(record(k)); fprintf(fid,'%c',p); fprintf(fid,'%c\n',' '); end fclose(fid); % create write_run_script.tcl fid1=fopen('write_run_script.tcl','wt'); fprintf(fid1, 'set iGMfile "current current";\n'); fprintf(fid1, 'set dt %f \n',dt); fprintf(fid1, 'set nPts %i \n',nPts); fclose(fid1) % call opensees !OpenSees.exe schoolmodel3.tcl

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% process output data

disp_storey3_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey3_y.out'); disp_storey2_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey2_y.out'); disp_storey1_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey1_y.out');

maxdata1 = max(max(disp_storey3_x(:,2)),-min(disp_storey3_x(:,2))); Roof_disp(i,1) = maxdata1;

ISD2x_max = max(disp_storey3_x(:,2)-disp_storey2_x(:,2)); ISD2x_min = min(disp_storey3_x(:,2)-disp_storey2_x(:,2)); ISD2x = max(ISD2x_max,-ISD2x_min); ISD1x_max = max(disp_storey2_x(:,2)-disp_storey1_x(:,2)); ISD1x_min = min(disp_storey2_x(:,2)-disp_storey1_x(:,2)); ISD1x = max(ISD1x_max,-ISD1x_min); ISD0x = max(max(disp_storey1_x(:,2)),-min(disp_storey1_x(:,2))); A = [ISD2x ISD1x ISD0x]; maxISDx = max(A);

Max_ISD(i,1)= maxISDx; end save Roof_disp.mat save Max_ISD.mat

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Appendix C – Matlab script for Incremental Dynamic Analysis clear all;

Saa=dlmread( 'C:\Users\lenovo\Desktop\Matlab\GMfiles_FEMA\Sa.txt'); dtt=dlmread('C:\Users\lenovo\Desktop\Matlab\GMfiles_FEMA\dt.txt');

Files=dir( 'C:\Users\lenovo\Desktop\Matlab\GMfiles_FEMA\EQrecords'); for i=1:44 i a=i+2; FileNames=Files(a).name record=load(FileNames); for n=1:20

% create ground motion input files nPts = length(record); dt = dtt(i,1); scaling=Saa(i,1); fid= fopen('C:\Users\lenovo\Desktop\Matlab\current.txt','w'); for k=1:length(record) p=(record(k)); m=p*0.1*n/scaling; fprintf(fid,'%c',m); fprintf(fid,'%c\n',' '); end fclose(fid);

% create write_run_script.tcl fid1=fopen('write_run_script.tcl','wt'); fprintf(fid1, 'set iGMfile "current current";\n'); fprintf(fid1, 'set dt %f \n',dt); fprintf(fid1, 'set nPts %i \n',nPts); fclose(fid1); % call opensees !OpenSees.exe schoolmodel3.tcl % process output data

disp_storey3_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey3_x.out'); disp_storey2_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey2_x.out');

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disp_storey1_x = load('C:\Users\lenovo\Desktop\Matlab\output\disp_storey1_y.out');

maxdata1 = max(max(disp_storey3_x(:,2)),-min(disp_storey3_x(:,2)));

Roof_disp_x(n,i) = maxdata1; ISD2x_max = max(disp_storey3_x(:,2)-disp_storey2_x(:,2)); ISD2x_min = min(disp_storey3_x(:,2)-disp_storey2_x(:,2));

ISD2x = max(ISD2x_max,-ISD2x_min); ISD1x_max = max(disp_storey2_x(:,2)-disp_storey1_x(:,2)); ISD1x_min = min(disp_storey2_x(:,2)-disp_storey1_x(:,2)); ISD1x = max(ISD1x_max,-ISD1x_min); ISD0x = max(max(disp_storey1_x(:,2)),-min(disp_storey1_x(:,2))); A = [ISD2x ISD1x ISD0x]; maxISDx = max(A); Max_ISD_x(n,i)= maxISDx; end end save Roof_disp_x.mat save Max_ISD_x.mat

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Appendix D – Matlab script for generating spectral acceleration function Sa = getLinearSpectra_withTH(T, z, ag, Dtg) record=ag; dt=Dtg;

N_points=length(record); save current_input.txt record -ASCII k=1; M=((T/(2*pi))^2)*k; v=0.05; C=v*2*(k*M)^0.5; SF=1; fid1=fopen( 'elastic_SDOF.tcl','wt'); fprintf(fid1, 'wipe\n'); fprintf(fid1, 'file mkdir output\n'); fprintf(fid1, 'model BasicBuilder -ndm 3\n'); fprintf(fid1, 'node 1 0. 0. 0.\n'); fprintf(fid1, 'node 2 0. 0. 0.\n'); fprintf(fid1, 'node 3 0. 0. 1.\n'); fprintf(fid1, 'fix 1 1 1 1 1 1 1\n'); fprintf(fid1, 'equalDOF 1 2 1 2 3 4 6\n'); fprintf(fid1, 'rigidLink beam 2 3\n'); fprintf(fid1, 'mass 3 %f 0. 0. 0. 0. 0.\n', M); fprintf(fid1, 'geomTransf Linear 1 -1 0 0\n'); fprintf(fid1, 'uniaxialMaterial Elastic 1 %f\n', k); fprintf(fid1, 'uniaxialMaterial Viscous 2 %f 1\n', C); fprintf(fid1, 'uniaxialMaterial Parallel 3 1 2\n'); fprintf(fid1, 'element zeroLength 1 1 2 -mat 3 -dir 5\n'); fprintf(fid1, 'element elasticBeamColumn 2 2 3 1. 1. 1. 1. 1. 1. 1\n'); fprintf(fid1, 'recorder Node -file output/disp.txt -time -node 3 -dof 1 disp\n'); fprintf(fid1, 'recorder Node -file output/vel.txt -time -node 3 -dof 1 vel\n'); fprintf(fid1, 'recorder Node -file output/accel.txt -time -node 3 -dof 1 accel\n'); %fprintf(fid1, 'recorder Element -file output/int_force.txt -time -ele 1 force\n'); fprintf(fid1, 'constraints Lagrange\n'); fprintf(fid1, 'numberer RCM\n'); fprintf(fid1, 'system SparseGeneral\n'); fprintf(fid1, 'test EnergyIncr 1.0e-8 300 0\n');

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14 Appendix fprintf(fid1, 'integrator Newmark 0.50 0.25\n'); fprintf(fid1, 'algorithm Newton\n'); fprintf(fid1, 'analysis Transient\n'); fprintf(fid1, 'set record "Series -dt %f -filePath current_input.txt - factor %f"\n', dt, SF); fprintf(fid1, 'pattern UniformExcitation 1 1 -accel $record\n'); fprintf(fid1, 'analyze %6.0f %s\n', N_points, dt); fclose(fid1);

! OpenSees elastic_SDOF.tcl displacement_filename=sprintf('output/disp.txt'); displacement=load(displacement_filename); velocity_filename=sprintf('output/vel.txt'); velocity=load(velocity_filename); acceleration_filename=sprintf('output/accel.txt'); acceleration=load(acceleration_filename); Sd=max(max(displacement(:,2)),-min(displacement(:,2))); %Sa_computed=max(max(acceleration(:,2)),-min(acceleration(:,2))); % Sv=Sd.*(2*pi./T); Sa=Sd.*(2*pi./T).^2; function output=IMs_computation(T1)

[record_names]=textread('data.txt', '%s'); N=length(record_names); for i=1:1:N current_record=record_names{i}; name_file_x=[record_names{i}, 'xa_record.mat']; name_file_y=[record_names{i}, 'ya_record.mat']; record_x=importdata(name_file_x); dt_x=record_x(2); record_x(1:2)=[]; ag_x=record_x; Sa_x(i,1)=getLinearSpectra_withTH(T1,0.05,ag_x,dt_x); record_y=importdata(name_file_y); dt_y=record_y(2); record_y(1:2)=[]; ag_y=record_y; Sa_y(i,1)=getLinearSpectra_withTH(T1,0.05,ag_y,dt_y); output=[Sa_x Sa_y]; end

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Appendix E – OpenSees script for 3D modelling of the index building

(Notes: since the actual OpenSees script is too lengthy to put in the appendix, the script given below has been simplified and just for illustration purpose.) # SET UP wipe; set dataDir output; file mkdir $dataDir; # create data directory set GMdir "GMfiles" set g 9.81 set pi [expr acos(-1.0)]

model BasicBuilder -ndm 3 -ndf 6 node 1 0 0 0 node 2 4.5 0 0 node 3 9 0 0 rigidDiaphragm 3 97 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 rigidDiaphragm 3 98 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 rigidDiaphragm 3 99 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 fix 1 1 1 1 1 1 1 fix 2 1 1 1 1 1 1 fix 3 1 1 1 1 1 1 geomTransf Linear 1 0 1 0 geomTransf Linear 2 0 0 -1 #dilosi ilikon# uniaxialMaterial Concrete01 1 -30000 -0.002 -6295.96316070939 0.0135347011565442 uniaxialMaterial Steel02 2 400000 200000000 0.02 20 0.925 0.15 # confined concrete for C1 # uniaxialMaterial Concrete01 3 -31479.8158035469 -0.00209865438690313 -6295.96316070939 0.0135347011565442 #Fiber section for Column C1 # section Fiber 1 -GJ 110026.041666667 { layer straight 2 8 0.000314159265358979 -0.22 0.22 0.22 0.22 layer straight 2 8 0.000314159265358979 -0.22 -0.22 0.22 -0.22 layer straight 2 8 0.000314159265358979 -0.22 0.0733333333333333 -0.22 -0.0733333333333333 layer straight 2 8 0.000314159265358979 0.22 0.0733333333333333 0.22 -0.0733333333333333 patch rect 3 8 8 -0.22 -0.22 0.22 0.22 patch rect 1 8 2 -0.25 0.22 0.25 0.25 patch rect 1 8 2 -0.25 -0.25 0.25 -0.22 patch rect 1 2 8 -0.25 -0.22 -0.22 0.22 patch rect 1 2 8 0.22 -0.22 0.25 0.22

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}

#Fiber sections for beam B1 # section Fiber 73 -GJ 51942.9015965827 { layer straight 2 8 0.0005 0.13 -0.305 -0.13 -0.305 layer straight 2 8 0.000375 -0.13 0.305 0.13 0.305 patch rect 1 8 8 0.13 0.305 -0.13 -0.305 patch rect 1 8 2 0.15 0.325 -0.15 0.305 patch rect 1 8 2 0.15 -0.305 -0.15 -0.325 patch rect 1 2 8 0.15 0.305 0.13 -0.305 patch rect 1 2 8 -0.13 0.305 -0.15 -0.305 } section Fiber 74 -GJ 51942.9015965827 { layer straight 2 8 0.00025 0.13 -0.305 -0.13 -0.305 layer straight 2 8 0.000375 -0.13 0.305 0.13 0.305 patch rect 1 8 8 0.13 0.305 -0.13 -0.305 patch rect 1 8 2 0.15 0.325 -0.15 0.305 patch rect 1 8 2 0.15 -0.305 -0.15 -0.325 patch rect 1 2 8 0.15 0.305 0.13 -0.305 patch rect 1 2 8 -0.13 0.305 -0.15 -0.305 } section Fiber 75 -GJ 51942.9015965827 { layer straight 2 8 0.0005 0.13 -0.305 -0.13 -0.305 layer straight 2 8 0.000375 -0.13 0.305 0.13 0.305 patch rect 1 8 8 0.13 0.305 -0.13 -0.305 patch rect 1 8 2 0.15 0.325 -0.15 0.305 patch rect 1 8 2 0.15 -0.305 -0.15 -0.325 patch rect 1 2 8 0.15 0.305 0.13 -0.305 patch rect 1 2 8 -0.13 0.305 -0.15 -0.305 } #eisagogi elements dokon# element nonlinearBeamColumn 1 25 26 4 -sections 73 74 74 75 2 -iter 100 10e-8 element nonlinearBeamColumn 10 33 35 4 -sections 100 101 101 102 2 -iter 100 10e-8 #eisagogi elements ipostilomaton# element nonlinearBeamColumn112 1 25 10 1 1 -iter 100 10e-8 pattern Plain 1 Linear { eleLoad -ele 1 -type -beamUniform 0 37.5 } # recorder ------recorder Node -file $dataDir/disp_storey3_x.out -time -node 73 -dof 1 disp recorder Node -file $dataDir/disp_storey3_y.out -time -node 73 -dof 2 disp recorder Node -file $dataDir/disp_storey2_x.out -time -node 49 -dof 1 disp recorder Node -file $dataDir/disp_storey2_y.out -time -node 49 -dof 2 disp

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Appendix recorder Node -file $dataDir/disp_storey1_x.out -time -node 25 -dof 1 disp recorder Node -file $dataDir/disp_storey1_y.out -time -node 25 -dof 2 disp # Gravity analysis ------set Tol 1.0e-5 constraints Transformation numberer RCM system BandGeneral test EnergyIncr $Tol 6 algorithm Newton set NstepGravity 10 set DGravity [expr 1./$NstepGravity] integrator LoadControl $DGravity analysis Static set ok 0 set tFinal 1 set tCurrent [getTime] while {$ok == 0 && $tCurrent < $tFinal} { set ok [analyze 1] if {$ok != 0} { puts "=== Regular Newton failed ... Lets try an initial stiffness for this step" test NormDispIncr 1.0e-12 2000 algorithm ModifiedNewton set ok [analyze 1] if {$ok == 0} {puts "=== That worked!...Back to Regular Newton"} test EnergyIncr $Tol 6 algorithm Newton } set tCurrent [getTime] } loadConst -time 0.0 puts "Model built" # EQ loading dynamic analysis ------source write_run_script.tcl set iGMdirection "1 3"; # ground-motion direction set iGMfact "1 1"; # ground-motion scaling factor # ------define & apply damping set xDamp 0.05; # damping ratio set MpropSwitch 1.0; set KcurrSwitch 0.0; set KcommSwitch 1.0; set KinitSwitch 0.0; set nEigenI 1; # mode 1

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set nEigenJ 2; # mode 2 set lambdaN [eigen [expr $nEigenJ]]; # eigenvalue analysis for nEigenJ modes set lambdaI [lindex $lambdaN [expr $nEigenI-1]]; # eigenvalue mode i set lambdaJ [lindex $lambdaN [expr $nEigenJ-1]]; # eigenvalue mode j set omegaI [expr pow($lambdaI,0.5)]; set omegaJ [expr pow($lambdaJ,0.5)]; set alphaM [expr $MpropSwitch*$xDamp*(2*$omegaI*$omegaJ)/($omegaI+$omegaJ)]; # M-prop. damping; D = alphaM*M set betaKcurr [expr $KcurrSwitch*2.*$xDamp/($omegaI+$omegaJ)]; set betaKcomm [expr $KcommSwitch*2.*$xDamp/($omegaI+$omegaJ)]; set betaKinit [expr $KinitSwitch*2.*$xDamp/($omegaI+$omegaJ)]; rayleigh $alphaM $betaKcurr $betaKinit $betaKcomm; # RAYLEIGH damping set IDloadTag 400; # for uniformSupport excitation # Uniform EXCITATION: acceleration input foreach GMdirection $iGMdirection GMfile $iGMfile GMfact $iGMfact { incr IDloadTag; set GMfatt [expr $g*$GMfact]; # data in input file is in g Unifts -- ACCELERATION TH set outFile $GMfile.txt; set AccelSeries "Series -dt $dt -filePath $outFile -factor $GMfatt"; # time series information pattern UniformExcitation $IDloadTag $GMdirection -accel $AccelSeries ; # create Unifform excitation } # perform DYNAMIC ANALYSIS ======system BandGeneral numberer RCM constraints Transformation #test EnergyIncr 1.0e-5 20 0 test NormDispIncr 1.0e-5 1000 integrator Newmark 0.5 0.25 algorithm KrylovNewton analysis Transient set dtAna [expr $dt/1.0] set dtMin 1.0e-8 set dtMax $dtAna set ok 0; set tFinal [expr $nPts * $dt] set tCurrent [getTime "%1.12E"] while {$ok == 0 && $tCurrent < $tFinal} {

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set ok [analyze 1 $dtAna]

if {$ok != 0} { if {[expr $dtAna/2.0] >= $dtMin} {

set dtAna [expr $dtAna/2.0]

puts [format "\nREDUCING time step size (dtNew = %1.6e)" $dtAna] set ok 0 } } else { set tCurrent [getTime "%1.12E"] if {[expr $dtAna*2.0] <= $dtMax} { set dtAna [expr $dtAna*2.0]

puts [format "\nINCREASING time step size (dtNew = %1.6e)" $dtAna]

} } } if {$ok != 0} { puts [format "\nModel failed (time = %1.3e)" $tCurrent] } else { puts [format "\nResponse-history analysis completed"] } puts "Ground Motion Done. End Time: [getTime]"

# if {$ok != 0} { ; # analysis was not successful. # # ------# # change some analysis parameters to achieve convergence # # performance is slower inside this loop # # Time-controlled analysis # set ok 0; # set controlTime [getTime]; # while {$controlTime < $TmaxAnalysis && $ok == 0} { # set controlTime [getTime] # set ok [analyze 1 $DtAnalysis] # if {$ok != 0} { # puts "Trying Newton with Initial Tangent .." # test NormDispIncr $Tol 1000 0 # algorithm Newton -initial # set ok [analyze 1 $DtAnalysis] # test $testTypeDynamic $TolDynamic $maxNumIterDynamic 0 # algorithm $algorithmTypeDynamic

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# }

# if {$ok != 0} { # puts "Trying Broyden .." # algorithm Broyden 8 # set ok [analyze 1 $DtAnalysis] # algorithm $algorithmTypeDynamic # } # if {$ok != 0} {

# puts "Trying NewtonWithLineSearch .." # algorithm NewtonLineSearch .8 # set ok [analyze 1 $DtAnalysis] # algorithm $algorithmTypeDynamic # } # } # }; # end if ok !0

# puts "Ground Motion Done. End Time: [getTime]"

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Appendix F –Damgae States in SeismoStruct

Table 0-1

Damage state Output step ISD Deformed view

First Yielding 15 0.39%

Slight 21 0.59% damage

Moderate 27 0.74% damage

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Extensive 37 1.00% damage

Complete 47 1.31% damage

Table 0-2: Damage views of index building in y-direction

Damage state Output step ISD Deformed view

First Yielding 13 0.28%

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Slight 20 0.42% damage

Moderate 33 0.84% damage

Extensive 44 1.65% damage

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15 Appendix

Complete 55 2.35% damage

Table 0-3: Damage views of FRP retrofitted frame in x-direction

Damage state Output step ISD Deformed view

First Yielding 15 0.39%

Slight 21 0.59% damage

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Moderate 30 0.82% damage

Extensive 62 1.71% damage

Complete 70 1.94% damage

Table 0-4: Damage views of FRP retrofitted frame in y-direction

Damage state Output step ISD Deformed view

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15 Appendix

0.31%

First Yielding 14

Slight 0.47% 19 damage

Moderate 0.93% 31 damage

Extensive 3.00% 63 damage

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Appendix

Complete 3.34% 71 damage

Table 0-5: Damage view of concrete jacketing retrofitted frame in y-direction

Damage state Output step ISD Deformed view

First Yielding 12 0.31%

Slight 16 0.48% damage

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15 Appendix

Moderate 29 0.95% damage

Extensive 66 3.25% damage

Complete 73 3.75% damage

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Appendix

Appendix G –Online Questionnaire

A Case Study of Safer Schools in Sichuan, China: Perceptions, Perspectives and Hazard Adjustments of Students Regarding Earthquakes

Dear students:

Hello! Thanks for your taking part in the programme collaborated by International

Centre for Collaborative Research on Disaster Risk Reduction and University College London EPICentre. This programme focuses on the vulnerability and resilience of school buildings in Sichuan, as well as student perceptions, perspectives and hazard adjustments in terms of earthquake. This questionnaire is used to collect data of perceptions , perspectives and hazard adjustments of undergraduate and graduates in Sichuan province regarding earthquakes.

This questionnaire only applies to students who were then in Sichuan Province when: 1. 12 May 2008 Wenchuan earthquake happened; 2. 20 April 2013 Ya’an earthquake happened.

Please fill in the questionnaire based on truth and your own opinions. Your participation and answers are entirely anonymous and confidential, your answer is only used for research purpose in this programme. Your participation is entirely voluntary, you can quit whenever you feel uncomfortable answering the questions. One person can only complete once, thanks for your cooperation.

Demographic Information

Q1. Your gender? o Female o Male Q2. Your grade? O 1st year o 2nd year o 3rd year o 4th year o graduated in 2016 Q3. Your major? O Philosophy & Law & Education & Literature & History o Economics o Science o Engineering o Agriculture o Medicine o Management o Art & P.E. o Others

2008 Wenchuan EQ (earthquake)

Q4. Where were you when the 2008 EQ happened? 18 cities+ 3 counties

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15 Appendix

Chengdu; Mianyang; Zigong; Panzhihua; Luzhou; Deyang; Guangyuan; Suining;

Neijiang; Leshan; Ziyang; Yibin; Nanchong; Dazhou; Ya’an; Guang’an; Bazhong; Meishan; Aba Autonomous County; Liangshan Autonomous County; Ganzi Autonomous County

Q5. Do you have any behaviours indicated below when it happened: (multiple - choice)

□ 1 Had no ideas what had happened □ 2 Stayed still, did not move till it stopped □ 3 Looked for shelter nearby to hide □ 4 Ran alone on yourself □ 5 Followed the command from teachers/parents □ 6 Did not follow the instructions but followed the aimless swarm □ 7 Jumped off building □ 8 Others______.

Q6. During the earthquake, you (multiple choices)

□ 1 were physically hurt □ 2 were mentally hurt □ 3 your relatives or friends died □ 4 your relatives or friends injured □ 5 your acquaintances died □ 6 your acquaintances injured □ 7 witnessed people you didn’t know died □ 8 witnessed people you didn’t know got injured □ 9 the above are all not applicable

Q7. Your feelings about your school or community performances:

Strongly Neither pleased Not Strongly Not Pleased pleased nor not pleased pleased not pleased applicable 1 Pre-EQ knowledge ○ ○ ○ ○ ○ ○ learning 2 Pre-EQ drills ○ ○ ○ ○ ○ ○ 3 Quality of the ○ ○ ○ ○ ○ ○ buildings 4 Quality of the ○ ○ ○ ○ ○ ○ facilities

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5 Planning and ○ ○ ○ ○ ○ ○ environment 6 Command and ○ ○ ○ ○ ○ ○ evacuation instructions

Q8. After the earthquake, your feelings:

Strong Moderate Not applicable 1 Anxious ○ ○ ○ 2 Sad ○ ○ ○ 3 Fearful ○ ○ ○ 4 Inferior ○ ○ ○

5 Angry ○ ○ ○

6 Helpless ○ ○ ○ 7 Complaining ○ ○ ○ 8 Hardly talk ○ ○ ○ 9 Isolated ○ ○ ○ 10 World/School-weary ○ ○ ○

11 Peaceful ○ ○ ○ 12 Safe and secure ○ ○ ○ 13 Hopeful ○ ○ ○ 14 Motivated ○ ○ ○ 15 Optimistic ○ ○ ○

Q9. After the earthquake, you:

Yes No 1 Paid attention to the news ○ ○ 2 Paid attention to risk mitigation/reduction ○ ○ knowledge 3 Talked about the news with the others ○ ○ 4 Propagated related knowledge to the others ○ ○

Q10. After the earthquake, the performances of your school or community:

Very Good Neither Bad Very N/A good good or bad bad 1 Care and consultation to your ○ ○ ○ ○ ○ ○ psychological health 2 Care and consultation to your ○ ○ ○ ○ ○ ○

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physical health

3 Care and consultation to your ○ ○ ○ ○ ○ ○ family 4 Earthquake knowledge/safety ○ ○ ○ ○ ○ ○ education 5 Improvement to the ○ ○ ○ ○ ○ ○ infrastructures nearby your house

6 Improvement to the environment ○ ○ ○ ○ ○ ○ nearby your house

2013 Ya’an Earthquake

Q11-Q17 are the same as Q4-Q10

Status Quo

Q18. In which region is your university located? 18 cities+ 3 counties

Q19. Are there any activities concerning earthquake reduction and mitigation being held within one year in your university or community? ○ Yes. ○ No. ○ I don’t know.

Q20. If Yes, have you participated in any of the activities?

○ Yes, I found it helpful. ○ Yes, but I didn’t find it helpful. ○ No. ○ I cannot remember.

Q21. How do you think of hosting earthquake knowledge and mitigation propagation activities regularly? ○ Very necessary. ○ Necessary. ○ Neutral. ○ Not necessary. ○ Not necessary at all.

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Q22. If necessary, which group(s) do you want to take part in with most, and find the most reliable?

□ 1 Schools themselves □ 2 Student associations and clubs □ 3 Government

□ 4 Local communities

□ 5 Social professional companies or organisations □ 6 Others ______*

Q23. If not necessary, why: ______*

Q24. Are there any drills concerning earthquake being held within one year in your university or community? ○ Yes. ○ No. ○ I don’t know.

Q25. If Yes, have you participated in any of the activities?

○ Yes, I found it helpful. ○ Yes, but I didn’t find it helpful. ○ No. ○ I cannot remember.

Q26. How do you think of hosting drills regularly?

○ Very necessary. ○ Necessary. ○ Neutral. ○ Not necessary. ○ Not necessary at all.

Q27. If necessary, which group(s) do you want to take part in with most, and find the most reliable?

□ 1 Schools themselves □ 2 Student associations and clubs □ 3 Government □ 4 Local communities

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□ 5 Social professional companies or organisations

□ 6 Others ______*

Q28. If not necessary, why: ______*

Q29. Do you learn about earthquake reduction and mitigation by yourself?

○ I learn regularly. ○ I learn occasionally. ○ Only after big or special events. ○ Never.

Q30. What aspects would you like to know about earthquake risk reduction and mitigation? (multiple-choice) □ 1 EQ prediction and forecasting □ 2 EQ evacuation techniques and routes □ 3 Techniques of protecting yourself during an EQ □ 4 Techniques of helping others after an EQ □ 5 Reconstruction and donation □ 6 Others ______*

Q31. Please share your ideas with us: ______.

Q32. If you are interested in our research and want to communicate with us, please leave your contact information: ______.

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Appendix H – In-depth interview questions(IQs)

IQ 1. What’s your grade, major? What was your grade when the 2008 earthquake happened? Where were you then? IQ2. Describe the situation when the earthquake happened. The actions of people you saw, heard.

IQ3. Have your psychological conditions changed after the earthquake, what were the changes? Does this affect your future planning and decision-making? How? IQ4. Regarding your awareness of earthquake risk reduction and mitigation, were there any changes? IQ5 -IQ8 change the 2008 earthquake into the 2013 earthquake. IQ 9. Do you know about any activities concerning earthquake risk mitigation in your university? What do you think of them? And do you think your knowledge of coping with earthquakes is enough? IQ10. Can you feel a sense of security living in your university concerning seismic risks? Answer from the aspects of the environment and the facilities. Good enough? Are there a ny improvement that you think necessary?

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