Estimation of Ground Structure and Its Impact on Structural Damage of Building

ESTIMATION OF GROUND STRUCTURE AND ITS IMPACT ON STRUCTURAL DAMAGE OF BUILDING

TNG KAH LOK

SCHOOL OF CIVIL ENGINEERING UNIVERSITI SAINS MALAYSIA 2019

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ESTIMATION OF GROUND STRUCTURE AND ITS IMPACT ON STRUCTURAL DAMAGE OF BUILDING

TNG KAH LOK

This dissertation is submitted to

UNIVERSITI SAINS MALAYSIA

As partial fulfilment of requirement for the degree of

BACHELOR OF ENGINEERING (HONS.) (CIVIL ENGINEERING)

School of Civil Engineering, Universiti Sains Malaysia

June 2019

SCHOOL OF CIVIL ENGINEERING ACADEMIC SESSION 2018/2019

FINAL YEAR PROJECT EAA492/6 DISSERTATION ENDORSEMENT FORM

Title: ESTIMATION OF GROUND STRUCTURE AND ITS IMPACT ON STRUCTURAL DAMAGE OF BUILDING

Name of Student: TNG KAH LOK

I hereby declare that all corrections and comments made by the supervisor(s) and examiner have been taken into consideration and rectified accordingly.

Signature: Approved by:

______

(Signature of Supervisor)

Date: Name of Supervisor: Date :

Approved by:

______

(Signature of Examiner)

Name of Examiner:

Date :

ACKNOWLEDGEMENT

First, I would like to express my deepest gratitude to my supervisor, Assoc. Prof.

Dr. Lau Tze Liang for his continuous support throughout my time as his student and research assistant. His patient guidance, advices and encouragement had greatly helped me in completing this project within the given time frame. I appreciated that my supervisor has provided me an excellent atmosphere and resources to write my dissertation. I have been so lucky to have him as my supervisor.

Besides, I would also like to thank Ho Kar Meng, Khoo Zheng Yang, Lee Jian

Yee, Richard Ng Eng Yao and Tan Kang Chin, who have contributed their time and effort in helping me to carry out my field work. My field work would not be done without their contributions. I am indebted to them for their helps and hopefully I have the opportunity to help them back in the nearest future.

Lastly, I would like to thank my parent for all the moral support and patient during the process of completing my dissertation. This accomplishment would not have been possible without them.

ABSTRAK

Pembentukan tanah tempatan adalah salah satu faktor yang mempengaruhi kerosakan seismik terhadap bangunan. Gerakan tanah berbeza di lokasi yang berlainan mengikut keadaan tanah setempat. Pelbagai kerosakan diperhatikan selepas gempa bumi

Sabah bermagnitud 6.0 melanda Ranau pada tahun 2015. Kesan struktur tanah terhadap kerosakan struktur masih belum disiasat. Oleh itu, pengukuran mikrogegaran tatasusunan dan titik tunggal telah dijalankan untuk menganggar struktur tanah di SMK Ranau dan

Hospital Ranau. Kesan struktur tanah terhadap sambutan struktur bangunan di tapak telah dinilai. Struktur tanah kedua-dua tapak tersebut dianggarkan menggunakan kaedah

Autokorelasi Ruangan (SPAC) untuk SMK Ranau dan kaedah keelipsan gelombang

Rayleigh untuk Hospital Ranau. Struktur tanah SMK Ranau dan Hospital Ranau telah dianggarkan dan masing-masing dikelaskan sebagai tanah jenis B dan C. Struktur tanah yang dianggarkan kemudiannya diubahsuai kepada jenis tanah lain dan sambutan tanah bagi setiap jenis tanah dianalisis dengan menggunakan Analisis Sambutan Gempa Bumi

Lelurus Setara (EERA). Tanah jenis B, C dan D dibandingkan dari segi pecutan permukaan tanah dan hasilnya menunjukkan bahawa tanah jenis D memberikan sambutan tanah dan faktor amplifikasi tanah tertinggi kerana ia menunjukkan pecutan permukaan tanah puncak terbesar, diikuti oleh tanah jenis C dan B. Bagi sambutan struktur yang disimulasikan oleh STERA 3D, hanyutan tingkat maksimum, anjakan tingkat maksimum, daya ricih tingkat maksimum dan ricih dasar-hanyutan atas digunakan untuk menyiasat sambutan struktur bangunan di atas struktur tanah yang berbeza. Di SMK Ranau, tanah jenis D memberi sambutan struktur terbesar, diikuti dengan tanah jenis C dan B. Kerosakan stuktur yang lebih teruk adalah dijangkakan jika bangunan ini berada di atas tanah jenis C atau D. Bagi Hospital Ranau, kecuali untuk anjakan tingkat maksimum yang terbesar untuk tanah jenis D, sambutan struktur lain

iii menunjukkan tanah jenis C memberikan impak terbesar, diikuti dengan tanah jenis D dan sambutan terendah diberikan oleh tanah jenis B. Kerosakan Hospital Ranau adalah dipengaruhi oleh penguatan tapak terhadap gerakan bumi.

ABSTRACT

Local soil formation is one of the factors that affect the seismic damage of building. Ground motion varies in different locations according to the local ground condition. Various extents of structural damages were observed after a 6.0 magnitude

Sabah earthquake hit Ranau in 2015. The effect of ground structure to structural damage has not been investigated. Therefore, microtremor array and single point measurement were conducted to estimate the ground structure at SMK Ranau and Hospital Ranau. The impact of ground structure to structural response of buildings at the site was evaluated.

The ground structures of both sites were estimated using Spatial Autocorrelation (SPAC) method for SMK Ranau and Rayleigh wave ellipticity method for Hospital Ranau.

Ground structures of SMK Ranau and Hospital Ranau were estimated and classified as ground types B and C, respectively. The estimated ground structures were then modified to other ground types and the ground response of each ground type was analysed using

Equivalent-linear Earthquake Response Analysis (EERA). Ground types B, C and D were compared in term of ground surface acceleration and results shows that ground type

D gave the largest ground response and amplification factor as it showed the largest peak ground surface acceleration, followed by ground types C and B. For structural response simulated by STERA 3D software, the maximum storey drift, maximum storey displacement, maximum storey shear force and the base shear-top drift were used to investigate the structural response of building on different ground structures. In SMK

Ranau, ground type D gave the largest structural responses, followed by ground types C and B. It is expected that more severe damage could happen if the building is located on ground types C and D. For Hospital Ranau, except for the maximum storey displacement which shows the largest for ground type D, the other structural responses show that ground type C gave the largest impact, followed by ground type D and the least response

v is given by ground type B. The damage of Hospital Ranau is influenced by the site amplification of ground motion.

TABLE OF CONTENTS

ACKNOWLEDGEMENT ...... II

ABSTRAK ...... III

ABSTRACT ...... V

TABLE OF CONTENTS ...... VII

LIST OF FIGURES ...... X

LIST OF TABLES ...... XV

LIST OF ABBREVIATIONS ...... XVII

CHAPTER 1 ...... 1

1.1 Background ...... 1 1.2 Study Area ...... 6 1.3 Problem Statement ...... 7 1.4 Objectives ...... 9 1.5 Scope of Study ...... 9 1.6 Dissertation Outline ...... 9

CHAPTER 2 ...... 11

2.1 Overview ...... 11 2.2 Historical Earthquake Events in Sabah ...... 11 2.3 Geological Setting in Ranau ...... 14 2.4 Local Geological Condition Impact on Seismic Damage ...... 16 2.5 Microtremor Observation Technique ...... 17 2.5.1 Phase Velocity and Dispersion Curve...... 19 2.5.2 Spectral Ratio of H/V of Rayleigh Wave ...... 20 2.6 SPAC and Rayleigh Wave Ellipticity Method ...... 22 2.6.1 Spatial Autocorrelation (SPAC) Method ...... 22 2.6.2 Ellipticity of Rayleigh Wave ...... 26 2.7 Ground Response Analysis ...... 32

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2.8 Eurocode 8 (EC8) and Malaysia National Annex (NA) ...... 33 2.9 Structural Earthquake Response Analysis ...... 37 2.10 Summary ...... 41

CHAPTER 3 ...... 43

3.1 Overview ...... 43 3.1 Desk Study ...... 43 3.1.1 Literature Search ...... 43 3.1.2 Study Area ...... 45 3.2 Field Work (Microtremor Measurement) ...... 45 3.3 Data Analysis ...... 54 3.3.1 Spatial Autocorrelation (SPAC) Method ...... 54 3.3.2 Ellipticity of Rayleigh Wave ...... 58 3.4 Equivalent-linear Earthquake Response Analysis (EERA) ...... 59 3.5 Structural Earthquake Response Analysis 3D (STERA 3D) ...... 64 3.6 Result Interpretation...... 67

CHAPTER 4 ...... 68

4.1 Overview ...... 68 4.2 Ground Structure Estimation ...... 68 4.2.1 SMK Ranau Based on SPAC Method ...... 68 4.2.2 Hospital Ranau Based on Rayleigh Wave Ellipticity Curve Method ...... 71 4.3 Ground Structure Response ...... 73 4.3.1 Ground Surface Acceleration of Estimated Ground Structure ...... 73 4.3.2 Ground Surface Acceleration of Modified Ground Structure...... 76 4.3.3 Peak Acceleration and Amplification Factor ...... 78 4.4 Structural Earthquake Response ...... 80 4.4.1 Mode Shape and Natural Frequency ...... 81 4.4.2 Non-linear Static Pushover Analysis ...... 84 4.4.3 Maximum Storey Drift ...... 88 4.4.4 Maximum Displacement ...... 91 4.4.5 Maximum Storey Shear Force ...... 98

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4.4.6 Base Shear-Top Drift ...... 100

CHAPTER 5 ...... 105

5.1 Conclusions ...... 105 5.2 Recommendations ...... 107

REFERENCE ...... 108

APPENDIX A

APPENDIX B

APPENDIX C

APPENDIX D

APPENDIX E

APPENDIX F

APPENDIX G

LIST OF FIGURES

Figure Title Page

Figure 1.1 : Location of Malaysia and the Pacific Ring of Fire 2 (Encyclopædia Britannica, 2019)

Figure 1.2 : Seismic damages at SMK Ranau after 2015 Sabah 3 earthquake (Tan, 2016)

Figure 1.3 : Seismic damages at Hospital Ranau after 2015 Sabah 3 earthquake (Tan, 2016)

Figure 1.4 : Location of study area sites (SMK Ranau and Hospital 7 Ranau)

Figure 2.1 : Tectonic setting of Sabah with major plate movements 12 (Tongkul, 2017)

Figure 2.2 : Historical earthquake events in Sabah since year 1923 13 (USGS, 2019a)

Figure 2.3 : Estimated peak ground acceleration and intensity map of 5th 13 June 2015 Sabah earthquake (USGS, 2015)

Figure 2.4 : Estimated intensity map in Ranau near to the epicenter on 14 5th June 2015 Sabah earthquake (USGS, 2015)

Figure 2.5 : Geological Map of Sabah (JMG, 2018) 15

Figure 2.6 : Illustration of site amplification of 1985 Michaocan 17 earthquake (Singh et al., 2015)

Figure 2.7 : Comparison between the dispersion curve obtained using 24 SPAC method (continuous line and points) and F-K method (dashed line) (Estrella and Gonzalez, 2003)

Figure 2.8 : Shear wave velocity profile from inversion of dispersion 25 curve using SPAC method (Estrella and Gonzalez, 2003)

Figure 2.9 : Shear wave velocity profile for each site at Hsinchu, Taiwan 25 (Morikawa et al., 2009)

Figure 2.10 : Comparison between measured H/V spectra ratio of 27 Rayleigh wave at different sites with the theoretical ellipticity correspond to substructure model estimated from phase velocity (Boore and Toksöz, 1969)

Figure 2.11 : (a) Comparison between H/V ratios of observed noise (thin 28 lines), frequency–time analysis method (thin grey line),

modal summation method (thick black line), with the ellipticity of the fundamental-mode Rayleigh wave for structure model (b) (thick grey line). (b) Structural model obtained from the inversion of the observed H/V ratios (thick black line) compared to the ground structure given by Maurer et al (1999) (thin grey line) (Giardini et al., 2001)

Figure 2.12 : 3D shear wave velocity model of the Coatzacoalcos City, 29 Veracruz. (Morales et al., 2014)

Figure 2.13 : Shear wave velocity profiles of computed soil models and 30 NSPT from borelog at PUM (top) and SEC (bottom) (Tan et al., 2018)

Figure 2.14 : H/V spectra ratio of microtremors of array radius (1 m (a) 31 and 10 m (b)) and ellipticity curve of Rayleigh wave from respective ground structure models at SEC (top) and PUM (bottom) (Tan et al., 2018)

Figure 2.15 : Amplification between surface and base motion 38 (GovindaRaju et al., 2004)

Figure 2.16 : Variation of natural frequency with number of bays and 38 storeys in (a) first mode (b) second mode (GovindaRaju et al., 2004)

Figure 2.17 : Base shear of different buildings on different soil layers 39 thickness and soil types (Chang, 2016)

Figure 2.18 : Base shear of different buildings on different soil layers 40 thickness and soil types (Chang, 2016)

Figure 2.19 : Storey shear drift of 6 storey building on different soil layers 40 thickness and soil types (Chang, 2016)

Figure 3.1 : Flowchart of methodology 44

Figure 3.2 : Aerial view of SMK Ranau and Hospital Ranau 46

Figure 3.3 : Microtremor instrument set 46

Figure 3.4 : Microtremor sensor arrangement for 5 m radius array 49

Figure 3.5 : Microtremor array measurement at SMK Ranau 49

Figure 3.6 : Microtremor single point measurement at Hospital Ranau 50

Figure 3.7 : Calibration factor of ITK 2 in X-direction 51

Figure 3.8 : Calibration factor of ITK 2 in Y-direction 52

Figure 3.9 : Calibration factor of ITK 2 in Z-direction 52

Figure 3.10 : Calibration factor of ITK 2 in X-direction after calibration 53

Figure 3.11 : Calibration factor of ITK 2 in Y-direction after calibration 53

Figure 3.12 : Calibration factor of ITK 2 in Z-direction after calibration 54

Figure 3.13 : General procedure of SPAC method analysis 55

Figure 3.14 : Arrangement of sensors and their coordinates for 58 microtremor array measurement

Figure 3.15 : Dispersion curve generated from microtremor array 57 measurement

Figure 3.16 : H/V spectra ratio of five selected time intervals from 59 measurement data

Figure 3.17 : Ellipticity curve evaluated from the estimated shear wave 59 velocity profile

Figure 3.18 : Locations of seismic stations with acceleration recorded and 61 distance with epicenter for 2015 Ranau Earthquake (Chang, 2016)

Figure 3.19 : Bedrock motion and ground surface motion 62

Figure 3.20 : Input acceleration time history in East-West (left top), 63 North-South (right top) and Up-Down (bottom) direction

Figure 3.21 : Plan view (left) and 3D model (right) of building at SMK 65 Ranau

Figure 3.22 : Plan view (left) and 3D model (right) of building at Hospital 65 Ranau

Figure 3.23 : Representative section of column and beam 66

Figure 4.1 : Comparison of theoretical dispersion curve from estimated 69 ground structure with measured dispersion curve

Figure 4.2 : Estimated shear wave velocity profile of SMK Ranau 70

Figure 4.3 : Comparison of H/V spectral ratio of different time intervals 72 and ellipticity curve

Figure 4.4 : Estimated shear wave velocity profile of Hospital Ranau 73

Figure 4.5 : Ground surface acceleration in EW direction at SMK Ranau 74

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Figure 4.6 : Ground surface acceleration in NS direction at SMK Ranau 74

Figure 4.7 : Ground surface acceleration in UD direction at SMK Ranau 75

Figure 4.8 : Ground surface acceleration in EW direction at Hospital 75 Ranau

Figure 4.9 : Ground surface acceleration in NS direction at Hospital 75 Ranau

Figure 4.10 : Ground surface acceleration in UD direction at Hospital 76 Ranau

Figure 4.11 : First three vibration mode shapes for building in SMK 81 Ranau

Figure 4.12 : First three vibration mode shapes for building in SMK 82 Ranau

Figure 4.13 : First three vibration mode shapes for building in Hospital 82 Ranau

Figure 4.14 : First three vibration mode shapes for building in Hospital 83 Ranau

Figure 4.15 : Relationships between shear force and story drift angle for 84 building in SMK Ranau

Figure 4.16 : Relationships between shear force and story drift angle for 85 building in Hospital Ranau

Figure 4.17 : Demonstration of damages corresponding to drift angles on 86 building in SMK Ranau subjected to uniform lateral load in X-direction (arrow)

Figure 4.18 : Demonstration of damages corresponding to drift angles on 87 building in Hospital Ranau subjected to uniform lateral load in X-direction (arrow)

Figure 4.19 : Maximum storey drift angle in X- and Y-direction on ground 88 types B, C and D for building in SMK Ranau

Figure 4.20 : Maximum storey drift angle in X- and Y-direction on ground 89 types B, C and D for building in Hospital Ranau

Figure 4.21 : Maximum storey displacement in X- and Y-direction on 92 ground types B, C and D for building in SMK Ranau

Figure 4.22 : Maximum storey displacement in X- and Y-direction on 92 ground types B, C and D for building in Hospital Ranau

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Figure 4.23 : Top building displacement in X- and Y-direction on ground 94 types B, C and D for building in SMK Ranau

Figure 4.24 : Top building displacement in X- and Y-direction on ground 95 types B, C and D for building in Hospital Ranau

Figure 4.25 : Orbit of top displacement of building in SMK Ranau on 96 ground types B, C and D

Figure 4.26 : Orbit of top displacement of building in Hospital Ranau on 97 ground types B, C and D

Figure 4.27 : Maximum storey shear force of the buildings in SMK Ranau 98 on ground types B, C and D

Figure 4.28 : Maximum storey shear force of the buildings in Hospital 99 Ranau on ground types B, C and D

Figure 4.29 : Relationship of top drift and base shear for building at SMK 101 Ranau on ground types B, C and D

Figure 4.30 : Relationship of top drift and base shear for building at 102 Hospital Ranau on ground types B, C and D

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LIST OF TABLES

Table Title Page Table 2.1 : Application of SPAC and H/V ellipticity of Rayleigh wave 22 method in estimating shear wave velocity profile Table 2.2 : Ground type classification (Table 3.1, MS EN 1998- 35 1:2015) Table 2.3 : Ground type classification scheme in accordance to site 36 natural period for soil deposit exceeding 30 m in depth (Table A1, Malaysia NA to MS EN 1998-1:2015) Table 3.1 : Summary of location of study site 46 Table 3.2 : Specification of velocity sensor 47 Table 3.3 : Microtremor measurement guidelines (SESAME, 2004) 48 Table 3.4 : Calibration Factor for Different Microtremor Sensors 52 Table 3.5 : Boundary limit for each layer (Cheah, 2018) 57 Table 3.6 : Types of worksheets in EERA and their contents (Bardet 60 et al., 2000) Table 3.7 : Peak acceleration of ground motion in three direction 63 Table 3.8 : List of materials and details 66 Table 3.9 : Parameters of building model at SMK Ranau and Hospital 66 Ranau Table 4.1 : Estimated shear wave velocity profile of SMK Ranau 70 analysed using SPAC method in numerical form Table 4.2 : Estimated shear wave velocity profile of Hospital Ranau 72 analysed using Rayleigh wave ellipticity method in numerical form Table 4.3 : Estimated ground structure and modified ground structure 77 with various ground type classification for SMK Ranau Table 4.4 : Estimated ground structure and modified ground structure 77 with various ground type classification for Hospital Ranau Table 4.5 : Summary of peak accelerations and amplification factors 79 for estimated and modified ground structures in SMK Ranau

Table 4.6 : Summary of peak accelerations and amplification factors 79 for estimated and modified ground structures in Hospital Ranau Table 4.7 : Summary of natural frequencies of first three mode shapes 83 for building at SMK Ranau and Hospital Ranau Table 4.8 : Summary of base shear coefficient for ground types B, C 103 and D in SMK Ranau and Hospital Ranau Table 4.9 : Percentage difference of base shear coefficient from 104 dynamic analysis and non-linear static pushover analysis

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LIST OF ABBREVIATIONS

SPAC Spatial Autocorrelation

EERA Equivalent- linear Earthquake Site Response Analysis of Layered Soil

Deposit software

H/V Spectral ratio of horizontal to vertical components

EC8 Eurocode 8

NA Malaysia National Annex

VS,30 Average shear wave velocity for top 30 m sediment

VS Weighted average shear wave velocity over the total thickness of soil

layers

TS Small strain site natural period

NSPT Number of blows of Standard Penetration Test

cu Undrained shear strength

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CHAPTER 1

INTRODUCTION

1.1 Background

Earthquake happens when the moving plates in Earth’s crust experience sudden breaks, slips, and collisions between ground mass along a fault. The sudden movement releases accumulated energy built up due to small sliding movement between plates. The energy is released in all direction from the focus of the earthquake in form of waves that propagate through the underground layer to the surface. This results in strong ground motion on the surface, causing destructive damage to structures and lives on ground.

Malaysia is located at the Sunda Plate, near to the Pacific Ring of Fire as shown in Figure 1.1. The Pacific Ring of Fire, according to the US Geological Survey (USGS), is the most active zone of frequent earthquake and volcanic eruption in the world (USGS,

2019b). This causes Malaysia especially in East Malaysia often subjected to earthquakes since the past. At East Malaysia, Sabah is surrounded by three most seismically active plate boundaries which are Eurasian Plate, Indian-Australian Plate, and Philippine Plate

(Tongkul, 2017). This causes Sabah has become the most seismically active area in

Malaysia. Since 1923, there are 64 earthquake events stroke East Malaysia with magnitudes more than 4.0 from the records based on USGS seismic catalogue in 2018.

Figure 1.1: Location of Malaysia and the Pacific Ring of Fire (Encyclopædia Britannica, 2019)

After the 2015 Sabah earthquake with a magnitude of 6.0 in Ranau, the loss of lives and significant damages to the city due to the earthquake had led to the study of seismology in Ranau and investigation of factors affecting the earthquake events. Figure

1.2 and Figure 1.3 show some of the damages during the 2015 Sabah earthquake. Other than the inadequate seismic design for buildings, the geotechnical aspects also play an important role in seismic damage effect of building (Tokimatsu et al., 1996). The geotechnical aspect mainly refers to the local geological condition and the ground subsurface soil condition.

Figure 1.2: Seismic damages at SMK Ranau after 2015 Sabah earthquake (Tan, 2016)

Figure 1.3: Seismic damages at Hospital Ranau after 2015 Sabah earthquake (Tan, 2016)

Local geological condition influences the local site effect induced by an earthquake. Historical events occurred around the world have shown that local site effect causes greater impact on structural damage. This can be demonstrated in the 1985

Michoacan event with a magnitude of 8.1, the 1989 Loma Prieta event with a magnitude of 7.1, and the 1990 Philippine event with a magnitude of 7.7 (Takemura et al., 1991).

When the seismic wave originates from focus of an earthquake and propagates from bedrock through soil layers, the seismic wave experiences refraction and reflection. The

3 different densities and properties of soil layers modify the seismic wave characteristic, where the soft sediment layer amplifies the seismic wave amplitude and the ground motion.

The event of 1923 Kanto earthquake has shown that the effect of local geology on damage to superstructure. This has led to the early study on the evaluation of effect of local geological condition using convenient and low-cost method (Bard, 1999), instead of expensive and slow conventional geophysical exploration method by drilling boreholes. It is important to know the local geology in order to determine the local site effect as well as for the seismic resistant design of structures. By knowing the local ground structure, the site amplification of ground motion, as the result of propagation of body wave from hard bedrock to upper soft soil layer, can be predicted. The other local site effects from the earthquake such as soil liquefaction can also be predicted from the obtained ground structure. This can help in the design of seismic resistant structure by modeling the structure and analyze its structural response to earthquake.

In the way finding low cost and convenient tools for estimation of local geological condition, feasible, economical, user- and environmental-friendly considerations have made the non-destructive microtremor measurement method to become the preferred approach. Microtremor is the ambient vibration of the ground with low amplitude and seismic frequency, caused by man-made or natural disturbance.

Microtremor measurements have been widely used for seismic response studies and subsurface structure estimation. It requires the surface wave inversion using the estimated phase velocity from microtremor to give detailed shear wave velocity structure of the ground. The obtained shear wave velocity profile (VS profile) represents to the ground structure profile.

There are two conventional data processing techniques to obtain the phase velocities from microtremor array measurements. The phase velocity of microtremors was first estimated by Aki (1957) and developed the Spatial Autocorrelation (SPAC) method. The further study on estimation of phase velocity of microtremors has confirmed the microtremors are primarily composed of surface waves (Nogoshi, 1971). This had led to the subsurface exploration using phase velocity of surface waves (Horike, 1996).

The second method is the frequency-wave number spectral (F-K) method (Capon, 1969).

It requires at least seven microtremor stations and larger aperture of array for microtremor array measurement. Thus, the SPAC method is more efficient in term of workmanship and analysis as compared to the F-K method.

The spectral ratio of horizontal to vertical component (H/V) is another method for subsurface structure estimation. The H/V power spectral ratio can be expressed as the

Rayleigh waves ellipticity to investigate the subsurface structure and the site amplification (Malischewsky and Scherbaum, 2004). The ellipticity of Rayleigh wave, based on the inversion of H/V spectral ratio which can be determined by using a single station three-component sensor, can be used for estimating the shear wave velocity profile, provided that the layer thickness or the shear wave velocity is known (Arai and

Tokimatsu, 2004).

In Sabah, due to insufficient soil investigation data, the local geological condition is unknown. The effect of ground structure to the structural damage in 2015 Sabah

Earthquake has yet to be investigated. Therefore, the microtremor observation techniques are then used to estimate the shear wave velocity profile and so the ground structure. The estimated ground structure is then used to investigate its impact on the structural performance during an earthquake event. This is important for the prediction of seismic

5 performance of structure as well as the enhancement of seismic design for building in

Sabah.

After obtaining the estimated ground structure, the local site amplification can be predicted and seismic response of the structure above the ground can be investigated if earthquake happens. The structural seismic response can be investigated by modelling the structure with input of the ground structure properties. From the structural seismic response analysis, the degree of damage of the building can be predicted and improvement in the design of seismic resistant structure can be done to reduce the impact of earthquake on the building.

1.2 Study Area

This study was carried out at SMK Ranau and Hospital Ranau. SMK Ranau and

Hospital Ranau are located in Ranau district in north-west region of Sabah. The location of Ranau district, SMK Ranau and Hospital Ranau are shown in Figure 1.4. On 5th June

2015, a 6.0 magnitude earthquake stroke Ranau district and caused destructive damage to buildings. Damages of the buildings at these two study sites had been investigated by

Tan (2016) and Lim (2016). The predominant ground period and amplification level in

Ranau during 2015 Sabah earthquake was also determined by Tan (2016) and Chang

(2016).

Figure 1.4: Location of study area sites (SMK Ranau and Hospital Ranau)

1.3 Problem Statement

There is still lack of study on the local geological condition in the Ranau district after the 5th June 2015 Sabah earthquake. The study area focused in this research is

Ranau, Sabah. After the earthquake event on 5th June 2015, the repair works on the damaged building have been done without any consideration of seismic resistant. To minimize the damage of building caused by earthquake in future, it is crucial to take into consideration for the ground structure characteristic and its impact on seismic response of building in designing building with improved performance against the earthquake action.

Damages of building are observed at certain locations in Ranau during the 2015

Sabah earthquake. It is strongly believed that the local geological condition affects the structural response. Tan (2016) has conducted single point microtremor observation in the affected areas and the predominant frequency of the ground was plotted. However, the detailed ground structure of the site was not investigated. The subsurface structure in

7 the affected areas is still unknown due to unavailable of borelog data or measurement at site. The effect on ground structure to the damage of building has not been investigated and the actual performance of the existing buildings to withstand earthquake action is unknown. Therefore, it is vital to estimate the ground structure using a low cost and convenient method.

With the estimated ground structure, the ground motion characteristic can be determined and the impact of ground structure on structural damage of buildings in

Ranau can be investigated through structural modelling. This is because different soil layers can result in different response of ground surface vibration as well as structural response during an earthquake event. By knowing the ground motion characteristic, it can help in the improvement and amendment of seismic resistant design of building in

Malaysia, especially in Sabah where it is often subjected to earthquake event. Hence, the structural response of building subjected to Sabah Earthquake considering the local ground structure is essentially to be investigated.

1.4 Objectives

The main objectives of this study are:

i. To determine the detailed ground structure at SMK Ranau and Hospital

Ranau using microtremor observation technique

ii. To investigate the impact of ground structure on structural response of

buildings at SMK Ranau and Hospital Ranau using 2015 Sabah earthquake

record

1.5 Scope of Study

This research focuses on ground structure estimation at SMK Ranau and Hospital

Ranau using microtremor array observation and single point observation techniques, respectively. Microtremor array measurement with radius of 5 m was conducted at the study areas. The analysis methods which are SPAC method and Rayleigh wave ellipticity method were used to estimate the shear wave velocity (Vs) profile of the ground structure.

The structural responses were then analysed for SMK Ranau and Hospital Ranau buildings subjected to the 2015 Sabah earthquake record at KKM station. Three ground types namely ground type B, C and D were considered and the impact of ground structure on the structural damage at the study area was assessed.

1.6 Dissertation Outline

This dissertation consists of five chapters as follows:

Chapter 1 gives an overview of this research. This chapter presents the background, study area, problem statement, objectives, scope of study and dissertation outline.

Chapter 2 discusses the related past studies done by researchers. This chapter involves the previous studies related to the historical earthquake event in Sabah, local geological impact to structural damage, theory of microtremor array measurement, application of SPAC and Rayleigh wave ellipticity methods and the structural response analysis.

The methodology used in this research is presented in Chapter 3. This chapter discusses the approaches used in desk study, microtremor array observation, data analysis using SPAC and Rayleigh wave ellipticity methods, comparison of estimated ground structures, simulation of ground surface motion and structural modelling analysis.

Chapter 4 discusses the results obtained from the present research. The results include analysed data from microtremor observation techniques, namely, ground structure estimated using SPAC and Rayleigh wave ellipticity methods. This chapter also includes the result interpretation which involved structural earthquake response and evaluation of the impact of ground structure on structural damage. The structural modelling and simulation were presented and comparison of structural response for ground structures of different soil type was assessed in this chapter.

Chapter 5 concludes the important findings together with recommendations for improvement for future study. List of references and appendices are attached in the last part of dissertation.

CHAPTER 2

LITERATURE REVIEW

2.1 Overview

This chapter discusses historical earthquake event in Sabah, local geological impact on structural damage, as well as the theory and application of microtremor observation technique for the estimation of shear wave velocity (Vs) profile and the ground structure. The analysis methods, namely Spatial Autocorrelation (SPAC) method and the Rayleigh wave ellipticity methods are discussed in this chapter. This chapter also discusses the simulation of ground surface motion, structural modelling, and reviews of previous work done for evaluation of the impact of ground structure on structural damage.

2.2 Historical Earthquake Events in Sabah

Located on the Sunda Plates near to the Ring of Fire, Sabah is known as the most tectonically active area in Malaysia. Philippine Sea Plate and Caroline Plate are moving westwards at a rate of about 10 cm per year colliding with the Eurasian Plate which is moving eastwards at rate of 4 cm per year. Additionally, the Indian-Australian Plate is moving northwards at a speed of 7 cm per year. Therefore, Sabah is still receiving the compression force from the interaction of three main tectonic plates as these plate boundaries are the most active and unstable. This tectonic setting of Sabah as shown in

Figure 2.1, makes it prone to seismic activities.

Figure 2.1: Tectonic setting of Sabah with major plate movements (Tongkul, 2017)

As compared with Peninsular Malaysia, Sabah is more susceptible to seismic activity due to its relative proximity to the plate boundaries. According USGS (2019), out of 64 earthquake events occurred in Malaysia, 58 of them occurred in Sabah since

1923 with magnitude more than 4.0. Figure 2.2 shows the historical earthquake events in

Sabah from 1923 up to present. The summary of the historical events is tabulated in

Appendix A.

On 5th June 2015, an earthquake of magnitude 6.0 hit Ranau, Sabah, recorded as the strongest earthquake event in most recent years for Malaysia. USGS (2015) classified this event as moderate to very strong perceived shaking and very light to moderate potential damage (USGS, 2015). The estimated peak ground acceleration and intensity map of the earthquake event in Sabah are shown in Figure 2.3 and Figure 2.4, respectively. The earthquake event caused seismic damages to buildings and infrastructures, especially at SMK Ranau and Hospital Ranau which suffered the most damages in the event (Tan, 2016). Thus, these two sites are of interested to this project.

According to Alih and Vafaei (2019), the main reasons that causing the seismic damages to the structures after the 2015 Sabah earthquake is due to structural factors and the

12 inappropriate reinforcement design. However, the geotechnical aspects were not considered in the study.

Figure 2.2: Historical earthquake events in Sabah since year 1923 until 2019 (USGS, 2019a)

Figure 2.3: Estimated peak ground acceleration and intensity map of 5th June 2015 Sabah earthquake (USGS, 2015)

Figure 2.4: Estimated intensity map in Ranau near to the epicenter on 5th June 2015 Sabah earthquake (USGS, 2015)

2.3 Geological Setting in Ranau

According to the JMG (2012), the Ranau district is located above sedimentary rocks, mainly consists of the Pleistocene and recent (clay, silt, sand and peat) and the

Palaeogene (argillaceous rocks, arenaceous and calcareous base) as shown in Figure 2.5.

According to Tan (2016), Ranau district has hilly landscape with hard rock base beneath.

However, the ground structure and the depth of hard rock base have yet to be determined due to insufficient soil investigation data.

Figure 2.5: Geological Map of Sabah (JMG, 2012)

2.4 Local Geological Condition Impact on Seismic Damage

In Japan, the 1923 Kanto earthquake caused spatial contrast of seismic damage level in Tokyo is clear evidence showing the impact of local geology to the seismic damage. The difference in local geological condition between hilly and coastal areas caused the contrast in seismic damage of the area had proved the control of seismic damages by the local geology (Horike, 1996).

Besides Japan, the destructive 1985 Mexico Michaocan earthquake event has also proven the control of seismic damages by the local geology. The Michaocan earthquake recorded a large magnitude of 8.1 and 7.5 occurred far away from Mexico City which is above the lacustrine clay of Lake zone. There was little damage at the area close to the epicentral area (20 km from the epicenter), recorded a low peak ground acceleration of

0.2g (Flores-Estrella et al., 2007). However, the peak ground acceleration was amplified as the seismic wave propagates to the ground beneath Mexico City, which was the Lake

Zone, even at 400km away from the epicenter. The peak ground acceleration recorded at

Mexico City exceeded the one at the epicentral area, causing extreme destruction to the city. The amplification was believed to be caused by the presence of the soft lacustrine clay under the Mexico City. The buildings with natural frequencies that coincide with the subsurface motion frequencies were encountered higher degree of structural damage

(Singh et al., 2015). The illustration of the site amplification of the 1985 Michaocan earthquake is shown in Figure 2.6.

Figure 2.6: Illustration of site amplification of 1985 Michaocan earthquake (Singh et al., 2015)

Other than Tokyo and Mexico City, there were many other earthquake events show the influence of local geology to the structural damage. Researchers thus are being finding convenient, simple, and low-cost tools for estimating substructure to investigate the local geology effect to seismic damage.

2.5 Microtremor Observation Technique

Microtremor is the ambient vibration of ground surface at seismic frequencies with low amplitude, due to sources other than earthquake, either by natural (ocean wave and variation in atmosphere) or artificial (human activity) sources. It is also known as the natural signals of the Earth surface, varying both spatially and temporally. The vibratory phenomena of microtremor are highly variable, complex, and irregular.

Microtremor sources are identified to be acting on the surface, where surface wave is considered as the dominant wave component in microtremor signals. Previous studies have shown that the use of microtremor can be divided into two groups, which are the estimation of site response to seismic motion and for geophysical exploration (Horike,

1996). In this project, the study is only focus on the geophysical exploration using microtremor.

Microtremor has been used worldwide as a geophysical exploration tool in microtremor observation technique or microtremor survey method. It is commonly used in seismic risk evaluation from its recording and analysis. Other than microtremor observation, there are many techniques such as gravity survey method, magnetic survey method, spontaneous potential (SP) measurement in electrical methods, magnetotelluric method in the electromagnetic methods, and radiometric measurements that used for geophysical exploration, which are known as “natural field methods.” (Okada, 2003), as well as the conventional exploration method.

The microtremor observation technique is the new exploration method developed by Okada (1990) for imaging subsurface structure using microtremors (Okada, 2003). It becomes one of the preferred approaches in subsurface estimation because of the advantages as compared to other exploration method. The advantages of the microtremor observation technique are its simple operation methods, which can be performed at any time and any place, convenient, low cost and most importantly it is a non-destructive measurement which does not require intensive environment precautions (Bard, 1999).

According to Horike (1996), the use of microtremors for the geophysical exploration can be classified into two methods, which are the phase velocity of surface wave (Aki, 1957, Horike, 1985) and the spectral ratios of horizontal to vertical components, H/V of Rayleigh waves (Nogoshi, 1971). These two methods are both capable to determine subsurface structure with assumption the spectra of microtremors observed only on the subsurface and both methods are controlled by the subsurface structure (Horike, 1996). The phase velocity of surface wave method can be obtained by using microtremor array measurement, while the spectral ratios of horizontal to vertical

18 components of Rayleigh wave method can be obtained by using microtremor single point measurement.

2.5.1 Phase Velocity and Dispersion Curve

Beginning with the phase velocity estimation of microtremors by Aki (1957), the exploration of subsurface structure was then firstly conducted by Toksöz (1964) using the phase velocities of microtremor (Horike, 1996). According to Okada (2003), the dispersion is a function of subsurface structure. Thus, theoretically, the subsurface structure can be estimated from the dispersion curve. The analysis of microtremor array measurement enables to obtain the dispersion curve of the surface wave by assuming that the ground structure at site is horizontally stratified and the microtremor is predominantly consists of surface wave (Okada, 2003).

From the microtremor data obtained from microtremor array measurement, analysis methods are required to give an acceptable quality dispersion curve to estimate a reliable shear wave velocity profile through inversion of dispersion curve. Since the microtremor consists mainly of Rayleigh wave in the vertical plane, the vertical component-array data of microtremor is sufficient to be used for analysis to estimate the shear wave velocity profile (Horike, 1996). The development of exploration method using phase velocity since Toksöz’s attempt leaded to improvement in substructure estimation and resulted in many proposed methods of analysis, such as SPAC method

(Aki, 1957), F-K method (Capon, 1969), and CCA method (Cho et al., 2004).

The idea of the subsurface structure estimation from phase velocity is explained by El-Eraki et al. (2012).The dominant wave component in microtremor which is the surface wave is generated by active sources energy imparted into the ground. The surface wave propagates at varying phase velocities can be analyzed and processed using the 1-

D multi-channel analysis of surface waves (MASW) method to infer the shear wave velocity profile. This can be done in two steps: the calculation of dispersion curve and the inversion of dispersion curve to a shear wave velocity profile. The obtained shear wave velocity profile is then used for estimating the thickness of sediments overlying the bedrock (El-Eraki et al., 2012).

Many researchers had used the phase velocity of microtremors in estimating shear wave velocity profile in their study such as Tokimatsu et al. (1992), Horike (1996),

Miyakoshi et al. (1997), Oppenheimer et al. (2000), Kawase et al. (2001a), Kawase et al.

(2001b), Estrella and Gonzalez (2003), Asten (2004), Ohrnberger et al. (2004), Arai

(2005), Okada (2006), Wathelet et al. (2008), Tarancıoğlu et al. (2010), Zaineh et al.

(2012), Chávez-García et al. (2014), Ridwan et al. (2015), Kuo et al. (2016) and (Teague et al., 2018). In this project, the microtremor data was analyzed to estimate the dispersion curve using SPAC method for the phase velocity of surface wave method.

2.5.2 Spectral Ratio of H/V of Rayleigh Wave

Same as the phase velocity of Rayleigh wave, the spectral ratio of horizontal to vertical components (H/V) of Rayleigh wave is also controlled only by subsurface structure. Thus, the spectral ratio as well can be used for geophysical exploration. The

H/V spectral ratio for exploration is first conducted by Boore and Toksöz (1969). The

H/V spectral ratio of Rayleigh wave at the surface, measured using single station microtremor sensor is used to be compared with the theoretical ellipticity curve correspond to the subsurface structure model estimated from the phase velocity of

Rayleigh wave (Boore and Toksöz, 1969). However, to use it alone, the H/V spectral ratio is difficult to measure the precision needed in the exploration and it is an estimation- based exploration. The application of H/V ellipticity is then further improved by Nogoshi

(1971), Nakamura (1989), Lermo and Chávez-García (1994) and Yamanaka et al. (1994) for the application of H/V ellipticity in geophysical exploration and earthquake measurement.

According to Tokimatsu and Miyadera (1992), the variation of frequency of H/V ratio is similar to that of Rayleigh wave for shear wave velocity profile. This suggests the availability of H/V ratio for subsurface structure estimation. Due to the stable characteristic in term of time of microtremors, further studies on the relationship between

H/V spectra ratio of single point microtremor and calculated ellipticity of fundamental

Rayleigh waves for substructure model was done by Yamanaka et al. (1994). The study by Arai and Tokimatsu (2000) has proven that the shear wave velocity profile can be estimated from the H/V ratio of microtremors using formulas proposed in their study based on inversion analysis.

There are also researchers who used the H/V ratio of Rayleigh wave for geophysical exploration such as Giardini et al. (2001), Fäh et al. (2003), Malischewsky and Scherbaum (2004), Arai (2005), Arai and Tokimatsu (2008), Hobiger et al. (2009),

Zaineh et al. (2012), Hobiger et al. (2013), Tanimoto et al. (2013), Piña-Flores et al.

(2016), Titi Anggono (2017), Molnar et al. (2017) and Tan et al. (2018).

2.6 SPAC and Rayleigh Wave Ellipticity Method

The microtremor observation technique is introduced for the estimation of shear wave velocity profile from microtremors. Many methods are proposed and modified to analyze and process the microtremor data, either for phase velocity and dispersion curve estimation of surface wave, or spectral ratio of H/V of Rayleigh wave. Table 2.1 shows the application of SPAC and Ellipticity of Rayleigh wave method in estimating shear wave velocity profile conducted in previous researches.

Table 2.1: Application of SPAC and H/V ellipticity of Rayleigh wave method in estimating shear wave velocity profile

Number of Author Site Target Method Sensors Apostolidis et al. (2004a) Thessaloniki SPAC & H/V 4 Maresca et al. (2006) Vesuvius Area, Italy SPAC 13 Morikawa et al. (2009) Hsinchu City, Taiwan SPAC 4 Kiyono et al. (2011) Padang, Indonesia SPAC & CCA NA Zaineh et al. (2012) Damascus City, Syria SPAC 7 Hamasaki et al. (2013) Penang Island, Malaysia SPAC 4 Morales et al. (2014) Coatzacoalcos, Veracruz SPAC & H/V 4 Thein et al. (2014) Palu, Indonesia H/V 1 North-western Anatolia, Asten et al. (2014) SPAC & H/V 4 Turkey Jirasakjamroonsri and Bangkok, Thailand SPAC & CCA 4 Poovarodomb (2015) Adelaide City, South Setiawan et al. (2016) SPAC 7 Australia Molnar et al. (2017) Kathmandu, Nepal H/V 1 Ridwan et al. (2017) Jakarta, Indonesia SPAC 4 Penang, Kuala Lumpur, Cheah and Lau (2018) SPAC & CCA 4 Malaysia Tan et al. (2018) Penang, Malaysia SPAC & H/V 4

2.6.1 Spatial Autocorrelation (SPAC) Method

The most popular and applicable method that have been used in estimating shear wave velocity profile is the Spatial Autocorrelation (SPAC) method. The estimation of

22 phase velocities from microtremor using SPAC method is based on the theory proposed by Aki (1957). According to Aki (1957), the wave phenomenon, including microtremors, is considered as stochastic wave which are stationary spatially and temporarily. The relationship between spatial and temporal spectra of complex wave was studied and the spatial autocorrelation was derived as the function of frequency, phase velocity and the distance between seismometer stations. The Spatial Autocorrelation (SPAC) coefficient is defined as azimuthal average of the coherence between the vertical component

(Rayleigh wave) records of pairs of sensors in the circular array as formula derived as shown in Equation 2.1. His study has led to the employment of phase velocity of microtremor to subsurface structure exploration.

1 2π ωr ρ(r,ω) = ∫ exp[ikr cos (θ-∅)]dθ = J0(kr) = J0 ( ) 2.1 2π 0 푐(ω) where,

ρ(r,ω) = SPAC coefficient

ω = angular frequency r = inter-station distance

θ = azimuth between two observation points

∅ = azimuth of incidence for incoming plane waves k = wavenumber

퐽0 = zero order Bessel function

SPAC method requires number of minimum four microtremors sensors arranged in a circular array, with one central microtremor sensor in the circular array. The use of

SPAC method can be found in numbers of previous studies, such as Kawase et al.

(2001a), Estrella and Gonzalez (2003), Apostolidis et al. (2004b), Ohrnberger et al.

(2004), Asten (2007), Claprood et al. (2011), Quispe et al. (2014), Setiawan et al. (2016),

Ridwan et al. (2017) and Tan et al. (2018).

One of the validation example for this method is done by Estrella and Gonzalez

(2003). Estrella and Gonzalez (2003) conducted a validation of SPAC method with the result obtained by Kagawa (1996) using F-K method at the Ciudad Universitaria, Mexico

City. They conducted the microtremor array measurement using four sensors, where three sensors in vertices of equilateral triangle with 1km sides and the fourth sensor at the center. The results using both methods were compared, and it showed the shear wave velocity structure estimated using SPAC method is consistent with F-K method. They concluded that the SPAC method can be used as a tool to estimate the shear wave velocity profile of a site as well as estimating the local site effect. The result of comparison is shown in Figure 2.7 and Figure 2.8. The shear wave velocity profile obtained from the inversion of dispersion curve using SPAC method is shown in Figure 2.8.

Figure 2.7: Comparison between the dispersion curve obtained using SPAC method (continuous line and points) and F-K method (dashed line) (Estrella and Gonzalez, 2003)

Figure 2.8: Shear wave velocity profile from inversion of dispersion curve using SPAC method (Estrella and Gonzalez, 2003)

Morikawa et al. (2009) applied SPAC method to model shear wave velocity profile in Hsinchu, Taiwan. Figure 2.9 shows shear wave velocity profiles at each site in

Hsinchu City, Taiwan. They found four to five layers sediment at nine sites around

Hsinchu City ranges from 500 m/s, 800 m/s, 1200 m/s and 2000 m/s and a very soft soil layer is presence with velocity of 250 m/s only at seashore area. The depth of bedrock is about 1000m below ground level with velocity of 3000 m/s.

Figure 2.9: Shear wave velocity profile for each site at Hsinchu, Taiwan (Morikawa et al., 2009)

2.6.2 Ellipticity of Rayleigh Wave

The ellipticity is defined as the ratio of the horizontal to vertical axis of the elliptic trajectory of particle motion, varies with period in the case of layered structures (Boore and Toksöz, 1969). According to Boore and Toksöz (1969), The ellipticity of the particle motion of Rayleigh wave at the surface enables the estimation of subsurface structure layers by comparing the theoretical ellipticity curve corresponds to the subsurface structure model with the H/V spectral ratio of Rayleigh wave measured using single station microtremor sensor, as shown in Figure 2.10. From their finding, the H/V spectra ratio of Rayleigh wave as a function of frequency can be used to estimate the subsurface structure by reasonably fitting the ellipticity curve estimated from shear wave velocity profile obtained using phase velocity with the H/V spectra ratio of Rayleigh wave. The

H/V spectral ratio was calculated as shown as Equation 2.2. (Tan et al., 2018).

√푆 2 + 푆 2 푛푠 푒푤 2.2 H/V(f) =

2 √2푆푢푑 where,

Sns = Fourier amplitude in horizontal north-south direction

Sew = Fourier amplitude in horizontal east-west direction

Sud = Fourier amplitude in vertical up-down direction

Figure 2.10: Comparison between measured H/V spectra ratio of Rayleigh wave at different sites with the theoretical ellipticity correspond to substructure model estimated from phase velocity (Boore and Toksöz, 1969)

Giardini et al. (2001) computed H/V ratio from microtremor measurement for site at Kreuzlingen, Switzerland and compared with H/V ratio obtained using other methods such as time-frequency analysis and modal summation technique as well as the ellipticity curve for the ground structure from previous research done by Maurer et al. (1999) as shown in Figure 2.11. The results in Figure 2.11 showed that the H/V ratios matched with the ellipticity curve and the ground structure model inversed from the ellipticity curve gave good agreement with the ground structure obtained from by Maurer et al. (1999).

Figure 2.11: (a) Comparison between H/V ratios of observed noise (thin lines), frequency–time analysis method (thin grey line), modal summation method (thick black line), with the ellipticity of the fundamental-mode Rayleigh wave for structure model (b) (thick grey line). (b) Structural model obtained from the inversion of the observed H/V ratios (thick black line) compared to the ground structure given by Maurer et al (1999) (thin grey line) (Giardini et al., 2001)

Morales et al. (2014) adopted SPAC method to obtain shear wave velocity profile from dispersion curve calculated from each array at seven sites in the city of

Coatzacoalcos, Veracruz. Subsequently, the H/V spectra ratio was calculated for each array. From the inversion process of the estimated shear wave velocity profile, the velocities may vary to obtain the ellipticity that is most suitable fitting with the measured

H/V spectra. A series of shear wave velocity profiles were employed to generate a 3D

28 shear wave velocity model, with exploration depth of approximately 100 meters, and a velocity range in between 206 m/s to 920 m/s. Figure 2.12 shows the 3D shear wave velocity model of the Coatzacoalcos City, Veracruz.

Figure 2.12: 3D shear wave velocity model of the Coatzacoalcos City, Veracruz. (Morales et al., 2014)

Tan et al. (2018) conducted microtremor array observation at Universiti Sains

Malaysia Engineering Campus in Penang, Malaysia. The microtremor data were then analysed using SPAC method to estimate the shear wave velocity profile structure and compared with borelog data. The horizontal to vertical (H/V) spectra at the center of the circular array was compared with the computed ellipticity of the fundamental mode of

Rayleigh wave to produce convincing estimation of ground structure. From his result, the shear wave velocity obtained using SPAC method had good agreement with the ground structure from borelog data as shown in Figure 2.13. The ellipticity curve of

Rayleigh wave of each site also match closely with the plot of H/V spectral ratios of the recorded microtremors, as shown in Figure 2.14. He concluded that the ellipticity curve match closely to the measured H/V ratio contributes to the validation of the soil profiles estimated from the SPAC method.

Site: SEC Shear Wave Velocity (m/s) 0 200 400 600 800 0

15 Depth (m) 20

25 0 10 20 30 40 50 Borelog N SPT

Site: PUM Shear Wave Velocity (m/s) 0 200 400 600 800 0

15 Depth (m) 20

25 0 10 20 30 40 50 Borelog N SPT

Figure 2.13: Shear wave velocity profiles of computed soil models and NSPT from borelog at PUM (top) and SEC (bottom) (Tan et al., 2018)

Site: PUM (Array Radius=1m) Site: PUM (Array Radius=10m) 100 100

10 10

H/V H/V

1 1

0.1 0.1 0.1 1 10 0.1 1 10 Frequency (Hz) Frequency (Hz)

(a) Site: SEC (Array Radius=1m) (b) Site: SEC (Array Radius = 10m) 100 100

10 10

H/V H/V

1 1

0.1 0.1 0.1 1 10 0.1 1 10 Frequency (Hz) Frequency (Hz)

Figure 2.14: H/V spectra ratio of microtremors of array radius (1 m (a) and 10 m (b)) and ellipticity curve of Rayleigh wave from respective ground structure models at SEC (top) and PUM (bottom) (Tan et al., 2018)

From the previous researches, the SPAC and ellipticity of Rayleigh wave techniques are validated and found capable to estimate the shear wave velocity profile as well as the subsurface structure.

2.7 Ground Response Analysis

As seismic waves travel from bedrock to the surface, the wave passes through soil layers, changes characteristics of the waves, such as amplitude and frequency content. This can result in amplification of accelerations at ground surface motion to structures causing large destruction, particularly when the resulting seismic wave frequency matches with natural frequency of the structures (Anbazhagan and Sitharam,

2009). Site specific ground response analysis is conducted to determine the effect of local soil conditions on amplification of seismic waves and estimating the ground surface response for design purposes. It can be used to predict ground surface motions for the development of design response spectra, to evaluate dynamic stresses and strains for evaluation of liquefaction hazards, and to determine the earthquake-induced forces that can lead to instability of earth and earth retaining structures (Govinda Raju et al., 2004).

Over the years, numbers of techniques have been developed for ground response analysis. Those techniques can be classified into three categories, which are one-, two- and three dimensional ground response analyses (Chang, 2016). Considering one dimensional ground response analysis in this study, The methods can be further grouped into three categories: linear analysis, equivalent linear analysis and non-linear analysis

(Govinda Raju et al., 2004). Several previous studies have been done on ground response analysis by researchers such as Satyam and Towhata (2016), Devdeep et al. (2017), Mase et al. (2018) and Tamari et al. (2019). One-dimensional equivalent-linear ground response analysis is employed in this research due to its ability to approximate the nonlinear response of soils under earthquake loading. The method is originally based on the lumped mass model of soil deposits resting on rigid base to which the seismic motions were applied (Tan, 2018), which is suitable to be used in this study.

Since the ground structure may influence the seismic wave vibration and thus vary the ground surface motion, the shear wave velocity profile corresponding to ground structure has to be identified to determine the ground surface motion for structural earthquake response analysis. The Equivalent-linear Earthquake Site Response Analysis of Layered Soil Deposit (EERA) software is then used for that purpose. EERA is a modern implementation of the well-known concepts of equivalent linear earthquake site response analysis. EERA's implementation takes full advantages of the dynamic array dimensioning and matrix operations in FORTRAN 90 and its input and output are fully integrated with the spreadsheet program Excel. EERA uses a finite difference formulation to solve wave propagation equations in the time domain. EERA is used to evaluate the soil profile and simulate equivalent-linear soil site response (Bardet et al.,

2000).

2.8 Eurocode 8 (EC8) and Malaysia National Annex (NA)

The European Standard EN 1998-1, Eurocode 8: Design of structures for earthquake resistance: General rules, seismic actions and rules for buildings, was prepared by Technical Committee Commission of European Community, CEN/TC 250

“Structural Eurocodes”, the secretariat of which is held by British Standard Institution

(BSI). CEN/TC 250 is responsible for all Structural Eurocodes. Eurocode 8 applies to the design and construction of buildings and civil engineering works in seismic regions.

Malaysia National Annex to MS EN 1998 Part 1: Design of Structures for Earthquake

Resistance is published in 2017 in conjunction to the implementation of seismic design in Malaysia. For Eurocode 8, there are seven ground types varied with A, B, C, D, E, S1 and S2 for soil sediments with depth less than or equal to 30 m. The ground classification is determined from average shear wave velocity (Vs,30), NSPT or cu. Whereas for Malaysia

National Annex, five ground types proposed are ground type A, B, C, D and E for soil deposit exceeding 30 m in depth. The ground classification of the sites is based on the site natural period. Table 2.2 and Table 2.3 show the ground type classification scheme of EC8 and NA, respectively.

According to EC8, Ground types A, B, C, D, E, S1, and S2 described by the stratigraphic profiles and parameters given in the ground type classification table, may be used to account for the influence of local ground conditions on the seismic action.

This may also be done by additionally taking into account the influence of deep geology on the seismic action. The average shear wave velocity Vs,30 should be computed in accordance with the Equation 2.3:

30 푉S,30 = ℎi 2.3 ∑푛 푖 푉i

where: hi = thickness of soil layer

Vi = shear-wave velocity (at a shear strain level of 10–5 or less) of the i-th

formation or layer, in a total of N, existing in the top 30 m

Table 2.2: Ground type classification (Table 3.1, MS EN 1998-1:2015)

Parameters Ground Description of stratigraphic N type profile V (m/s) SPT c (kPa) s,30 (blows/30cm) u Rock or other rock-like geological formation, including A > 800 - - at most 5 m of weaker material at the surface. Deposits of very dense sand, gravel, or very stiff clay, at least several tens of metres in B 360 – 800 > 50 > 250 thickness, characterised by a gradual increase of mechanical properties with depth. Deep deposits of dense or medium-dense sand, gravel or 70 - 250 C stiff clay with thickness from 180 – 360 15 - 50

several tens to many hundreds of metres. Deposits of loose-to-medium cohesionless soil (with or D without some soft cohesive < 180 < 15 < 70 layers), or of predominantly soft-to-firm cohesive soil. A soil profile consisting of a surface alluvium layer with Vs values of type C or D and E thickness varying between about 5 m and 20 m, underlain by stiffer material with Vs > 800 m/s Deposits consisting, or containing a layer at least 10 m < 100 S thick, of soft clays/silts with a - 10 – 20 1 (indicative) high plasticity index (PI > 40) and high water content Deposits of liquefiable soils, of sensitive clays, or any other soil S 2 profile not included in types A – E or S1

For MS EN 1998-1:2015 (Malaysia National Annex, 2017), the soil site is characterized by its small-strain site natural period (TS) of the soil layer down to the depth of much stiffer sediments or bedrock. For soil sediments of more than 30 m deep to bedrock, TS can be estimated using the formula as shown in Equation 2.4 and 2.5.

퐻S 푉S = 푑i 2.4 ∑푛 푖 푉S,i

4 x 퐻S 푇S = 2.5 푉푠

where:

Vs = weighted average shear wave velocity over the total thickness of soil

layers

Hs = total thickness of soil layers di = thickness of individual soil layer

Vs,i = shear wave velocity of individual soil layer

Ts = small strain site natural period

Table 2.3: Ground type classification scheme in accordance to site natural period for soil deposit exceeding 30 m in depth (Table A1, Malaysia NA to MS EN 1998-1:2015)

Ground type Description and range of Site Natural Period, TS (S)

A Rock site, or site with very thin sediments and TS < 0.15s

B A site not classified as Ground Type A, C, D or E

C A site with sediments of more than 30 m deep to bedrock and TS = 0.5 – 0.7 s

D A site with sediments of more than 30 m deep to bedrock and TS = 0.7 – 1.0 s

A site with sediments of more than 30 m deep to bedrock and TS = > 1.0 s, or E deposits consisting of at least 10 m thick of clays/silts with a high plasticity index (PI > 50)

2.9 Structural Earthquake Response Analysis

Structural earthquake response analysis involves determination of structural behaviour of a building using dynamic analysis. Dynamic analysis can be used to find dynamic displacements time history and modal analysis. Several studies were conducted on the structural response analysis such as Govinda Raju et al. (2004), Kohrangi et al.

(2016), Chang (2016), Afifuddin et al. (2017), Silva et al. (2018), Tan (2018), Agrawal and Khedikar (2018) and Yuan et al. (2019).

A case study on the ground response analysis in terms of settlement of soil deposit and soil amplification of a site close to Sabarmati river belt in Ahmedabad City during the earthquake in Bhuj, 2001 is presented by Govinda Raju et al. (2004). He demonstrated that the high degree of damage is due to the consequence of large amplification of shear waves by the thick sandy soil deposit which caused large accelerations to buildings. Close matching of the resulting wave frequencies with resonant frequencies of the high-rise buildings is one of the factors responsible for their collapse. Figure 2.15 shows the amplification ratio between surface and base motion simulated from computer program SHAKE 91 based on equivalent linear analysis. The variation of natural frequencies for the frame structure models of different storey heights and bays are shown in Figure 2.16.

Figure 2.15: Amplification between surface and base motion (Govinda Raju et al., 2004)

Figure 2.16: Variation of natural frequency with number of bays and storeys in (a) first mode (b) second mode (Govinda Raju et al., 2004)

Structural response of reinforced concrete moment-resisting frame buildings vary in height (1-8 storeys building) were analysed using ETABS software by Chang (2016).

The structural analysis was conducted on elastic response spectrum on different soil thickness (10 m, 20 m and 30 m) and soil types (soft, medium and hard). Structural response such as joint displacement, base shear and inter-storey drift were analysed and investigated. From his study, the five and six storey buildings experience largest joint displacement, base shear and inter-storey drift under the recorded ground motion. The structural response for building on soft soil showed the highest responses for 10 m and

20 m thick soil layers, followed by medium and hard. For 30 m thick soil, building up to six storeys on medium soil gives highest response. After six storey height, buildings on soft soil continue has larger response than others. Figure 2.17 to Figure 2.19 shows the structural responses of buildings on different soil thickness and soil types.

Figure 2.17: Base shear of different buildings on different soil layers thickness and soil types (Chang, 2016)

Figure 2.18: Base shear of different buildings on different soil layers thickness and soil types (Chang, 2016)

Figure 2.19: Storey shear drift of 6 storey building on different soil layers thickness and soil types (Chang, 2016)

Afifuddin et al. (2017) conducted structural earthquake response analysis using time history analysis method to observe the response of a hotel building in Banda Aceh,

Indonesia. Structural Earthquake Response Analysis 3D (STERA 3D) developed by

Professor Taiki Saito (2015) from Toyohashi University of Technology, Japan was used in their study. It is integrated computer software for the seismic analysis of steel and reinforced concrete buildings in three dimensional spaces. In this study, the seismic

40 loading was assigned using the time-history analysis which provide for linear or nonlinear evaluation of dynamic structural response under loading which may vary according to the specified time function.

Tan (2018) compared the structural responses of three building models (three storey, eight storey and fifteen storey) subjected to near-field and far-field ground surface accelerations on ground type C and E sites with the design response spectra from

Malaysia National Annex. The numerical simulation was applied and analysed in

ETABS software. The structural responses such as base reaction, maximum displacement and inter-storey drift were obtained. He found that three storeys building on ground type

C and E sites subjected to near-field ground motions gave larger responses as compared to far-field ground motions whereas for eight-storey building subjected to far-field ground motion has higher responses than that subjected to near-field ground motion. For

15-storey buildings, ground type E soil profile manipulated the responses of buildings instead of the ground motions. Malaysia National Annex gave higher prediction on responses of most of the buildings subjected to near-field and far-field ground motions, except for three-storey building that considering near-field and far-field ground motions and eight-storey building that considering far-field ground motion on ground type C site.

2.10 Summary

The 2015 Sabah Earthquake occurred due to its tectonic setting caused destructive damage to the Ranau district. It is believed that the destructive damage at

Ranau district may due to the local geological condition as the local geological condition can affect the structural damage during an earthquake event. Microtremor observation technique used to be the most preferred technique for determining the shear wave velocity profile over the decades due to its simplicity in both operation and analysis.

Basically, the determination of shear wave velocity profile can be classified into two methods, which are the phase velocity of surface and the spectra ratios of horizontal to vertical components, H/V of Rayleigh waves. Shear wave velocity profile can be obtained from the dispersion curve of Rayleigh wave estimated from microtremor array data by mean of inversion. SPAC method is used for the analysis. Many researches have been done to validate the SPAC method. SPAC method has high accuracy to estimate shear wave velocity profile with an array of minimum several ten meters. For the H/V spectra ratio of Rayleigh wave, the H/V spectra ratio is to be calculated using data from a single point microtremor sensor to be compared with the theoretical ellipticity of

Rayleigh wave of the subsurface structure. The thickness of each layer of the shear wave velocity profile correspond to the subsurface structure is to be adjusted so that it can best fit the ellipticity curve. The EERA software is used to evaluate ground structure and simulate the soil site response to determine the ground surface motion. The STERA 3D software is used for evaluation of dynamic structural response under loading which may vary according to the specified time function, for example the seismic load, by inputting the time history. Due to the insufficient data, the local geological condition at two sites in Ranau district is remaining unknown. The impact of ground structure on the structural damage is yet to be investigated. Thus, the shear wave velocity profiles of both sites have to be determined for the structural earthquake response analysis.

CHAPTER 3

METHODOLOGY

3.1 Overview

This chapter discusses the activities carried out in this research. The process in this research is divided into four stages, which are desk study, field work, data analysis and result interpretation. The flowchart in Figure 3.1 summarizes all the activities performed for this research.

3.1 Desk Study

3.1.1 Literature Search

Related literature about the impact of ground structure on structural damage, local site effect, microtremor observation techniques in subsurface exploration were first studied. Previous studies on the effect of ground structure to ground motion and structural damage was reviewed to understand the relationship among ground structure, ground motion, and structural response of building constructed on ground surface. Literature search was also done on the local site effect and local site amplification during earthquake motion in an earthquake event.

For the microtremor observation techniques, the SPAC method and the Rayleigh wave ellipticity method were adopted to estimate the VS profile in this research. Previous researches in validating and applying these two methods in VS profile estimation were also reviewed. The application programme used for data analysis method such as SPAC analysis program developed by Professor Hitoshi Morikawa, Tokyo Institute of

Technology for SPAC and Rayleigh wave ellipticity method were studied. Study on

43 simulation of ground surface motion of specific ground structure and structural modelling using EERA and STERA 3D software were done as well on application of the software.

Literature search Desk Study Determination of study area

Microtremor measurement Field work

Data analysis

SPAC Method Rayleigh Wave Ellipticity method

Dispersion curve H/V Spectrum

Data analysis Inversion

Estimation of Vs profile

Simulation for Ground Surface Motion Using EERA

Structural modelling for building using STERA 3D

Analysis of the structural earthquake response

Evaluation of the impact of Result interpretation ground structure on structural damage in the earthquake event

Figure 3.1: Flowchart of methodology

3.1.2 Study Area

The study area was determined based on study on ground motion characteristic by Tan (2016) in the 2015 Sabah Earthquake, where his study was carried out for 22 sites at Ranau. Among the 22 sites, only two sites were chosen for this study which are the

SMK Ranau and Hospital Ranau as shown in Figure 3.2. The summary of coordinate of the sites is tabulated in Table 3.1. Both sites experienced the 2015 Sabah Earthquake and suffered from seismic damages from the event. The study area was chosen due to undetermined ground structure at the site and so the impact of ground structure on the structural damage in an earthquake event. The building at each site was visited. The structural properties, such as the dimension and layout of structural element of the buildings were and drawn for the usage in modelling of the structures.

3.2 Field Work (Microtremor Measurement)

Microtremor instruments as shown in Figure 3.3 were used to estimate the ground structure of the study area. The microtremor instrument set includes velocity sensor, data logger, GPS sensor, power supply and LAN cable. The velocity sensor unit measured the velocity time history in two horizontal channels of North-South (NS) in the X-direction and East-West (EW) in the Y-direction and a vertical channel of Up-Down (UD) in the

Z-direction. Specification of velocity sensor is shown in Table 3.2. To ensure the reliability and accuracy of the measurement, measurement guidelines for single point microtremor survey proposed by SESAME (2004) in Table 3.3 was used as a reference.

Duration of measurement were about 20 to 30 minutes based on the guideline.

Figure 3.2: Aerial view of SMK Ranau and Hospital Ranau

Table 3.1: Summary of location of study site

Site ID Site Coordinate 1 SMK Ranau 05°58'31.6801", 116°40'27.6701" 2 Hospital Ranau 05°57'15.0401", 116°40'20.2998"

Figure 3.3: Microtremor instrument set

Table 3.2: Specification of velocity sensor

Dimension 104 (W) x 104 (H) x 104 (D) mm (Adjustable height) Weight About 1.5 kg Sensor Type Moveable Coil Measuring Composition 3 components (X, Y, Z) Predominant Frequency 1.9 Hz – 2.1 Hz Sensitivity Above 0.8 V/kine Damping Ratio 0.7 (When connected to an external 100 kW wiring) Shunt Impedance Built-in Environmental Condition -200C – + 550C Amplitude Measurement ±2 mm (Range of movement 5.4 mmpp) Range Moveable Mass 29 g – 40 g Coil Resistance 7000 W – 7700 W Waterproof Performance IP65 Protection Level

The site at SMK Ranau was chosen to conduct the microtremor array observation.

The observation points were selected based on suitability of the location considering criteria such as least noise, least human activities, away from traffic, flat terrain and accessible. A total of four microtremor sensors with array size of 5 m radius was used for the microtremor array measurement. The sensors used were ITK 2, ITK 3, ITK4 and

ITK5. A medium radius array was used due to the space limitation at the site. The illustration of the microtremor sensor arrangement for 5 m radius array is shown in Figure

3.4 and microtremor array measurement at the site is shown in Figure 3.5. The recorded data were analysed using SPAC method.

Table 3.3: Microtremor measurement guidelines (SESAME, 2004)

Type of Main Recommendation parameters Recommended minimum Minimum expected f [Hz] o recording duration [min] 0.2 30’ 0.5 20’ Recording duration 1 10’ 2 5’ 5 3’ 10 2’ • Set the sensor down directly on the ground, whenever possible In situ soil-sensor • Avoid setting the sensor on “soft grounds” (mud, coupling ploughed soil, tall grass, etc.), or soil saturated after rain

• Avoid plates from “Soft” materials such as foam rubber, cardboard, etc. Artificial soil-sensor • On steep slopes that do not allow correct sensor coupling levelling, install the sensor in a sand pile or in a container filled with sand.

• Avoid recording near structures such as buildings, trees, etc. in case of wind blowing (faster than approx. 5 m/s). It may strongly influence H/V results by introducing Nearby Structures some low frequencies in the curves • Avoid measuring above underground structures such as car parks, pipes, sewer lids, etc.

• Wind: Protect the sensor from the wind (faster than approx. 5 m/s). This only helps if there are no nearby structures. Weather Conditions • Rain: Avoid measurements under heavy rain. Slight rain has no noticeable influence. • Temperature: Check sensor and recorder manufacturer’s instruction. • Monochromatic sources: avoid measurements near construction machines, industrial machines, pumps, generators, etc. Disturbance • Transient: In case of transients (steps, cars, etc.), increase the recording duration to allow for enough windows for the analysis after transient removal.

Figure 3.4: Microtremor sensor arrangement for 5 m radius array

Figure 3.5: Microtremor array measurement at SMK Ranau

For the site of Hospital Ranau, due to space limitation, only single point microtremor measurement was carried out to obtain the H/V spectral ratio for comparison with the ellipticity curve of proposed shear wave velocity profile. The field measurement is shown in Figure 3.6.

Before conducting the field measurement, Huddle test was conducted for the microtremor sensors to be used in the measurement. The microtremor instruments were calibrated to ensure all the instruments are providing approximately equal in magnitude in term of velocity time history. The huddle test was carried out where the arrangement of the four velocity sensor units was placed at the same space, pointing towards the same direction, and the observed data were compared for before and after calibration.

Figure 3.6: Microtremor single point measurement at Hospital Ranau

The recorded data in by all set of microtremor instruments in three directions, namely X-direction, Y-direction and Z-direction, shows small variation in amplitude but similar trends time history data as shown in Appendix B. In order to ensure all sensors provide much closer magnitude of output, calibration factors for ITK 2, ITK 3, ITK 4 and ITK 6 were developed, taking ITK 5 as reference arbitrarily, to improve the results obtained from each microtremor sensors. The calibration chart to obtain the calibration factors for microtremor sensor ITK 2 in X-, Y- and Z-direction are showed in Figure 3.7 to Figure 3.9. The calibration chart before and after calibration for other microtremor sensors are shown in Appendix C. Table 3.4 shows the calibration factor required to be multiplied for data in three directions obtained from different microtremor instruments.

The calibrations of velocity time history of X-direction, Y-direction and Z- direction for all data sets were done by multiplying with the calibration factor. After the calibration, all the four sets of data extracted from each microtremor instruments showed smaller variation and almost similar magnitude with one another. The calibration graphs after calibration are plotted as shown in Figure 3.10 to Figure 3.12. The calibrated data is then used for further analysis.

Figure 3.7: Calibration factor of ITK 2 in X-direction

Figure 3.8: Calibration factor of ITK 2 in Y-direction

Figure 3.9: Calibration factor of ITK 2 in Z-direction

Table 3.4: Calibration Factor for Different Microtremor Sensors

Microtremor Instrument X-axis Y-axis Z-axis ITK2 0.8918 0.9083 1.0126 ITK3 0.9273 1.0284 1.0596 ITK4 0.8549 1.0055 1.0213 ITK5 1.0000 1.0000 1.0000 ITK6 1.0339 0.9450 1.1163

Figure 3.10: Calibration factor of ITK 2 in X-direction after calibration

Figure 3.11: Calibration factor of ITK 2 in Y-direction after calibration

Figure 3.12: Calibration factor of ITK 2 in Z-direction after calibration

3.3 Data Analysis

Microtremor sensors collected raw data from the field measurement and it was saved as raw format file. The data was first converted into .csv format file using ITK

Data View program then further converted into .txt format using MS Excel Macro. The data files in raw format file were then analysed using SPAC analysis program while the

.txt format file were analysed using Matlab Program for H/V spectral ratio. The H/V spectra ratio was calculated from the data obtained using program written in Matlab and compared with the ellipticity curve from the proposed shear wave velocity. The Matlab script is shown in Appendix D. The details of the analysis procedure are explained in the following subsections.

3.3.1 Spatial Autocorrelation (SPAC) Method

The general procedure of SPAC method analysis is showed in Figure 3.13.

Generally, SPAC method consists of three steps. First is the observation by the microtremor array measurement on ground surface. Next is the estimation of the

54 dispersion of the surface wave as a response to the subsurface structure directly below the array. Lastly, the subsurface structure causing the dispersion is estimated by means of inversion.

Figure 3.13: General procedure of SPAC method analysis

In this study, SPAC analysis program developed by Professor Hitoshi Morikawa,

Tokyo Institute of Technology was used to analyse the data using SPAC method. The analysis using SPAC analysis program involves four stages, which are the selection of signal segments, generation of dispersion curve, proposal of site ground structure and comparison of dispersion curve.

First, the raw format data files were copied into the SPAC analysis program. The recorded signal data were then converted to ascii format in the program. The signal data

55 were plotted as time-history and signal segments were selected from the time-history on

30 seconds basis for each sensor. From the signal segments selected, Fast Fourier

Transform was done for the time-history data and a Fourier result was plotted from each selected signal segment. The Fourier plot from all sensors were compared for each direction (East-West, North-South and Up-Down) and the Fourier plots which gives consistent plots among all sensors is considered as good plot. The consistent plots showed that the signal data were stable at that particular interval. Only the signal segment with the best Fourier plot was used to generate dispersion curve.

Before the generation of dispersion curve, the sensors position was inputted in form of coordinate. Taking ITK 3 as the origin, the location and respective coordinates of the microtremor sensors is shown in Figure 3.14. The selected segment was used for calculation and generation of the dispersion curve from the measurement data as shown in Figure 3.15. The dispersion curve ‘TRI_a.phv’ was due to autocorrelation between centre sensor and outer sensors while the dispersion curve ‘TRI_b.phv’ was due to the autocorrelation between outer sensors.

Next, the ground structure was proposed to match with the dispersion curve of measurement data. The ground structure was assumed to be four layers, consists of top- most soft layer, medium layer, hard layer and bedrock. The thickness and shear wave velocity of each soil layers were altered to give a theoretical dispersion curve that can match with the measured dispersion curve. The proposed ground structure which the theoretical dispersion curve matched with the measured dispersion curve was chosen as the estimated ground structure for ground response analysis. The layer depth boundary limit of each soil layer can be identified based on shear wave velocity of soil layer using proposed description as shown in Table 3.5 as suggested by Cheah (2018).

Table 3.5: Boundary limit for each layer (Cheah, 2018)

Layer NSPT (Nos of blow/0.3m) VS (m/s) (Proposed description)

Soft 0 – 15 0 – 180

Medium 15 – 50 180 – 360

Hard >50 360 – 800

Bedrock Bedrock encountered in borelog* > 800

*NSPT not available, assumed to be same with hard layer.

Figure 3.14: Arrangement of sensors and their coordinates for microtremor array measurement

Figure 3.15: Dispersion curve generated from microtremor array measurement

3.3.2 Ellipticity of Rayleigh Wave

The microtremor sensors data is used to generate the H/V spectral ratio using the formula given in Equation 2.2. Five H/V spectral ratio is generated from five time- interval segments chosen of measurement data and is plotted as shown in Figure 3.16.

The segment is selected based on the low noise or intensity in the measurement data. The estimated shear wave velocity profile evaluated from SPAC method is then used to generate the theoretical ellipticity of Rayleigh wave to compare with the measured H/V spectral ratio from the microtremor data. The ellipticity was used to validate and estimate a reliable shear wave velocity profile by fitting the H/V spectral ratio with the ellipticity of Rayleigh wave as shown in Figure 3.17.

Figure 3.16: H/V spectra ratio of five selected time intervals from measurement data

100.00

10.00 H/V

1.00 0.10 1.00 10.00

0.10

Frequency (Hz)

Figure 3.17: Ellipticity curve evaluated from the estimated shear wave velocity profile

3.4 Equivalent-linear Earthquake Response Analysis (EERA)

To simulate the 2015 Sabah earthquake event, the estimated shear wave velocity profile was used for modelling soil profile in Equivalent -linear Earthquake site Response

Analysis (EERA) software for ground response analysis. Table 3.6 shows the types of

59 worksheets in EERA and their contents. In this study, one-dimensional equivalent-linear ground response analysis was adopted based on several assumptions. First, all the boundaries between different geological materials were horizontal. Second, the ground and bedrock surface were assumed to extend infinitely in the horizontal direction. Lastly, the response of a soil deposit was predominantly caused by shear horizontal wave propagating vertically from the underlying bedrock (Bardet et al., 2000).

The time history of 2015 Sabah earthquake at KKM station was selected as the input bedrock acceleration time history for the analysis to determine the ground response as the station recorded the largest peak ground acceleration (PGA) during the event. The locations of seismic stations with acceleration recorded and distance with epicenter for

2015 Ranau Earthquake are shown in Figure 3.18. Besides the input time history, other input data such as soil types, unit weight, shear wave velocity and thickness of each soil layer which had been collected were defined for the generation of ground surface motion.

Table 3.6: Types of worksheets in EERA and their contents (Bardet et al., 2000)

Worksheets Contents Earthquake Earthquake input time history Material curves (G/G and Damping versus strain for Material max each material type) Profile Vertical profile of layers Iteration Results of main calculation Acceleration Time history of acceleration/velocity/displacement Strain Time history of stress and strain Amplification Ratio Amplification between two sub-layers Fourier Amplitude Fourier amplitude spectrum of acceleration Spectra Response spectra

Figure 3.18: Locations of seismic stations with acceleration recorded and distance with epicenter for 2015 Ranau Earthquake (Chang, 2016)

The estimated shear wave velocity profiles were used for ground surface motion response analysis using EERA. A modified shear wave velocity profile with same depth for each layer but different shear wave velocities for various ground types, namely ground types B, C and D, according to Eurocode 8 and MS EN 1998-1:2015 (Malaysia

National Annex, 2017) were analysed using the same time history for comparison of the ground surface motion. The ground type of the estimated ground structure at site is classified in accordance to Eurocode 8 and Malaysia National Annex 2017.

From EERA, the time history of acceleration at ground surface was attained for further comparison and discussion. Figure 3.19 shows the bedrock motion and the ground surface motion. The soil amplification factor is the calculated using Equation 3.1 for discussion.

0.2 Recorded motion 0.1 0 -0.1

Acceleration (g) Acceleration -0.2 0 10 20 30 40 Time (sec)

0.4 Ground surface motion 0.2

-0.2

-0.4 Acceleration (g) Acceleration 0 10 20 30 40 Time (sec)

Figure 3.19: Bedrock motion and ground surface motion

푀푎푥𝑖푚푢푚 푎푐푐푒푙푒푟푎푡𝑖표푛 푎푡 푠푢푟푓푎푐푒 퐴푚푝푙𝑖푓𝑖푐푎푡𝑖표푛 푓푎푐푡표푟 = 3.1 푀푎푥𝑖푚푢푚 푎푐푐푒푙푒푟푎푡𝑖표푛 푎푡 푏푒푑푟표푐푘

Using the EERA Excel spread sheet, the acceleration time history recorded from

KKM station during the 2015 Sabah earthquake main shock event was inputted in the

Earthquake worksheet in EERA. The input acceleration time histories were assumed to be the bedrock motion and the time history in three directions, namely East-West (EW),

North-South (NS), and Up-Down (UD) direction, are shown in Figure 3.20. Table 3.7 shows the absolute peak acceleration of three directions, which were illustrated as the red circle in Figure 3.20. The input peak acceleration in EW, NS and US are 0.1240 g,

0.1351 g and 0.0544 g, respectively.

0.2 0.2 0.1 0.1 0 0 -0.1 -0.1

-0.1240 -0.1351 Acceleration (g) Acceleration Acceleration (g) Acceleration -0.2 -0.2 0 10 20 30 0 10 20 30 Time (sec) Time (sec)

0.05

-0.05 -0.0544

-0.1 Acceleration (g) Acceleration 0 10 20 30 Time (sec)

Figure 3.20: Input acceleration time history in East-West (left top), North-South (right top) and Up-Down (bottom) direction

Table 3.7: Peak acceleration of ground motion in three direction

Direction Peak acceleration (g)

East-West (EW) 0.1240 North-South (NS) 0.1351 Up-Down (UD) 0.0544

The ground response analysis for estimated ground structure of both sites inputted in Profile worksheet results in respective output. The soil material type, total unit weight, location and type of earthquake are kept constant for both ground structure. The difference in output of both ground structure is due to the different in shear wave velocity and thickness of each soil layer which result in different site effect to the wave propagation.

3.5 Structural Earthquake Response Analysis 3D (STERA 3D)

The ground motion data generated from EERA for each shear wave velocity is used for the structural response analysis using Structural Earthquake Response Analysis

3D (STERA 3D) software. The structural modelling for the building at SMK Ranau and

Hospital Ranau were drawn and modelled in the software. The nonlinear time history analysis is done by inputting the generated ground motion data and the structural response of the building and its seismic performance were investigated.

Several assumptions were made for the structural earthquake response analysis.

All structural elements were modelled by line-elements with nonlinear springs except the floor diaphragm which can be modelled by a Finite Element Method (FEM) model. Beam elements were modelled as a line element with nonlinear flexural springs at both ends and a nonlinear shear spring at the middle. The degrading tri-linear slip model was used for the flexural hysteresis. The column elements was modelled in a similar manner, while nonlinear interaction between axial force and moment was expressed using axial springs of concrete and steel arranged in the sections at both ends (so called MS-model) and the nonlinear shear characteristics were modelled by the nonlinear shear springs (Takano and

Saito, 2017).

Figure 3.21 and Figure 3.22 show the plan view and 3D model of the building at

SMK Ranau and Hospital Ranau, respectively. Table 3.8 shows the list of material for structural members and their respective details. Due to limitation in STERA 3d for the number of masonry walls to be modelled, some of the masonry walls were replaced with reinforced concrete struts of size 125 mm x 373 mm. The parameters used for the building models are tabulated in Table 3.9 and representative section with detailed reinforcements of the structural members are shown in Figure 3.23. Both buildings were assumed to have the same reinforcement and material properties but with different

64 member size. The weights applied on each building are assumed to be 7000 kN and 4000 kN each floor for SMK Ranau and Hospital Ranau, respectively. The calculations for the weight on each floor of each building are shown in Appendix E.

Figure 3.21: Plan view (left) and 3D model (right) of building at SMK Ranau

Figure 3.22: Plan view (left) and 3D model (right) of building at Hospital Ranau

Figure 3.23: Representative section of column, beam and struts

Table 3.8: List of materials and details

Material Compressive strength /Yield strength (N/mm2) Concrete 28 Brick 20.5 Mortar 10 Steel Bar Main reinforcement: 460 Shear reinforcement: 250

Table 3.9: Parameters of building model at SMK Ranau and Hospital Ranau

Building Column Beam Masonry Total Storey Storey size size wall Floor Area level height (mm) (mm) thickness (m2) (mm) (mm) SMK 250 x 250 250 x 500 125 1800 4 3200 per Ranau 150 x 450 storey Hospital 300 x 300 250 x 625 125 1250 5 3000 per Ranau storey

From STERA 3D, the fundamental frequency and the mode shapes of vibration of both buildings are obtained. Non-linear static pushover analysis is conducted for both buildings to determine the structural performance of the buildings and to predict the possible damages to the buildings when subjected to lateral loads. The structural responses of building on different ground structures are presented in the form of

66 maximum response of the building, such as maximum drift, maximum displacement and maximum storey shear force in both directions for each storey of the building. The top building displacement and orbit of top building displacement also used to illustrate the displacement of the top floor of the building while the base shear-top drift result shows the relationship between top displacement and the base shear coefficient which is also ratio of base shear to total weight of building.

3.6 Result Interpretation

The structural earthquake response analyzed using STERA 3D was observed and investigated for each building with different shear wave velocity profiles. The structural responses due to the earthquake for each building on ground types B, C and D were compared. The impact of ground structure correspond to the shear wave velocity profile on the structural damage in the earthquake event was then evaluated from the investigation.

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Overview

This chapter discusses about the result obtained from microtremor observation, ground surface motion response and structural response of building at site. This chapter consists of three parts, which are ground structure estimated using SPAC and Rayleigh wave ellipticity methods, ground surface motion response for different soil profiles and the structural earthquake response of building, including storey drift, displacement of each floor and storey shear force.

4.2 Ground Structure Estimation

Microtremor array measurement was conducted at SMK Ranau for 5 m radius array. Due to space limitation, single microtremor measurement was conducted at

Hospital Ranau. The estimation of shear wave velocity profile is executed using SPAC and Rayleigh wave ellipticity methods. The soil layers and their respective shear wave velocity were proposed as stated in Table 3.5.

4.2.1 SMK Ranau Based on SPAC Method

For SMK Ranau, the theoretical dispersion curve of the proposed ground structure is used to estimate shear wave velocity profile at the site. The theoretical dispersion curve of the proposed ground structure is compared with the dispersion curve generated from microtremor measurement. Figure 4.1 shows the comparison of the theoretical and measured dispersion curves.

1 SPAC(center to outer) 0.9 SPAC(outer to outer) 0.8 Theoretical 0.7

0.6

0.5

0.4

Phase Velocity Phase Velocity (m/s) 0.3

0.2

0.1

0 0 1 2 3 4 5 6 7 8 9 10 Frequency (Hz)

Figure 4.1: Comparison of theoretical dispersion curve from estimated ground structure with measured dispersion curve

The dispersion curves from measurement data generated using SPAC method show two dispersion curves of similar trend but different value is because of the correlation between microtremors of sensors with separated distances. The SPAC (center to outer) stated in the figure indicates the correlation between microtremors of center sensor and the outer sensors while SPAC (outer to outer) indicates the correlation between microtremors of outer sensors of the array.

From many try and errors on the soil layer, the proposed ground structure as shown in Table 4.1 and Figure 4.2 produces the theoretical dispersion curve that matches well with the dispersion curves calculated from SPAC method based on the microtremor observation. The theoretical dispersion curve matches both dispersion curves generated from outer to outer sensors at frequency range of 6 – 7.5 Hz and center to outer sensors at frequency range of 7.5 to 10 Hz. Therefore, the proposed shear wave velocity profile is accepted as the ground structure at SMK Ranau.

Table 4.1: Estimated shear wave velocity profile of SMK Ranau analysed using SPAC method in numerical form

Layer Vs (m/s) Thickness (m) Soft 155 3.5

Medium 314 6.2

Hard 580 29

Engineering Bedrock 980 11.3

Vs (m/s) 0 200 400 600 800 1000 1200 0.0 Soft 5.0 Medium 10.0

15.0

20.0

25.0 Hard

Depth (m) Depth 30.0

35.0

40.0 Engineering 45.0 Bedrock 50.0

Figure 4.2: Estimated shear wave velocity profile of SMK Ranau

From the result SMK Ranau has a shallow depth of soft and medium layers with depth from 0 to 3.5 m at 155 m/s and 3.5 m to 9.7 m at 314 m/s, respectively. Beneath depth of 9.7 m to 38.7 m, the soil layer achieved shear wave velocity of 580 m/s which is in the range of shear wave velocity corresponding to the hard soil layer. For shear wave velocity more than 800 m/s, which is the soil layer at depth below 38.7 m, the soil layer is assumed to be engineering bedrock.

According to EC8, the average shear wave velocity Vs,30 of the soil profile is calculated using Equation 2.3 giving Vs,30 of 388.0 m/s. On the other hand, according to

MS EN 1998-1:2015 (National Annex 2017), the ground type of the ground structure at

SMK Ranau is determined from the weighted average shear wave velocity over the total thickness of soil layers, Vs and the small strain site natural period, Ts based on Equations

2.4 and 2.5. From the calculation, Vs of 481.4 m/s and Ts of 0.42 s are obtained. The calculated Vs considering soil deposit exceeding 30 m in depth is larger than Vs,30.

However, from both Table 2.2 and Table 2.3, the ground type of the estimated ground structure at SMK Ranau is classified as type B, which is classified as deposits of very dense sand, gravel, or very stiff clay, at least several tens of metres in thickness, characterised by a gradual increase of mechanical properties with depth as stated in

Eurocode 8 (MS EN 1998-1:2015). This result agrees with the site which is located on hilly area.

4.2.2 Hospital Ranau Based on Rayleigh Wave Ellipticity Curve Method

The ground structure for Hospital Ranau is estimated using Rayleigh wave ellipticity curve. The ellipticity curve of estimated ground structure is plotted and compared with the H/V spectral ratio of the measurement data as shown in Figure 4.3.

From many trial and errors on the soil layer, the proposed ground structure as shown in

Table 4.2 and Figure 4.4 produces the ellipticity curve that matches well with the peak of H/V spectral ratio.

100.00

10.00 H/V

1.00 0.10 1.00 10.00

0.10 Frequency (Hz)

Ellipticity curve set 1 set 2 set 3 set 4 set 5

Figure 4.3: Comparison of H/V spectral ratio of different time intervals and ellipticity curve

From Figure 4.3, the H/V spectral ratio from five time-intervals shows a predominant frequency at 2.5 Hz to 3.0 Hz. The ellipticity curve from the estimated ground structure matches with the H/V spectral ratio, with the peak close to the predominant frequency. Therefore, the proposed shear wave velocity profile is accepted as the ground structure for Hospital Ranau.

Table 4.2: Estimated shear wave velocity profile of Hospital Ranau analysed using Rayleigh wave ellipticity method in numerical form

Layer Vs (m/s) Thickness (m) Soft 80 5.7 Medium 285 4.6 Hard 562 28.8 Engineering Bedrock 911 10.9

Vs (m/s) 0 200 400 600 800 1000 1200 0.0 Soft 5.0 Medium 10.0

15.0

20.0

25.0 Hard

Depth (m) Depth 30.0

35.0

40.0 Engineering 45.0 Bedrock 50.0

Figure 4.4: Estimated shear wave velocity profile of Hospital Ranau

For Hospital Ranau, Vs,30 of the soil profile is 245.0 m/s, Vs and Ts are 332 m/s and 0.60 s respectively. Again, shear wave velocity considering soil deposit exceeding

30 m in depth gives higher value as compared to Vs,30. According to MS EN 1998-1:2015

(National Annex 2017), the ground type of the estimated ground structure at Hospital

Ranau is classified as type C, which is classified as deep deposits of dense or medium- dense sand, gravel or stiff clay with thickness from several tens to many hundreds of metres. The obtained result is consistent with the observation at site where Hospital

Ranau is located on valley area.

4.3 Ground Structure Response

4.3.1 Ground Surface Acceleration of Estimated Ground Structure

The output to represent the ground response are presented in ground surface acceleration time histories in three directions as shown in Figure 4.5 to Figure 4.7 using 73 the estimated ground structure at SMK Ranau and Figure 4.8 to Figure 4.10 for in the estimated ground structure at Hospital Ranau based on the peak ground acceleration time histories from 2015 Sabah earthquake record at KKM Seismic Station.

0.5 0.4 0.3 0.2 0.1 0 -0.1 0 5 10 15 20 25 30 35 40 -0.2 Acceleration (g) Acceleration -0.3 -0.2503 -0.4 -0.5 Time (s)

Figure 4.5: Ground surface acceleration in EW direction at SMK Ranau

0.5 0.4 0.3 0.2602 0.2 0.1 0 -0.1 0 5 10 15 20 25 30 35 40

-0.2 Acceleration (g) Acceleration -0.3 -0.4 -0.5 Time (s)

Figure 4.6: Ground surface acceleration in NS direction at SMK Ranau

0.5 0.4 0.3 0.2 0.1 0 -0.1 0 5 10 15 20 25 30 35 40

-0.2 Acceleration (g) Acceleration -0.3 -0.4 -0.3123 -0.5 Time (s)

Figure 4.7: Ground surface acceleration in UD direction at SMK Ranau

0.5 0.4 0.3331 0.3 0.2 0.1 0 -0.1 0 5 10 15 20 25 30 35 40

-0.2 Acceleration (g) Acceleration -0.3 -0.4 -0.5 Time (s)

Figure 4.8: Ground surface acceleration in EW direction at Hospital Ranau

0.5

0.3

0.1

-0.1 0 5 10 15 20 25 30 35 40

Acceleration (g) Acceleration -0.3 -0. 2932 -0.5 Time (s)

Figure 4.9: Ground surface acceleration in NS direction at Hospital Ranau

0.5 0.4 0.3 0.2 0.1 0 -0.1 0 5 10 15 20 25 30 35 40

-0.2 Acceleraion (g) Acceleraion -0.3 -0. 2646 -0.4 -0.5 Time (s)

Figure 4.10: Ground surface acceleration in UD direction at Hospital Ranau

From Figure 4.5 to Figure 4.10, it shows that the output peak accelerations at ground surface are amplified as compared to the input peak acceleration at bedrock in three-directions at both sites. This is due to the site effect of soil layers as the seismic wave propagates upwards from bedrock to ground surface. The difference in soil density causes the reflection and refraction of seismic wave, results in amplification of seismic wave as well as the ground motion due to multiple reflections in soft layer. For SMK

Ranau, the maximum acceleration at ground surface are recorded as 0.2503 g, 0.2602 g and 0.3123 g with respect to EW, NS and UD directions. Whereas for Hospital Ranau, the maximum ground surface accelerations are 0.3331 g (EW), 0.2923 g (NS), and

0.2646 g (UD)

4.3.2 Ground Surface Acceleration of Modified Ground Structure

In order to study the effect of ground classification on structural damage, the ground structures with various ground types classification according to Eurocode 8 and

Malaysia Annex were then determined by altering the shear wave velocity of each layer while maintaining the soil layer thickness of the estimated ground structure from each

76 site. The ground types that are used for analysis are ground types B, C and D. The summary of the estimated ground structures and modified ground structures for both

SMK Ranau and Hospital Ranau are tabulated in Table 4.3 and Table 4.4, repectively.

Table 4.3: Estimated ground structure and modified ground structure with various ground type classification for SMK Ranau

Estimated ground structure Modified ground structure (Ground type B) Ground type C Ground type D Thickness (m) Vs (m/s) Vs (m/s) Vs (m/s) 3.5 155 100 65 6.2 314 180 135 29.0 580 360 280 11.0 980 980 800 Vs (m/s) 481.4 309.5 229.9 Ts (s) 0.42 0.65 0.86 Vs,30 (m/s) 388.0 238.4 174.1

Table 4.4: Estimated ground structure and modified ground structure with various ground type classification for Hospital Ranau

Estimated ground structure Modified ground structure (Ground type C) Ground type B Ground type D Thickness (m) Vs (m/s) Vs (m/s) Vs (m/s) 5.7 80 175 50 4.6 285 355 175 28.8 562 660 355 10.9 911 950 800 Vs (m/s) 332.0 496.8 212.7 Ts (s) 0.60 0.40 0.94 Vs,30 (m/s) 245.0 398.0 153.2

The Vs,30 of the modified ground structure for SMK Ranau is 238.4 m/s and 174.1 m/s for ground types C and D, respectively, while Vs and Ts are 309.5 m/s and 0.65 s for ground type C and 229.9 m/s and 0.86 s for ground type D. Whereas for Hospital Ranau, the Vs,30, Vs and Ts of ground type B are 398.0 m/s, 496.8 m/s and 0.40 s while for ground type D are 153.2 m/s, 212.7 m/s and 0.94 s, respectively. The details of ground surface

77 motions from ground response analysis for the modified ground structures of SMK

Ranau and Hospital Ranau are shown Appendix F.

4.3.3 Peak Acceleration and Amplification Factor

Table 4.5 and Table 4.6 show the summary of the input and output peak acceleration in three directions and the amplification factor for estimated and modified ground structures in SMK Ranau and Hospital Ranau, respectively. The peak accelerations were amplified in both estimated ground structure and modified ground structure at both sites. Generally, the output peak accelerations show increase in amplification for both sites when the ground structures change from ground type B to D.

For SMK Ranau, the ground structure is modified from ground type B to type C and D.

It shows amplification in three directions from the input acceleration. For ground type B, the peak acceleration in engineering bedrock is amplified from 0.1240 g (EW), 0.1351 g

(NS) and 0.0544 g (UD) to 0.2503 g (EW), 0.2602 g (NS) and 0.3116 g (UD) with amplification factor of 2.0, 1.9, and 5.7, respectively. The peak acceleration is amplified from the same input peak acceleration to 0.3576 g (EW), 0.3557 g (NS) and 0.3495 g

(UD) with amplification factor of 3.2, 2.6, and 6.4, respectively for ground type C, and

0.4417 g (EW), 0.4766 g (NS) and 0.3562 g (UD) with amplification factor of 3.6, 3.5 and 6.5, respectively for ground type D. It is expected that more severe damage could had been happened to SMK Ranau if the building is located on ground types C or D.

Table 4.5: Summary of peak accelerations and amplification factors for various ground structures at SMK Ranau

Peak acceleration (g) Direction Output Input Ground type B Ground type C Ground type D EW 0.1240 0.2503 0.3957 0.4417 NS 0.1351 0.2602 0.3557 0.4766 UD 0.0544 0.3116 0.3495 0.3562 Direction Amplification factor Ground type B Ground type C Ground type D EW 2.0 3.2 3.6 NS 1.9 2.6 3.5 UD 5.7 6.4 6.5

Table 4.6: Summary of peak accelerations and amplification factors for various ground structures at Hospital Ranau

Peak acceleration (g) Direction Output Input Ground type B Ground type C Ground type D EW 0.1240 0.2918 0.3331 0.3510 NS 0.1351 0.2864 0.2932 0.3089 UD 0.0544 0.2986 0.2646 0.2734 Direction Amplification factor Ground type B Ground type C Ground type D EW 2.4 2.7 2.8 NS 2.1 2.2 2.3 UD 5.5 4.9 5.0

Whereas for Hospital Ranau, the ground structure was modified from ground type

C to type B and D. Ground type C shows amplification of peak acceleration from input data to 0.3331 g (EW), 0.2932 g (NS) and 0.2646 g (UD) with amplification factor of

2.7, 2.2, and 4.9, respectively. For ground type D, the peak accelerations are amplified slightly from input data to 0.3510 g (EW), 0.3089 g (NS) and 0.2734 g (UD) with amplification factor of 2.8, 2.3, and 5.0, respectively. The ground surface motions at ground type C and ground type D shows almost similar peak acceleration. However, for ground type B, the peak acceleration amplification reduces from the estimated type C

79 ground structure in EW and NS direction but increases in UD direction. The peak accelerations for ground type B record 0.2918 g (EW), 0.2864 g (NS) and 0.2986 g (UD) with amplification factor of 2.4, 2.1 and 5.5, respectively. Although ground type B gives greater peak acceleration amplification in UD direction than ground type C, the acceleration time history shows that ground type B have overall lower acceleration as compared ground type C. Hence, the damage of Hospital Ranau is influenced by the site amplification of ground motion.

Generally, both sites show increment in amplification of ground motion when the ground structures are changed from ground types B to D due to the site effect of the soil layers. Softer soil layer tends to amplify the bedrock ground motion and so result in larger amplification at the ground surface. From the amplification factor result, both sites show greater amplification factor in vertical direction than that of horizontal direction.

4.4 Structural Earthquake Response

Building models as shown in Section 3.5 were modelled and analysed using

STERA 3D software. Modal analysis and non-linear time history analysis were carried out to determine the structural response. The ground surface accelerations at both sites were applied to respective building in STERA 3D software to study the structural response such as maximum storey drift, maximum storey displacement, maximum storey shear force and the base shear-top drift. The direction of ground motion in EW, NS and

UD direction are corresponded to the direction of building in X-, Y- and Z-direction, respectively.

4.4.1 Mode Shape and Natural Frequency

The modal analysis is conducted using STERA 3D software for both buildings at

SMK Ranau and Hospital Ranau. The first three vibration mode shapes are of interest because they contribute most of the total deformation in lateral deformation. The result of vibration mode shapes in X- and Y-axis and their respective natural frequencies for SMK Ranau and Hospital Ranau are shown from Figure 4.11 to Figure 4.14, respectively.

First Mode Shape Second Mode Shape Third Mode Shape (Frequency = 2.05 Hz) (Frequency = 6.27 Hz) (Frequency = 11.00 Hz) 4 4 4

3 3 3

2 2 2

Floor

Floor Floor

1 1 1

0 0 0 0 1 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 Relative Mode Shape Relative Mode Shape Relative Mode Shape

Figure 4.11: First three vibration mode shapes for building in SMK Ranau in X-axis

First Mode Shape Second Mode Shape Third Mode Shape (Frequency = 2.12 Hz) (Frequency = 7.41 Hz) (Frequency = 15.67 Hz) 4 4 4

3 3 3

2 2 2

Floor Floor Floor

1 1 1

0 0 0 0 1 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 Relative Mode Shape Relative Mode Shape Relative Mode Shape

Figure 4.12: First three vibration mode shapes for building in SMK Ranau in Y-axis

First Mode Shape Second Mode Shape Third Mode Shape (Frequency = 3.88 Hz) (Frequency = 12.50 Hz) (Frequency = 29.59 Hz) 5 5 5

4 4 4

3 3 3

Floor Floor Floor 2 2 2

1 1 1

0 0 0 0 1 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 Relative Mode Shape Relative Mode Shape Relative Mode Shape

Figure 4.13: First three vibration mode shapes for building in Hospital Ranau in X-axis

First Mode Shape Second Mode Shape Third Mode Shape (Frequency = 3.43 Hz) (Frequency = 11.26 Hz) (Frequency = 23.70 Hz) 5 5 5

4 4 4

3 3 3

Floor Floor Floor 2 2 2

1 1 1

0 0 0 0 1 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 Relative Mode Shape Relative Mode Shape Relative Mode Shape

Figure 4.14: First three vibration mode shapes for building in Hospital Ranau in Y-axis

The first vibration mode shape, also known as the fundamental vibration mode shape of the building, shows the lowest frequency of vibration. The natural frequencies increase as the vibration mode shape increases. The summary of the frequency for each mode shape for buildings at SMK Ranau and Hospital Ranau are tabulated in Table 4.7.

The results show that the building of SMK Ranau is stiffer in Y-axis while the building of Hospital Ranau is stiffer in X-axis.

Table 4.7: Summary of natural frequencies of first three mode shapes for building at SMK Ranau and Hospital Ranau

Frequency (Hz) Vibration Mode Shape SMK Ranau Hospital Ranau X-axis Y-axis X-axis Y-axis First 2.05 2.12 3.88 3.43 Second 6.27 7.41 12.50 11.26 Third 11.00 15.67 29.59 23.70

4.4.2 Non-linear Static Pushover Analysis

The relationship between storey drift and storey shear coefficient can be obtained through the non-linear static pushover analysis. The lateral resistances of the buildings are evaluated from the pushover analysis. Figure 4.15 and Figure 4.16 show the relationships between shear force and story drift angle in each story obtained in the X- and Y-axis for SMK Ranau and Hospital Ranau, respectively. The storey shear coefficients (Qi/W) are showed in Figure 4.15 and Figure 4.16. The drift-shear relationship of building is demonstrated on the buildings in SMK Ranau and Hospital

Ranau subjected to uniform lateral load in X-direction. The damages on buildings in

SMK Ranau and Hospital Ranau corresponding to different storey drift angle are demonstrated in Figure 4.17 and Figure 4.18. The results showing damages on both buildings in Y-direction are shown in Appendix G.

0.30 0.30 0.20 0.19 0.20 0.20

0.10 0.10

0.00 0.00 0.00 0.01 0.02 0.00 0.01 0.02

Storey Drift Angle Storey Drift Angle

Storey shear coefficient shear Storey coefficient Storey Storey shear coefficient Floor 4 Floor 3 Floor 4 Floor 3 Floor 2 Floor 1 Floor 2 Floor 1

(a) X-direction (b) Y-direction

Figure 4.15: Relationships between shear force and story drift angle for building in SMK Ranau

0.50 0.50 0.39 0.40 0.40 0.31 0.30 0.30 0.20 0.20 0.10 0.10 0.00 0.00 0.00 0.01 0.02 0.00 0.01 0.02 Storey Drift Angle

Storey Storey shear coefficient Storey Drift Angle Storey Storey shear coefficient

Floor 5 Floor 4 Floor 3 Floor 5 Floor 4 Floor 3 Floor 2 Floor 1 Floor 2 Floor 1

(a) X-direction (b) Y-direction

Figure 4.16: Relationships between shear force and story drift angle for building in Hospital Ranau

The base shear coefficients were obtained from the plot. The base shear coefficient is defined as the ratio of base shear to the weight of building. From the result, the base shear coefficients for SMK Ranau are 0.20 (X-axis) and 0.19 (Y-axis) for drift ratio of 0.02. Whereas for Hospital Ranau, the base shear coefficients are 0.39 (X-axis) and 0.31 (Y-axis). Moderate or severe damages can occur when the earthquake vibration exceeds the base shear coefficient of the buildings.

(a) Moderate damages on masonry walls and columns

(b) Severe and moderate damages on masonry walls and columns

Figure 4.17: Damages corresponding to drift angles on building in SMK Ranau subjected to uniform lateral load in X-direction (arrow)

(a) Moderate damages on masonry walls and columns

(a) Severe and moderate damages on masonry walls and columns

Figure 4.18: D damages corresponding to drift angles on building in Hospital Ranau subjected to uniform lateral load in X-direction (arrow)

In the diagrams, the yellow and red dots indicate the moderate (1 < ductility factor

< 5) and severe damages (ductility > 5) on elements, respectively. From the analysis, the buildings experiences first moderate damage on walls at ground level when the drift angle passes 1/400 or 0.0025. As the drift angle increases, the number of damaged elements increases. The damages are concentrated on walls and joints between beams and columns at ground level due to soft storey effect. For building in SMK Ranau, as the drift angle increases close to 1/50, the walls experience severe damages and lastly the column joints are subjected to severe damage as well. The damages become more severe and propagate to elements on upper levels as the drift angle increases. Whereas for building in Hospital Ranau, the severe damages on walls at ground floor induces damage to walls at upper levels when drift angle increases to 1/50. All the walls and joints at ground level suffer severe damages as the drift angle increases to more than 1/20. The damage propagation predicted by the analysis shows similarity with the damage observed during 2015 Sabah earthquake.

4.4.3 Maximum Storey Drift

From the non-linear time history analysis, the result of maximum storey drift for both building is obtained. The maximum storey drift in both X- and Y-axis on various ground types for building in SMK Ranau and Hospital Ranau are shown in Figure 4.19 and Figure 4.20, respectively.

2 Floor

0 0.00 0.50 1.00 1.50 2.00 Maximum Drift (cm)

Ground B Ground C Ground D

(a) X-axis

2 Floor

0 0.00 0.50 1.00 1.50 2.00 Maximum Drift (cm)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.19: Maximum storey drift in X- and Y-axis on ground types B, C and D for building in SMK Ranau

Floor 2

0 0.00 0.50 1.00 1.50 2.00 2.50 Maximum Drift (cm)

Ground B Ground C Ground D

(a) X-axis

Floor 2

0 0.00 0.50 1.00 1.50 2.00 Maximum Drift (cm)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.20: Maximum storey drift in X- and Y-axis on ground types B, C and D for building in Hospital Ranau

From the results, all the ground structures at both sites show the maximum drift in X- and Y-axis at the first level. The deformations are concentrated at the first storey due to the soft storey effect. In SMK Ranau, the maximum storey drift in both X- and Y- axis at the first floor of the building records the highest value on ground type D with 1.87

89 cm (X-axis) and 1.55 cm (Y-axis), followed by ground type C with 1.35 cm (X-axis) and

1.13 cm (Y-axis). Ground type B shows the smallest maximum drift of 0.76 cm and 0.64 cm in X- and Y-axis, respectively. This shows that the softer ground type (ground type

D) has greater effect on structural response. However, in Hospital Ranau, the building on ground type C shows a greater maximum storey drift in X-axis than the lower ground type (ground type D) site. Ground type C has the highest maximum storey drift of 2.08 cm in X-axis while Ground type D has the maximum storey drift of 1.70 cm in Y-axis,

Ground type B records the smallest maximum drift of 0.82 cm (X-axis) and 0.75 cm (Y- axis).

According to EC8 Clause 4.4.3.2, considering the earthquake event has higher return period, the limitation of inter-storey drift for buildings having non-structural elements of brittle materials attached to the structure is defined as 0.005*h, where h is the inter-storey height. The limitation of inter-storey drift was calculated for SMK Ranau and Hospital Ranau, as 1.6 cm and 1.5 cm, respectively. From the result, the maximum inter-storey drift for building at SMK Ranau which is located on ground type B is within the limitation. However, maximum inter-storey drift for building at Hospital Ranau located on ground type C has exceeded the limitation at the ground floor. This shows that

Hospital Ranau experienced more damages as compared to SMK Ranau during the earthquake event which is consistent with the actual event on 5th June 2015.

Generally, the maximum storey drift occurs at the first floor of both buildings on all ground types and decreases as the floor level goes higher. The deformations are concentrated at the first storey for both buildings which is consistent as the actual damage observed in 2015 Sabah earthquake. This is due to the buildings have soft first storey as there are lack of wall elements at ground floor. The results give good agreement with the actual situation where both buildings experienced damages mainly at ground storey

90 during the 2015 Sabah earthquake. The maximum storey drift increases as the ground type changes from ground type B to D at SMK Ranau. This is because the greater tendency of softer ground to amplify the ground surface motions that results in higher maximum drift. The actual damage at SMK Ranau which is located on ground type B is considered as minor structural damage because it records smaller maximum drift angle of 0.0024 or 1/421 as compared to the result from pushover analysis in Figure 4.17. The damage can be severe if the building is located on ground type C and type D.

In Hospital Ranau, the building on ground type C shows the greatest maximum storey drift in X-axis than the ground type D site while ground type D shows the greatest maximum storey drift in Y-axis. Hospital Ranau which is located on ground type C is considered to have moderate damage with maximum drift angle of 0.0069 or 1/144 in X- axis as the pushover analysis result in Figure 4.18 showing moderate damages on ground level for drift angle in the range from 1/400 to 1/200. Both buildings show consistent results as the actual condition occurred in 2015 Sabah earthquake.

4.4.4 Maximum Displacement

The maximum storey displacement increases as the floor level goes higher.

Figure 4.21 and Figure 4.22 present the maximum storey displacement for building in

SMK Ranau and Hospital Ranau, respectively on various ground structures. From the result, the maximum building displacement occurs at the top level of both buildings on all ground structures and the overall maximum storey displacement increases from ground type B to type D. The maximum storey displacement increases as the floor level goes higher.

4 4

3 3

2 2

Floor Floor

1 1

0 0 0.00 1.00 2.00 3.00 4.00 0.00 1.00 2.00 3.00 Displacement (cm) Displacement (cm)

Ground B Ground C Ground B Ground C Ground D Ground D

(a) X-axis (b) Y-axis

Figure 4.21: Maximum storey displacement in X- and Y-axis on ground types B, C and D for building in SMK Ranau

5 5

4 4

3 3

Floor Floor 2 2

1 1

0 0 0.00 1.00 2.00 3.00 0.00 1.00 2.00 3.00 Displacement (cm) Displacement (cm)

Ground B Ground C Ground B Ground C Ground D Ground D

(a) X-axis (b) Y-axis

Figure 4.22: Maximum storey displacement in X- and Y-axis on ground types B, C and D for building in Hospital Ranau

Building in SMK Ranau shows that the ground type D gives the largest maximum displacement of building at the top floor level, followed by ground types C and B, in both axes. The maximum building displacement increases from ground type B (1.46 cm and

1.07 cm in X- and Y-axis, respectively) to 2.26 cm (X-axis) and 1.67 cm (Y-axis) for ground type C and 2.87 cm (X-axis) and 2.31 cm (Y-axis) for ground type D. In actual situation, SMK Ranau which is located on ground type B has experienced small building displacement during the earthquake event. The displacement would increase if it is located on softer ground type (ground types C and D).

For Hospital Ranau, ground type C shows largest maximum storey displacement in X-axis while ground type D shows largest maximum storey displacement in Y-axis.

Ground type B shows the least maximum storey displacement in both X- and Y-axis. The maximum building displacements for ground type B are 1.39 cm and 1.16 cm in X- and

Y-axis, respectively. These values increase to 2.75 cm (X-axis) and 1.95 cm (Y-axis) for ground type C. Ground type D gives the maximum building displacement of 2.30 cm (X- axis) and 2.14 cm (Y-axis). Hospital Ranau which is located on ground type C has experienced the largest maximum displacement in X-axis and moderate displacement in

Y-axis in the past earthquake event and has resulted in moderate structural damages.

Both buildings have the largest maximum storey displacement at the top floor level for all ground types. Since the top floor level gives the largest maximum storey displacement, the top building displacement for each building on three ground types are plotted with time in Figure 4.23 and Figure 4.24 for SMK Ranau and Hospital Ranau, respectively to represent the building displacement. The orbit of top displacement illustrating the vibration and top displacement of buildings at both sites for each ground type are shown in Figure 4.25 and Figure 4.26. The orbit of top displacement shows the result where the top displacement of building increases from ground type B to type D for

SMK Ranau in both axes. For Hospital Ranau, the top displacement increases from ground type B to type D in y-axis. However, for x-axis, the top displacement at ground type C is the greatest, followed by ground type D and type B.

4.00

3.00

2.00

1.00

0.00 -2 3 8 13 18 23 28 33 38 -1.00

-2.00 Top buildng Topbuildng displacement (cm)

-3.00 Time (s) Ground B Ground C Ground D

(a) X-axis

3.00 2.50 2.00 1.50 1.00 0.50 0.00 -0.50-2 3 8 13 18 23 28 33 38 -1.00

Top buildng Topbuildng displacement (cm) -1.50 -2.00 Time (s)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.23: Top building displacement in X- and Y-axis on ground types B, C and D for building in SMK Ranau

3.00

2.00

1.00

0.00 0 5 10 15 20 25 30 35 -1.00

-2.00

Top buildng Topbuildng displacement (cm) -3.00

-4.00 Time (s)

Ground B Ground C Ground D

(a) X-axis

2.50 2.00 1.50 1.00 0.50 0.00 0 5 10 15 20 25 30 35 -0.50 -1.00

-1.50 Top buildng Topbuildng displacement (cm) -2.00 -2.50 Time (s)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.24: Top building displacement in X- and Y-axis on ground types B, C and D for building in Hospital Ranau

2.50 2.50

2.00 2.00

1.50 1.50

1.00 1.00

0.50 0.50

direction(cm) direction(cm)

- 0.00 0.00

- X -3.00 -1.00 1.00 3.00 -3.00X -1.00 1.00 3.00 -0.50 -0.50

-1.00 -1.00

-1.50 -1.50

-2.00 -2.00

-2.50 -2.50 Y-direction(cm) Y-direction(cm)

(a) Ground type B (b) Ground type C

2.50

2.00

1.50

1.00

0.50 direction(cm)

- 0.00 X -3.00 -1.00 1.00 3.00 -0.50

-1.00

-1.50

-2.00

-2.50 Y-direction(cm)

Figure 4.25: Orbit of top displacement of building in SMK Ranau on ground types B, C and D

2.50 2.50

2.00 2.00

1.50 1.50

1.00 1.00

0.50 0.50

direction(cm)

direction(cm) -

- 0.00 0.00 X X -3.00 -1.00 1.00 3.00 -3.00 -1.00 1.00 3.00 -0.50 -0.50

-1.00 -1.00

-1.50 -1.50

-2.00 -2.00

-2.50 -2.50 Y-direction(cm) Y-direction(cm)

(a) Ground type B (b) Ground type C

2.50

2.00

1.50

1.00

0.50 direction(cm) - 0.00 X -3.00 -1.00 1.00 3.00 -0.50

-1.00

-1.50

-2.00

-2.50 Y-direction(cm)

Figure 4.26: Orbit of top displacement of building in Hospital Ranau on ground types B, C and D

4.4.5 Maximum Storey Shear Force

Figure 4.27 and Figure 4.28 show the maximum storey shear force of the buildings in SMK Ranau and Hospital Ranau on three ground types. Generally, the maximum shear force in both directions of the buildings occurs at the ground floor which is the base shear of the building.

2 Floor

0 0 1,000 2,000 3,000 4,000 5,000 Maximum shear force (kN) Ground B Ground C Ground D

(a) X-axis

2 Floor

0 0 1,000 2,000 3,000 4,000 5,000 Maximum shear force (kN)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.27: Maximum storey shear force of the buildings in SMK Ranau on ground types B, C and D

Floor 2

0 0 1000 2000 3000 4000 5000 6000 7000 8000 Maximum shear force (kN)

Ground B Ground C Ground D

(a) X-axis

Floor 2

0 0 1000 2000 3000 4000 5000 6000 Maximum shear force (kN)

Ground B Ground C Ground D

(b) Y-axis

Figure 4.28: Maximum storey shear force of the buildings in Hospital Ranau on ground types B, C and D

For SMK Ranau, the maximum storey shear force increases from ground type B to ground type D for every floor level. The maximum base shear of building are on ground type D, with shear force of 4473 kN (X-axis) and 4350 kN (Y-axis), followed by ground type C with shear force of 4008 kN (X-axis) and 3563 kN (Y-axis), and ground type B with shear force of 3309 kN (X-axis) and 2747 kN (Y-axis). This shows that

99 building on softer ground type has a larger base reaction due to the larger drift at the building base.

While for Hospital Ranau, ground type D shows largest overall maximum storey shear force in both axis, followed by ground types D and B at the ground floor. Same as

SMK Ranau, the base shear has maximum shear force as compared to other storey shear forces. The maximum base shear of building are on ground type D with shear force of

7356 kN (X-axis) and 5527 kN (Y-axis), followed by ground type C with shear force of

7334 kN (X-axis) and 5419 kN (Y-axis), and ground type B with shear force of 7033 kN (X-axis) and 4708 kN(Y-axis).

Generally, the storey shear force decreases at floor level goes higher, causing decrease in maximum storey drift. The higher shear force at ground floor is due to the ground vibration which results in lateral movement of the building. In both SMK Ranau and Hospital Ranau, the maximum base shear increases from ground type B to type D.

This is because the ground vibration is amplified as the ground types change from ground type B to type D.

4.4.6 Base Shear-Top Drift

The base shear-top drift of the buildings at various ground structures are demonstrated in Figure 4.29 and Figure 4.30 to investigate the relationship between top displacement and the base shear coefficient. The base shear coefficient is necessary for earthquake resistance building design. The base shear coefficients for buildings at SMK

Ranau and Hospital Ranau is taken as the maximum base shear coefficient in the base shear-top drift graph and it is compared with the base shear coefficients obtained from non-linear static pushover analysis. The summary of the base shear coefficient is tabulated in Table 4.8.

100

0.50 0.50

0.40 0.40

0.30 0.30

0.20 0.20

0.10 0.10

0.00 0.00 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30

-0.10 -0.10 Top Drift TopDrift (cm)

-0.20 -0.20 Top(cm) drift -0.30 -0.30

-0.40 -0.40

-0.50 -0.50 Base shear/ weight Base shear / Weight

X-direction Y-direction X-direction Y-direction

(a) Ground type B (b) Ground type C

0.50 0.40 0.30 0.20 0.10 0.00 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30

TopDrift -0.10 -0.20 -0.30 -0.40 -0.50 Base shear / Weight

X-direction Y-direction

Figure 4.29: Relationship of top drift and base shear for building at SMK Ranau on ground types B, C and D

101

0.50 0.50

0.40 0.40

0.30 0.30

0.20 0.20

0.10 0.10

0.00 0.00 -0.30 -0.10 0.10 0.30 -0.30 -0.10 0.10 0.30

-0.10 -0.10

Top(cm) drift Top(cm) drift -0.20 -0.20

-0.30 -0.30

-0.40 -0.40

-0.50 -0.50 Base shear/ weight Base shear/ weight

X-direction Y-direction X-direction Y-direction

(a) Ground type B (b) Ground type C

0.50 0.40 0.30 0.20 0.10 0.00 -0.30 -0.10 0.10 0.30

-0.10 Top(cm) drift -0.20 -0.30 -0.40 -0.50 Base shear/ weight

X-direction Y-direction

Figure 4.30: Relationship of top drift and base shear for building at Hospital Ranau on ground types B, C and D

102

Table 4.8: Summary of base shear coefficient for ground types B, C and D in SMK Ranau and Hospital Ranau

Base shear coefficient SMK Ranau Hospital Ranau Ground type X-axis Y-axis X-axis Y-axis

B 0.12 0.10 0.35 0.24 C 0.14 0.13 0.37 0.27 D 0.16 0.15 0.37 0.28

Generally, base shear coefficient increases from ground type B to type D. For

SMK Ranau, ground type B records the least base shear coefficient of 0.12 and 0.10 in

X- and Y-axis, respectively, followed by ground type C with 0.14 (X-axis) and 0.13 (Y- axis). The largest base shear coefficient for SMK Ranau is on ground type D of 0.16 and

0.15 in X- and Y-axis, respectively. While for Hospital Ranau, the base shear coefficient increases when ground type B changes to ground type C. Ground type B records base shear coefficient of 0.35 in X-axis and 0.24 in Y-axis. It increases to 0.37 (X-axis) and

0.27 (Y-axis) on ground type C. Ground types C and D shows similar base shear coefficient due to small difference in base shear and top drift between ground types C and D.

From the non-linear static pushover analysis, the base shear coefficient limits before the building exceeds elastic limit are 0.20 (X-axis) and 0.19 (Y-axis) for building at SMK Ranau and 0.39 (X-axis) and 0.31 (Y-axis) for building at Hospital Ranau. Table

4.9 shows the percentage difference between base shear coefficient obtained from dynamic analysis and that of non-linear static pushover analysis.

103

Table 4.9: Percentage difference of base shear coefficient from dynamic analysis and non-linear static pushover analysis

Building Base shear coefficient Pushover analysis Dynamic analysis Percentage difference (%) X-axis Y-axis X-axis Y-axis X-axis Y-axis SMK Ranau (Ground 0.20 0.19 0.12 0.10 40.0 47.4 type B)

Hospital Ranau (Ground 0.39 0.31 0.37 0.27 5.1 12.9 type C)

From the table, SMK Ranau shows large percentage difference between base shear coefficients from two analyses in both axes. This shows that the base coefficient for SMK Ranau is far from the elastic limit and the building is only subjected to minor damage from the analysis. For Hospital Ranau, the result form dynamic analysis shows base shear coefficient is close to that of non-linear static pushover analysis. This shows that the ground floor of the building at Hospital Ranau has experienced moderate damages where the structure nearly achieves its elastic limit. In the past earthquake in

2015, both SMK Ranau and Hospital Ranau which are on ground type B and type C, respectively, experienced minor to moderate structural damages, without severe damages or collapse. The result of the study is consistent with the actual situation.

104

CHAPTER 5

CONCLUSION AND RECOMMENDATION

5.1 Conclusions

The study is carried out to determine the detailed ground structure at SMK Ranau and Hospital Ranau using microtremor observation technique and to investigate the impact of ground structure on structural response of buildings in at SMK Ranau and

Hospital Ranau in the 2015 Sabah earthquake.

Microtremor single point and array measurement with radius 5 m were conducted in Hospital Ranau and SMK Ranau, respectively. The detailed ground structures were estimated by using Spatial Autocorrelation (SPAC) method for microtremor array measurement in SMK Ranau and Rayleigh wave ellipticity method for single point measurement in Hospital Ranau. The ground type was determined and result shows that

SMK Ranau is located on ground type B while Hospital Ranau is located on ground type

The estimated ground structures were modified to obtain other ground types by changing the shear wave velocity while maintaining the thickness of each soil layers.

Ground responses at both SMK Ranau and Hospital Ranau on these three ground types were analyzed using the acceleration time history from KKM station recorded in 2015

Sabah Earthquake. The ground surface response of each ground type was obtained using

Equivalent-linear Earthquake Response Analysis (EERA). The peak ground surface acceleration and their respective amplification factor were compared. From the result, both sites show increment in amplification of ground motion when the ground structures are changed from ground type B to type D due to the site effect of the soil layers. This shows that softer soil layer tends to amplify the bedrock ground motion and so result in

105 larger amplification at the ground surface. Moreover, the amplification factor for both sites increases as well as ground structure changes from harder to softer ground. Except for ground type B in Hospital Ranau, it shows the largest peak ground surface acceleration in UD-direction as compared to ground type C and type D. However, the acceleration time history show that ground type B has overall lower acceleration as compared ground type C.

For the structural response of building in SMK Ranau and Hospital Ranau, the maximum inter-storey drift, maximum storey displacement, maximum storey shear force and the base shear-top drift were used to investigate the structural response of building on different ground structure. Generally, for SMK Ranau, ground type D gives the largest responses as compared to ground type C and ground type B give the least responses. Due to SMK Ranau is located on ground type B, it is expected that more severe damage could happen if the ground type is C or D. However, for Hospital Ranau, ground type C shows the largest responses in maximum storey drift, maximum storey shear force and gives the largest base shear coefficient in X-axis, followed by ground types D and B. For the structural response in Y-axis, it follows the trend as in SMK Ranau, where the response increases from ground type B to type C and the largest response is on ground type D.

The damage of Hospital Ranau is influenced by the site amplification of ground motion.

The structural responses simulated using STERA 3D software show strong similarity with the observed damages of the buildings at SMK Ranau and Hospital Ranau during

5th June 2015 Sabah earthquake.

106

5.2 Recommendations

The following recommendations are proposed to improve the study in the future:

i. Latest geotechnical reports geotechnical reports and borehole data are

required to enhance the evaluation of estimated ground structure at site.

ii. Further study for the effects of soil amplification on structures can

consider the effect of soil profiles with different site classes including

Classes A, B, C, D and E and different ground motions.

iii. More information and details are required for building modelling to

represent the actual building and give more accurate structural response.

iv. Comparison between building of larger difference in height or natural

frequency can be done for the structural response analysis to investigate

the impact of ground response to buildings of different natural

frequencies.

107

REFERENCE

Afifuddin, M., Panjaitan, M. & Ayuna, D. 2017. The behaviour of reinforced concrete structure due to earthquake load using time history analysis method. IOP Conference Series: Earth and Environmental Science, 56 (1), 012013.

Agrawal, C. P. & Khedikar, A. R. 2018. Linear and Nonlinear Dynamic Analysis of RC Structure for Various Plan Configurations Using ETABS. i-Manager's Journal on Structural Engineering, 7 (2), 70-76.

Aki, K. 1957. Space and time spectra of stationary stochastic waves, with special reference to microtremors. Bull. Earth. Res. Inst., 35, 415-456.

Alih, S. C. & Vafaei, M. 2019. Performance of reinforced concrete buildings and wooden structures during the 2015 Mw 6.0 Sabah earthquake in Malaysia. Engineering Failure Analysis, 102, 351-368.

Anbazhagan, P. & Sitharam, T. 2009. Estimation of ground response parameters and comparison with field measurements. Indian Geotechnical Journal, 39 (3), 245-270.

Apostolidis, P., Raptakis, D. & Pitilakis, K. 2004a. The use of microtremors for the definition of soil properties and bedrock depth in an urban area. Proceedings of the 13th World Conference on Earthquake Engineering (WCEE). August 1-6, Vancouver, Canada.

Apostolidis, P., Raptakis, D., Roumelioti, Z. & Pitilakis, K. 2004b. Determination of S- wave velocity structure using microtremors and SPAC method applied in Thessaloniki (Greece). Soil Dynamics and Earthquake Engineering.

Arai, H. 2005. S-wave velocity profiling by joint inversion of microtremor dispersion curve and horizontal-to-vertical (H/V) spectrum. Bulletin of the Seismological Society of America, 95 (5), 1766-1778.

Arai, H. & Tokimatsu, K. 2000. Effects of Rayleigh and Love waves on microtremor H/V spectra. Proceedings of the 12th World Conference on Earthquake Engineering. New Zealand National Society for Earthquake Engineering Inc. Jan 31 - Feb 4, Auckland, New Zealand.

Arai, H. & Tokimatsu, K. 2004. S-wave velocity profiling by inversion of microtremor H/V spectrum. Bulletin of the Seismological Society of America, 94 (1), 53-63.

Arai, H. & Tokimatsu, K. 2008. Three-dimensional Vs profiling using microtremors in Kushiro, Japan. Earthquake Engineering & Structural Dynamics, 37 (6), 845-859.

Asten, M. W. 2004. Optimised array design for microtremor array studies applied to site classification ; observations, results and future use. Proceedings of the 13th World Conference on Earthquake Engineering. August 1-6, Vancouver, Canada.

108

Asten, M. W. 2007. Shear velocity profiles obtained from microtremor array data with an example from direct fitting of SPAC curves. Symposium on the Application of Geophysics to Engineering and Environmental Problems 2007.

Asten, M. W., Askan, A., Ekincioglu, E. E., Sisman, F. N. & Ugurhan, B. 2014. Site characterisation in north-western Turkey based on SPAC and HVSR analysis of microtremor noise. Exploration Geophysics, 45 (2), 74-85.

Bard, P. Y. 1999. Microtremor measurement: A tool for site effect estimation? Proceedings of the 2nd International Symposium on the Effects of Surface Geology on Seismic Motion. December 1-3, Yokohama, Japan.

Bardet, J., Ichii, K. & Lin, C. 2000. User’s manual for EERA: a computer program for equivalent-linear earthquake site response analysis. Los Angeles, California, USA: University of Southern California.

Boore, D. M. & Toksöz, N. M. 1969. Rayleigh wave particle motion and crustal structure. Bulletin of the Seismological Society of America, 59 (1), 331-346.

Capon, J. 1969. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 57 (8), 1408-1418.

Chang, W. H. 2016. Seismic response analysis for reinforced concrete moment-resisting frame buildings in Sabah, Malaysia. Msc. Dissertation, Universiti Sains Malaysia.

Chávez-García, F. J., Manakou, M. V. & Raptakis, D. G. 2014. Subsoil structure and site effects: A comparison between results from SPAC and HVSR in sites of complex geology. Soil Dynamics and Earthquake Engineering, 57, 133-142.

Cheah, Y. B. & Lau, T. L. 2018. Introducing a new method for ground structure estimation using microtremor array observation technique. E3S Web Conf., 65, 06007.

Cho, I., Tada, T. & Shinozaki, Y. 2004. A new method to determine phase velocities of Rayleigh waves from microseisms. Geophysics, 69 (6), 1535-1551.

Claprood, M., Asten, M. W. & Kristek, J. 2011. Using the SPAC microtremor method to identify 2D effects and evaluate 1D shear-wave velocity profile in valleys using the SPAC microtremor method to identify 2D effects and evaluate 1D shear-wave velocity profile. Bulletin of the Seismological Society of America, 101 (2), 826-847.

Devdeep, B., Arindam, D. & Shiv Shankar, K. 2017. One-dimensional effective stress non-masing nonlinear ground response analysis of IIT Guwahati. International Journal of Geotechnical Earthquake Engineering (IJGEE), 8 (1), 1-27.

El-Eraki, M., Mohamed, A., Ali, A., Toni, M. & Shimaa, I. M. 2012. Engineering seismological studies in and around Zagazig city, Sharkia, Egypt. NRIAG Journal of Astronomy and Geophysics, 1 (2), 141–151.

109

Encyclopædia Britannica. 2019. Volcanoes and thermal fields that have been active during the past 10,000 years [Online]. Encyclopædia Britannica. Available: https://www.britannica.com/place/Ring-of-Fire/images-videos/media/118426/3265 [Accessed].

Estrella, H. F. & Gonzalez, J. A. 2003. SPAC: An alternative method to estimate earthquake site effects in Mexico City. Geofisica Internacional 42 (2), 227-236.

Fäh, D., Kind, F. & Giardini, D. 2003. Inversion of local S-wave velocity structures from average H/V ratios, and their use for the estimation of site-effects. Journal of Seismology, 7 (4), 449-467.

Flores-Estrella, H., Yussim, S. & Lomnitz, C. 2007. Seismic response of the Mexico city basin: a review of twenty years of research. Natural Hazards, 40 (2), 357-372.

Giardini, D., Fäh, D. & Kind, F. 2001. A theoretical investigation of average H/V ratios. Geophysical Journal International, 145 (2), 535-549.

Govinda Raju, L., Ramana, G., Hanumantha Rao, C. & Sitharam, T. 2004. Site-specific ground response analysis. Current Science, 1354-1362.

Hamasaki, S., Lau, T. L., Morikawa, H. & Ogura, Y. 2013. Estimation of ground structure using microtremor observation in Penang island, Malaysia. Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)), 69.

Hobiger, M., Bard, P. Y., Cornou, C. & Le Bihan, N. 2009. Single station determination of Rayleigh wave ellipticity by using the random decrement technique (RayDec). Geophysical Research Letters, 36 (14).

Hobiger, M., Cornou, C., Wathelet, M., Giulio, G. D., Knapmeyer-Endrun, B., Renalier, F., Bard, P. Y., Savvaidis, A., Hailemikael, S., Le, B. N., Ohrnberger, M. & Theodoulidis, N. 2013. Ground structure imaging by inversions of Rayleigh wave ellipticity: sensitivity analysis and application to European strong-motion sites. Geophysical Journal International, 192 (1), 207-229.

Horike, M. 1985. Inversion of phase velocity of long-period microtremors to the s-wave- velocity structure down to the basement in urbanized areas. Journal of Physics of the Earth, 33 (2), 59-96.

Horike, M. 1996. Geophysical exploration using microtremor measurement. Proceedings of the 11th World Conference on Earthquake Engineering. June 23-28, Acapulco, Mexico.

Jirasakjamroonsri, A. & Poovarodomb, N. 2015. Investigation of long period amplifications in the Greater Bangkok basin by microtremor observations. Proceedings of the 10th Pacific Conference on Earthquake Engineering. November 6-8, Sydney, Australia.

110

JMG. 2012. Geological map of Sabah, Malaysia [Online]. Available: https://www.jmg.gov.my/add_on/mt/sbh/tiles/ [Accessed].

Kawase, H., Huang, H.-C., Irikura, K., Higashi, S., Iwata, T., Sato, T. & Satoh, T. 2001a. S-wave velocity structure of the Taichung basin, Taiwan, estimated from array and single-station records of microtremors. Bulletin of the Seismological Society of America, 91 (5), 1267-1282.

Kawase, H., Matsushima, S. i. & Satoh, T. 2001b. Estimation of s-wave velocity structures in and around the Sendai basin, Japan, using array records of microtremors. Bulletin of the Seismological Society of America, 91 (2), 206-218.

Kiyono, J., Ono, Y., Sato, A., Noguchi, T. & Putra, R. R. 2011. Estimation of subsurface structure based on microtremor observations at Padang, Indonesia. ASEAN Engineering Journal, 1 (3), 66-81.

Kohrangi, M., Bazzurro, P. & Vamvatsikos, D. 2016. Vector and scalar IMs in structural response estimation, part II: building demand assessment. Earthquake Spectra, 32 (3), 1525-1543.

Kuo, C.-H., Chen, C.-T., Lin, C.-M., Wen, K.-L., Huang, J.-Y. & Chang, S.-C. 2016. S- wave velocity structure and site effect parameters derived from microtremor arrays in the Western Plain of Taiwan. Journal of Asian Earth Sciences, 128, 27-41.

Lermo, J. & Chávez-García, F. J. 1994. Are microtremors useful in site response evaluation? Bulletin of the Seismological Society of America, 84 (5), 1350-1364.

Lim, Y. S. 2016. Determination of dynamic characteristics of damaged buildings in the 2015 Sabah earthquake using microtremor observation. Undergraduate Dissertation, Universiti Sains Malaysia.

Malaysia National Annex 2017. Malaysia national annex to eurocode 8: design of structures for earthquake resistance- part 1: general rules, seismic actions and rules for building, Malaysia NA to MS EN 1998-1:2015. Department of Standard Malaysia.

Malaysia Standard 2015. Malaysia standard eurocode 8: design of structures for earthquake resistance- part 1: general rules, seismic actions and rules for building, MS EN 1998-1:2015, Malaysia Standard. Department of Standard Malaysia.

Malischewsky, P. G. & Scherbaum, F. 2004. Love’s formula and H/V-ratio (ellipticity) of Rayleigh waves. Wave Motion, 40 (1), 57-67.

Maresca, R., Nardone, L., Galluzzo, D., La Rocca, M. & Del Pezzo, E. 2006. Application of the SPAC method to ambient noise recorded in the Vesuvius area (Italy). 3rd International Symposium on the Effects of Surface Geology on Seismic Motion. August 30 - September 1, Grenoble, France.

Mase, L. Z., Likitlersuang, S. & Tobita, T. 2018. Analysis of seismic ground response caused during strong earthquake in Northern Thailand. Soil Dynamics and Earthquake Engineering, 114, 113-126.

111

Miyakoshi, K., Kagawa, T., Akazawa, T., Ogawa, Y., Shimizu, K. & Ejiri, J. 1997. Estimation of underground structures in Kyoto city by seismic-array observations of microtremors. Proceedings of the 97th Society of Exploration Geophysicists of Japan (SEGJ) Annual Fall Meeting. October 22-24, Sapporo, Japan.

Molnar, S., Onwuemeka, J. & Adhikari, S. R. 2017. Rapid post-earthquake microtremor measurements for site amplification and shear wave velocity profiling in Kathmandu, Nepal. Earthquake Spectra, 33 (S1), S55-S72.

Morales, L., Aguirre, J., Vazquez Rosas, R., Suarez, G., Contreras Ruiz-Esparza, M. & Farraz, I. 2014. Application of the H/V and SPAC method to estimate a 3D shear wave velocity model, in the city of Coatzacoalcos, Veracruz. AGU Fall Meeting Abstracts.

Morikawa, H., Komazawa, M., Chen, H., Takahashi, C., Shosaka, T. & Kawatsure, H. A model of ground structure to estimate earthquake ground motions around Hsinchu city, Taiwan on the basis of gravity and microtremor surveys. Proceeding of the 14th World Conference on Earthquake Engineering, 2009. October 12-17, Beijing, China.

Nakamura, Y. 1989. A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. QR Railway Tech. Res. Inst., 30 (1), 25-33.

Nogoshi, M. 1971. On the amplitude characteristics of microtremor, Part II. Journal of the seismological society of Japan, 24, 26-40.

Ohrnberger, M., Schissele, E., Cornou, C., Bonnefoy-Claudet, S., Wathelet, M., Savvaidis, A., Scherbaum, F. & Jongmanns, D. 2004. Frequency wavenumber and spatial autocorrelation methods for dispersion curve determination from ambient vibration recordings. 13th World Conference on Earthquake Engineering (WCEE) August 1-6, Vancouver, Canada.

Okada, H. 1990. An exploration technique using long-period microtremors for determination of deep geological structures under urbanized areas. Butsuri Tansa, 43, 402-417.

Okada, H. 2003. The microseismic survey method : society of exploration geophysicists of Japan. Geophysical Monograph Series, 12.

Okada, H. 2006. Theory of efficient array observations of microtremors with special reference to the SPAC method. Exploration Geophysics, 37 (1), 73-85.

Oppenheimer, D. H., Boore, D. M., Liu, H.-P., Hamilton, J. C., Brown, L. T., Warrick, R. E., Zhang, W. & Joyner, W. B. 2000. Comparison of phase velocities from array measurements of rayleigh waves associated with microtremor and results calculated from borehole shear-wave velocity profiles. Bulletin of the Seismological Society of America, 90 (3), 666-678.

Piña-Flores, J., Perton, M., García-Jerez, A., Carmona, E., Luzón, F., Molina Villegas, J. & J Sánchez-Sesma, F. 2016. The inversion of spectral ratio H/V in a layered system using the Diffuse Field Assumption (DFA). Geophysical Journal International, 208 (1), 577-588.

112

Quispe, S., Chimoto, K. & Yamanaka, H. 2014. Estimation of s-wave velocity profiles at Lima city, Peru using microtremor arrays (special issue on enhancement of earthquake and tsunami disaster mitigation technology in peru(2)). Journal of disaster research, 9 (6), 931-938.

Ridwan, M., Widiyantoro, S., Afnimar & Irsyam, M. 2015. Identification of engineering bedrock in Jakarta by using array observations of microtremors. Procedia Earth and Planetary Science, 12, 77-83.

Ridwan, M., Widiyantoro, S., Irsyam, M., Afnimar & Yamanaka, H. 2017. Development of an engineering bedrock map beneath Jakarta based on microtremor array measurements. Geological Society, London, Special Publications, 441 (1), 153-165.

Saito, T. 2015. STERA 3D user manual. Structural Earthquake Response Analysis 3D.

Satyam, N. D. & Towhata, I. 2016. Site-specific ground response analysis and liquefaction assessment of Vijayawada city (India). Natural Hazards, 81 (2), 705-724.

Setiawan, B., Jaksa, M., Griffith, M. & Love, D. 2016. Analysis of microtremor array measurement using the spatial autocorrelation (SPAC) method across the Adelaide City. The University of Adelaide.

Silva, T. A. N., Almeida, R. A. B. & Urgueira, A. P. V. 2018. On the use of measured rotational responses in structural dynamic analysis. Journal of Physics: Conference Series, 1106, 012019.

Singh, S. K., Ordaz, M., Pérez-Campos, X. & Iglesias, A. 2015. Intraslab versus interplate earthquakes as recorded in Mexico city: Implications for seismic hazard. Earthquake Spectra, 31 (2), 795-812.

Takano, S. & Saito, T. 2017. Analysis of a school building damaged by the 2015 Ranau earthquake Malaysia. AIP Conference Proceedings. August 8-9, Penang, Malaysia.

Takemura, M., Kato, K., Ikeura, T. & Shima, E. 1991. Site amplification of s-waves from strong motion records in special relation to surface geology. J. Phys. Earth, 39, 537-552.

Tamari, Y., Suzuki, Y., Nakagama, Y. & Otsuka, Y. 2019. An example of three dimensional ground model development for earthquake response analysis by using a simple ground modeling system. Japanese Geotechnical Society Special Publication, 6 (2), 45-52.

Tan, C. G. 2018. Seismic response analysis of buildings in Malaysia subjected to near- field and far-field ground motions. Undergraduate Dissertation, Universiti Sains Malaysia.

Tan, J. P. 2016. Structural damage and ground motion characteristics in the 2015 Sabah earthquake. Undergraduate Dissertation, Universiti Sains Malaysia.

Tan, K. C., Cheah, Y. B. & Lau, T. L. 2018. Estimation of ground structure at USM using microtremor observation technique. E3S Web Conf., 65, 06001.

113

Tanimoto, T., Yano, T. & Hakamata, T. 2013. An approach to improve rayleigh-wave ellipticity estimates from seismic noise: application to the Los Angeles Basin. Geophysical Journal International, 193 (1), 407-420.

Tarancıoğlu, A., Karaaslan, A., Erkan, B., Zor, E., Tapırdamaz, M. C., Özalaybey, S. & Özalaybey, S. Ç. 2010. Shear wave velocity structure of the İzmit Bay area (Turkey) estimated from active–passive array surface wave and single-station microtremor methods. Geophysical Journal International, 182 (3), 1603-1618.

Teague, D., Cox, B., Bradley, B. & Wotherspoon, L. 2018. Development of deep shear wave velocity profiles with estimates of uncertainty in the complex interbedded geology of Christchurch, New Zealand. Earthquake Spectra, 34 (2), 639-672.

Thein, P. S., Pramumijoyo, S., Wilopo, W., Setianto, A., Brotopuspito, K. S. & Kiyono, J. 2014. Estimation of subsurface structure based on microtremor, bore hole observations and stochastic strong ground motion simulations in Palu city, Central Sulawesi, Indonesia: A validation and sensitivity study on the 23 january 2005 (Palu) earthquake. Journal of Southeast Asian Applied Geology, 6 (2), 96-108.

Titi Anggono, S. 2017. Subsurface velocity model from inversion of H/V spectral ratio of ambient noise records. Jurnal Fisika dan Aplikasinya, 2, 45-52.

Tokimatsu, K. & Miyadera, Y. 1992. Characteristics of rayleigh waves in microtremors and their relation to underground structures.

Tokimatsu, K., Mizuno, H. & Kakurai, M. 1996. Building damage associated with geotechnical problems. Soils and Foundations, 36 (Special), 219-234.

Tokimatsu, K., Shinzawa, K. & Kuwayama, S. 1992. Use of short period microtremors for Vs profiling. Journal of Geotechnical Engineering, 118 (10), 1544-1558.

Toksöz, M. N. 1964. Microseisms and an attempted application to exploration. Geophysics, 29 (2), 154-177.

Tongkul, F. 2017. Active tectonics in Sabah seismicity and active faults. Bulletin of the Geological Society of Malaysia, 64, 27-36.

USGS. 2015. Intensity map of 5th June 2015 Sabah earthquake [Online]. Available: https://earthquake.usgs.gov/earthquakes/eventpage/us20002m5s/shakemap/intensity [Accessed].

USGS. 2019a. Historical earthquake event in Sabah from year 1923 to 2019 [Online]. Available: https://earthquake.usgs.gov/earthquakes/map/ [Accessed].

USGS. 2019b. What is the "Ring of Fire"? [Online]. Available: https://www.usgs.gov/faqs/whaat-ring-fire?qt-news_science_products=0#qt- news_science_products [Accessed].

114

Wathelet, M., Jongmans, D., Ohrnberger, M. & Bonnefoy-Claudet, S. 2008. Array performances for ambient vibrations on a shallow structure and consequences over Vsinversion. Journal of Seismology, 12 (1), 1-19.

Yamanaka, H., Takemura, M., Ishida, H. & Niwa, M. 1994. Characteristics of long- period microtremors and their applicability in exploration of deep sedimentary layers. Bulletin of the Seismological Society of America, 84 (6), 1831-1841.

Yuan, P., Li, D., Cai, C. S. & Xu, G. 2019. Time Integration Method with High Accuracy and Efficiency for Structural Dynamic Analysis. Journal of Engineering Mechanics, 145 (3), 04019008.

Zaineh, H. E., Yamanaka, H., Dakkak, R., Khalil, A. & Daoud, M. 2012. Estimation of shallow s-wave velocity structure in Damascus city, Syria, using microtremor exploration. Soil Dynamics and Earthquake Engineering, 39, 88-99.

115

APPENDIX A

Table A.1: Summary of historical event in Sabah from year 1923 until May 2019

Latitude Longitude Epicentre No Date Time (UTC) Magnitude (N) (E) Depth (km)

1 8/3/2018 6.085 116.585 13:06:13 AM 10 5.2 2 5/1/2018 5.9501 117.72 12:40:33 AM 10 4.3 3 26/3/2017 4.933 118.779 9:30:48 AM 34 4.6 4 4/3/2016 4.9182 118.4359 12:43:36 AM 34.96 4.1 5 26/7/2015 6.2782 116.8568 4:10:12 PM 14.96 4.6 6 23/6/2015 6.1277 116.5537 9:32:31 AM 15.32 4.5 7 12/6/2015 6.2053 116.6814 6:29:16 PM 7.25 5.3 8 12/6/2015 6.1504 116.692 6:25:37 PM 15.01 4.4 9 6/6/2015 6.1416 116.6689 5:45:15 AM 10 4.6 10 5/6/2015 6.1402 116.7228 3:13:36 PM 18.23 4.4 11 4/6/2015 5.9867 116.5409 11:15:44 PM 10 6 12 19/3/2015 5.5346 118.6135 9:56:07 PM 50.77 4.1 13 24/10/2014 7.2393 117.256 5:40:34 AM 15.69 4.6 14 5/9/2014 4.6396 118.3414 1:15:55 AM 12.43 4.3 15 1/2/2014 6.1586 116.5453 11:35:11 AM 17.28 4.6 16 28/5/2012 4.786 118.321 4:44:14 PM 39.5 4.6 17 21/8/2010 5.37 118.368 7:43:34 PM 54.1 4.2 18 4/9/2009 7.191 117.115 4:49:13 AM 35 4.5 19 18/5/2008 4.598 118.173 6:26:41 AM 10 5 20 9/4/2008 4.838 118.713 12:51:44 AM 27.8 4.5 21 10/1/2008 4.205 116.508 1:18:37 PM 10 4.1 22 28/9/2006 6.041 117.398 3:11:35 PM 10 4.5 23 22/4/2006 6.121 117.81 2:01:34 AM 70.8 4 24 30/6/2005 4.329 115.62 6:09:49 PM 24.7 4.5 25 23/5/2005 6.256 117.709 7:58:10 PM 19 5.3 26 7/4/2002 7.225 117.052 1:03:58 AM 33 5.1 27 6/12/1996 4.894 118.605 12:42:26 PM 33 4.4 28 11/8/1995 6.34 117.15 6:21:02 AM 33 4.1 29 2/11/1994 5.099 118.643 1:43:56 AM 55.2 5.7 30 4/7/1992 4.976 118.454 10:33:02 PM 10 4.6 31 4/7/1992 4.579 118.049 6:19:34 PM 50 4.3 32 25/8/1991 4.636 118.256 7:15:10 AM 33 4.5 33 26/5/1991 5.869 116.815 11:16:59 AM 18 5.4 34 26/5/1991 5.718 116.748 11:14:31 AM 33 4.7 35 26/5/1991 5.865 116.746 10:59:49 AM 33 5.1 36 26/5/1991 6.113 117.168 7:02:34 AM 33 4.6

Table A.1: Continued

37 13/2/1989 4.265 117.843 8:24:02 PM 32.5 4.4 38 5/2/1989 4.56 118.089 6:32:55 PM 24.4 3.7 39 14/12/1988 5.753 117.859 5:06:28 PM 79.4 5.2 40 24/5/1984 4.108 118.6 2:56:37 PM 33 4.5 41 14/3/1984 5.203 118.387 12:39:18 AM 50.3 5.7 42 26/11/1982 4.895 118.387 7:29:35 PM 33 4.5 43 25/12/1981 4.76 118.477 12:28:16 AM 39.1 5.4 44 23/10/1980 6.519 117.957 2:00:21 PM 51 5.1 45 30/5/1979 6.886 117.004 2:06:44 PM 33 4.5 46 18/9/1976 4.639 118.033 7:54:45 AM 33 5 47 14/8/1976 4.714 118.421 11:10:28 AM 36 5.1 48 26/7/1976 4.592 118.16 1:12:11 PM 33 4.5 49 26/7/1976 4.994 118.55 9:43:51 AM 33 5.1 50 26/7/1976 4.894 118.342 8:49:35 AM 33 5.3 51 26/7/1976 4.904 118.052 8:36:12 AM 33 5.3 52 26/7/1976 4.986 118.594 5:35:10 AM 33 5.2 53 26/7/1976 5.062 118.385 3:03:15 AM 33 5.3 54 26/7/1976 4.956 118.308 2:56:39 AM 33 6.2 55 25/7/1976 5.092 118.287 2:03:18 PM 33 5.3 56 28/4/1973 6.386 117.704 8:39:44 PM 33 5.4 57 2/6/1951 6.878 116.727 6:47:56 AM 15 6.1 58 11/8/1923 5.231 118.28 12:54:43 AM 35 6.3

APPENDIX B

0.15

0.1

0.05 ITK 2 0 ITK 3 ITK 4 -0.05

Velocity (cm/s) Velocity ITK 5

-0.1 ITK 6

-0.15 0 0.5 1 1.5 2 Time (s)

Figure B.1: Velocity time history in X-direction before calibration

0.15

0.1

0.05 ITK 2 0 ITK 3 ITK 4 -0.05

Velocity (cm/s) Velocity ITK 5 -0.1 ITK 6

-0.15 0 0.5 1 1.5 2 Time (s)

Figure B.2: Velocity time history in Y-direction before calibration

0.3 0.25 0.2 0.15 0.1 ITK 2 0.05 ITK 3 0 ITK 4 -0.05

Velocity (cm/s) Velocity ITK 5 -0.1 ITK 6 -0.15 -0.2 -0.25 0 0.5 1 1.5 2 Time (s)

Figure B.3: Velocity time history in Z-direction before calibration

0.1

0.05

0 ITK 2 ITK 3 -0.05 ITK 4

Velocity (cm/s) Velocity ITK 5 -0.1 ITK 6

-0.15 0 0.5 1 1.5 2 Time (s)

Figure B.4: Velocity time history in X-direction after calibration

0.15

0.1

0.05 ITK 2 0 ITK 3 ITK 4 -0.05 Velocity (cm/s) Velocity ITK 5 ITK 6 -0.1

-0.15 0 0.5 1 1.5 2 Time (s)

Figure B.5: Velocity time history in Y-direction after calibration

0.3 0.25 0.2 0.15 0.1 ITK 2 0.05 ITK 3 0 ITK 4 -0.05

Velocity (cm/s) Velocity ITK 5 -0.1 ITK 6 -0.15 -0.2 -0.25 0 0.5 1 1.5 2 Time (s)

Figure B.6: Velocity time history in Z-direction after calibration

APPENDIX C

Figure C.1: Calibration factor of ITK 3 in X-direction before calibration

Figure C.2: Calibration factor of ITK 3 in Y-direction before calibration

Figure C.3: Calibration factor of ITK 3 in Z-direction before calibration

Figure C.4: Calibration factor of ITK 4 in X-direction before calibration

Figure C.5: Calibration factor of ITK 4 in Y-direction before calibration

Figure C.6: Calibration factor of ITK 4 in Z-direction before calibration

Figure C.7: Calibration factor of ITK 5 in X-direction before calibration

Figure C.8: Calibration factor of ITK 5 in Y-direction before calibration

Figure C.9: Calibration factor of ITK 5 in Z-direction before calibration

Figure C.10: Calibration factor of ITK 6 in X-direction before calibration

Figure C.11: Calibration factor of ITK 6 in Y-direction before calibration

Figure C.12: Calibration factor of ITK 6 in Z-direction before calibration

Figure C.13: Calibration factor of ITK 3 in X-direction after calibration

Figure C.14: Calibration factor of ITK 3 in Y-direction after calibration

Figure C.15: Calibration factor of ITK 3 in Z-direction after calibration

Figure C.16: Calibration factor of ITK 4 in X-direction after calibration

Figure C.17: Calibration factor of ITK 4 in Y-direction after calibration

Figure C.18: Calibration factor of ITK 4 in Z-direction after calibration

Figure C.19: Calibration factor of ITK 6 in X-direction after calibration

Figure C.20: Calibration factor of ITK 6 in Y-direction after calibration

Figure C.21: Calibration factor of ITK 6 in Z-direction after calibration

APPENDIX D clc close all clear

%cal = 1; cal=13.5e-07;

%%%%%%%%%%%%%%%%%%%%%%%%%% Start Up %%%%%%%%%%%%%%%%%%%%%%%%%% ndata_set = questdlg('Please chosse your data sets.',... 'Data Sets',... '10sets','5sets','5sets'); switch ndata_set case '10sets' ndata_set = 10; case '5sets' ndata_set = 5; end if isempty(ndata_set) == 1 msgbox('Operation Terminated.','System Message','error') return end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% testing_mode = questdlg('Please chosse your testing mode.',... 'Testing Mode',... 'Random','Fixed Interval','Fixed Interval'); if isempty(testing_mode) == 1 msgbox('Operation Terminated.','System Message','error') return end switch testing_mode case 'Random' testing_mode = 0; case 'Fixed Interval' testing_mode = 1; if ndata_set == 10

testing_interval = questdlg('Please chosse your testing interval.',... 'Testing Interval',... '45sec','35sec','40sec','40sec');

if isempty(testing_interval) == 1 msgbox('Operation Terminated.','System Message','error') return end

switch testing_interval case '40sec' testing_interval = 40;

case '45sec' testing_interval = 45; case '35sec' testing_interval = 35; end

else

testing_interval = questdlg('Please chosse your testing interval.',... 'Testing Interval',... '30sec','40sec','35sec','35sec');

if isempty(testing_interval) == 1 msgbox('Operation Terminated.','System Message','error') return end

switch testing_interval case '30sec' testing_interval = 30; case '40sec' testing_interval = 40; case '35sec' testing_interval = 35; end end end %%%%%%%%%%%%%%%%%%%%%%%%%% Read All Sets of Data %%%%%%%%%%%%%%%%%%%%%%%%%% %search file if ndata_set == 5 [filename,pathname]=uigetfile('name','Select 5 data files','*.tsv;*.txt',... 'MultiSelect','on'); if numel(filename) > 5 msgbox('Input files should be 5.','Error','error') return else if numel(filename) < 5 msgbox('Input files should be 5.','Error','error') return end end else [filename,pathname]=uigetfile('name','Select 10 data files','*.tsv;*.txt',... 'MultiSelect','on'); if numel(filename) > 10 msgbox('Input files should be 10.','Error','error') return else if numel(filename) < 10 msgbox('Input files should be 10.','Error','error') return end end end

%read the search file filenamestr1 = strjoin(filename(1));

m1=fullfile(pathname,filenamestr1); A1=dlmread(m1,'',0,2); x1=A1(:,1); y1=A1(:,2); z1=A1(:,3); filenamestr2 = strjoin(filename(2)); m2=strcat(pathname,filenamestr2); A2=dlmread(m2,'',0,2); x2=A2(:,1); y2=A2(:,2); z2=A2(:,3); filenamestr3 = strjoin(filename(3)); m3=strcat(pathname,filenamestr3); A3=dlmread(m3,'',0,2); x3=A3(:,1); y3=A3(:,2); z3=A3(:,3); filenamestr4 = strjoin(filename(4)); m4=strcat(pathname,filenamestr4); A4=dlmread(m4,'',0,2); x4=A4(:,1); y4=A4(:,2); z4=A4(:,3); filenamestr5 = strjoin(filename(5)); m5=strcat(pathname,filenamestr5); A5=dlmread(m5,'',0,2); x5=A5(:,1); y5=A5(:,2); z5=A5(:,3); if ndata_set == 10 filenamestr6 = strjoin(filename(6)); m6=fullfile(pathname,filenamestr6); A6=dlmread(m6,'',0,2); x6=A6(:,1); y6=A6(:,2); z6=A6(:,3);

filenamestr7 = strjoin(filename(7)); m7=strcat(pathname,filenamestr7); A7=dlmread(m7,'',0,2); x7=A7(:,1); y7=A7(:,2); z7=A7(:,3);

filenamestr8 = strjoin(filename(8)); m8=strcat(pathname,filenamestr8); A8=dlmread(m8,'',0,2); x8=A8(:,1); y8=A8(:,2); z8=A8(:,3);

filenamestr9 = strjoin(filename(9)); m9=strcat(pathname,filenamestr9); A9=dlmread(m9,'',0,2); x9=A9(:,1);

y9=A9(:,2); z9=A9(:,3);

filenamestr10 = strjoin(filename(10)); m10=strcat(pathname,filenamestr10); A10=dlmread(m5,'',0,2); x10=A10(:,1); y10=A10(:,2); z10=A10(:,3); end

%%%%%%%%%%%%%%%%%%%%%%% Show selected files %%%%%%%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Selected Files',... 'position',[100 500 1000 100]);

if ndata_set == 10 dat = {filenamestr1,filenamestr2,filenamestr3,filenamestr4,... filenamestr5,filenamestr6,filenamestr7,filenamestr8,... filenamestr9,filenamestr10}; columnname = {'File 1','File 2', 'File 3','File 4', 'File 5',... 'File 6','File 7','File 8','File 9','File 10'}; else dat = {filenamestr1,filenamestr2,filenamestr3,filenamestr4,filenamestr5}; columnname = {'File 1','File 2', 'File 3','File 4', 'File 5'}; end rowname = {'File Name'}; uitable('Data', dat, 'ColumnName', columnname, 'RowName', rowname,... 'Position',[0 0 1000 80]); saveas(gcf,'datasets.emf');

%%%%%%%%%%%%%%%%%%%%%% Combine Data into 1 axis %%%%%%%%%%%%%%%%%%%%%%%%% %data in 1 axis if ndata_set == 5 x = [x1;x2;x3;x4;x5]; y = [y1;y2;y3;y4;y5]; z = [z1;z2;z3;z4;z5]; i = 1:30000; %total duration of data inserted t=0.01:0.01:300; else x = [x1;x2;x3;x4;x5;x6;x7;x8;x9;x10]; y = [y1;y2;y3;y4;y5;y6;y7;y8;y9;y10]; z = [z1;z2;z3;z4;z5;z6;z7;z8;z9;z10]; i = 1:60000; t=0.01:0.01:600; end x_sum = cumsum(x); y_sum = cumsum(y); z_sum = cumsum(z); i_rs = reshape(i,[],1); mx = x_sum ./ i_rs; my = y_sum ./ i_rs;

mz = z_sum ./ i_rs;

%data divided by mean of each consecutive measurement X = (x - mx) * cal; Y = (y - my) * cal; Z = (z - mz) * cal;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% % Velocity Time History Graph % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% scrsz = get(0,'ScreenSize'); figure('color','white','name','Velocity Time history (Set X)',... 'position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]);

subplot(3,1,1); plot(t,X,'r') grid minor; xlabel('Time (sec)') ylabel('Velocity (cm/sec)') title ('X Velocity Time History') hold on

%determine the upper and lower limit of y axis lim = get(gca,'ylim'); limi = lim(1); limf = lim(2); subplot(3,1,2); plot(t,Y,'g') grid minor; xlabel('Time (sec)') ylabel('Velocity (cm/sec)') title ('Y Velocity Time History') hold on subplot(3,1,3); plot(t,Z,'b') grid minor; xlabel('Time (sec)') ylabel('Velocity (cm/sec)') title ('Z Velocity Time History') hold on

%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Select Desired data %%%%%%%%%%%%%%%%%%%%%%%%% if testing_mode == 0

%select data interval (time) [t_1i,~] = ginput(1); [t_1f,~] = ginput(1); a1i = area([t_1i t_1f],[limi limi]); a1f = area([t_1i t_1f],[limf limf]); a1i.FaceColor = 'y'; a1i.FaceAlpha = 0.2; a1f.FaceColor = 'y'; a1f.FaceAlpha = 0.2;

[t_2i,~] = ginput(1); [t_2f,~] = ginput(1); a2i = area([t_2i t_2f],[limi limi]); a2f = area([t_2i t_2f],[limf limf]); a2i.FaceColor = 'y'; a2i.FaceAlpha = 0.2; a2f.FaceColor = 'y'; a2f.FaceAlpha = 0.2;

[t_3i,~] = ginput(1); [t_3f,~] = ginput(1); a3i = area([t_3i t_3f],[limi limi]); a3f = area([t_3i t_3f],[limf limf]); a3i.FaceColor = 'y'; a3i.FaceAlpha = 0.2; a3f.FaceColor = 'y'; a3f.FaceAlpha = 0.2;

[t_4i,~] = ginput(1); [t_4f,~] = ginput(1); a4i = area([t_4i t_4f],[limi limi]); a4f = area([t_4i t_4f],[limf limf]); a4i.FaceColor = 'y'; a4i.FaceAlpha = 0.2; a4f.FaceColor = 'y'; a4f.FaceAlpha = 0.2;

[t_5i,~] = ginput(1); [t_5f,~] = ginput(1); a5i = area([t_5i t_5f],[limi limi]); a5f = area([t_5i t_5f],[limf limf]); a5i.FaceColor = 'y'; a5i.FaceAlpha = 0.2; a5f.FaceColor = 'y'; a5f.FaceAlpha = 0.2; else [t_1i,~] = ginput(1); t_1f = t_1i + testing_interval - 0.01; a1i = area([t_1i t_1f],[limi limi]); a1f = area([t_1i t_1f],[limf limf]); a1i.FaceColor = 'y'; a1i.FaceAlpha = 0.2; a1f.FaceColor = 'y'; a1f.FaceAlpha = 0.2;

[t_2i,~] = ginput(1); t_2f = t_2i + testing_interval - 0.01; a2i = area([t_2i t_2f],[limi limi]); a2f = area([t_2i t_2f],[limf limf]); a2i.FaceColor = 'y'; a2i.FaceAlpha = 0.2; a2f.FaceColor = 'y'; a2f.FaceAlpha = 0.2;

[t_3i,~] = ginput(1); t_3f = t_3i + testing_interval - 0.01; a3i = area([t_3i t_3f],[limi limi]); a3f = area([t_3i t_3f],[limf limf]);

a3i.FaceColor = 'y'; a3i.FaceAlpha = 0.2; a3f.FaceColor = 'y'; a3f.FaceAlpha = 0.2;

[t_4i,~] = ginput(1); t_4f = t_4i + testing_interval - 0.01; a4i = area([t_4i t_4f],[limi limi]); a4f = area([t_4i t_4f],[limf limf]); a4i.FaceColor = 'y'; a4i.FaceAlpha = 0.2; a4f.FaceColor = 'y'; a4f.FaceAlpha = 0.2;

[t_5i,~] = ginput(1); t_5f = t_5i + testing_interval - 0.01; a5i = area([t_5i t_5f],[limi limi]); a5f = area([t_5i t_5f],[limf limf]); a5i.FaceColor = 'y'; a5i.FaceAlpha = 0.2; a5f.FaceColor = 'y'; a5f.FaceAlpha = 0.2; end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1st data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %return interval to sequence n1i = round(t_1i / 0.01); n1f = round(t_1f / 0.01);

%return interval to y axis values int1 = n1i : n1f; t_int_1 = (n1i : n1f) * 0.01; if size(t_int_1) < 3000 close all msgbox('Operation Terminated. Error: Minimum interval is 30 seconds.',... 'System Error','error') return end x_int_1 = X(int1); y_int_1 = Y(int1); z_int_1 = Z(int1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2nd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% n2i = round(t_2i / 0.01); n2f = round(t_2f / 0.01); int2 = n2i : n2f; t_int_2 = (n2i : n2f) * 0.01; if size(t_int_2) < 3000 close all msgbox('Operation Terminated. Error: Minimum interval is 30 seconds.',... 'System Error','error') return

end x_int_2 = X(int2); y_int_2 = Y(int2); z_int_2 = Z(int2);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3rd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% n3i = round(t_3i / 0.01); n3f = round(t_3f / 0.01); int3 = n3i : n3f; t_int_3 = (n3i : n3f) * 0.01; if size(t_int_3) < 3000 close all msgbox('Operation Terminated. Error: Minimum interval is 30 seconds.',... 'System Error','error') return end x_int_3 = X(int3); y_int_3 = Y(int3); z_int_3 = Z(int3);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% n4i = round(t_4i / 0.01); n4f = round(t_4f / 0.01); int4 = n4i : n4f; t_int_4 = (n4i : n4f) * 0.01; if size(t_int_4) < 3000 close all msgbox('Operation Terminated. Error: Minimum interval is 30 seconds.',... 'System Error','error') return end x_int_4 = X(int4); y_int_4 = Y(int4); z_int_4 = Z(int4);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% n5i = round(t_5i / 0.01); n5f = round(t_5f / 0.01); int5 = n5i : n5f; t_int_5 = (n5i : n5f) * 0.01; if size(t_int_5) < 3000 close all msgbox('Operation Terminated. Error: Minimum interval is 30 seconds.',... 'System Error','error')

return end x_int_5 = X(int5); y_int_5 = Y(int5); z_int_5 = Z(int5);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Plot Desired data %%%%%%%%%%%%%%%%%%%%%%%%%%% % X % %%%%%%%%%%%%%%%%%%%%%%%%%%%% 1st data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Velocity Time History (Selected Data)',... 'position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) subplot(3,5,1) plot(t_int_1,x_int_1,'r') grid minor xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 1 - X')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2nd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,2); plot(t_int_2,x_int_2,'g') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 2 - X')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3rd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,3); plot(t_int_3,x_int_3,'b') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 3 - X')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,4); plot(t_int_4,x_int_4,'k') hold on; grid on; grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 4 - X')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,5); plot(t_int_5,x_int_5,'y') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)')

title ('Data 5 - X')

%%%%%%%%%%%%%%%%%%%%%%%%%%%% Plot Desired data %%%%%%%%%%%%%%%%%%%%%%%%%%%% % Y % %%%%%%%%%%%%%%%%%%%%%%%%%%% 1st data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,6); plot(t_int_1,y_int_1,'r') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 1 - Y')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2nd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,7); plot(t_int_2,y_int_2,'g') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 2 - Y')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3rd data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,8); plot(t_int_3,y_int_3,'b') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 3 - Y')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,9); plot(t_int_4,y_int_4,'k') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 4 - Y')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5th data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,10); plot(t_int_5,y_int_5,'y') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 5 - Y')

%%%%%%%%%%%%%%%%%%%%%%%%%%%% Plot Desired data %%%%%%%v%%%%%%%%%%%%%%%%%%%% % Z % %%%%%%%%%%%%%%%%%%%%%%%%%%% 1st data %%%%%v%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,11); plot(t_int_1,z_int_1,'R') grid minor;

xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 1 - Z')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2nd data %%%%%%%%%%%%v%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,12); plot(t_int_2,z_int_2,'g') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 2 - Z')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3rd data %%%%%%%%%%%%v%%%%%%%%%%%%%%%%%%%%%% subplot(3,5,13); plot(t_int_3,z_int_3,'b') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 3 - Z')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4th data %%%%%%%%%%%%%%v%%%%%%%%%%%%%%%%%%%% subplot(3,5,14); plot(t_int_4,z_int_4,'k') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 4 - Z')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5th data %%%%%%%%%%%%%%%%%%%%v%%%%%%%%%%%%%% subplot(3,5,15); plot(t_int_5,z_int_5,'y') grid minor; xlabel('Time (sec)') ylabel('Vel. (cm/sec)') title ('Data 5 - Z')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %%%%%%%%%%%% Displacement Time Histories %%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%

%Integrate velocity time histories to obtain displcement time histories dt = 0.01;

%%%%%%%%%%%%%%%%%%% X %%%%%%%%%%%%%%%%%%%%%%% %disp_X1 = dt * cumtrapz(x_int_1); %disp_X2 = dt * cumtrapz(x_int_2); %disp_X3 = dt * cumtrapz(x_int_3); %disp_X4 = dt * cumtrapz(x_int_4); %disp_X5 = dt * cumtrapz(x_int_5);

%%%%%%%%%%%%%%%%%%% Y %%%%%%%%%%%%%%%%%%%%%%% %disp_Y1 = dt * cumtrapz(y_int_1); %disp_Y2 = dt * cumtrapz(y_int_2); %disp_Y3 = dt * cumtrapz(y_int_3); %disp_Y4 = dt * cumtrapz(y_int_4); %disp_Y5 = dt * cumtrapz(y_int_5);

%%%%%%%%%%%%%%%%%%% Z %%%%%%%%%%%%%%%%%%%%%%% %disp_Z1 = dt * cumtrapz(z_int_1); %disp_Z2 = dt * cumtrapz(z_int_2); %disp_Z3 = dt * cumtrapz(z_int_3); %disp_Z4 = dt * cumtrapz(z_int_4); %disp_Z5 = dt * cumtrapz(z_int_5);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %%%%%%%%%%%% Acceleration Time Histories %%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %Differentiate velocity time histories to obtain acceleration time histories if testing_mode == 0

n1 = numel(t_int_1); nI1 = 2 : numel(t_int_1); nF1 = 1 : numel(t_int_1) - 1; n2 = numel(t_int_2); nI2 = 2 : numel(t_int_2); nF2 = 1 : numel(t_int_2) - 1; n3 = numel(t_int_3); nI3 = 2 : numel(t_int_3); nF3 = 1 : numel(t_int_3) - 1; n4 = numel(t_int_4); nI4 = 2 : numel(t_int_4); nF4 = 1 : numel(t_int_4) - 1; n5 = numel(t_int_5); nI5 = 2 : numel(t_int_5); nF5 = 1 : numel(t_int_5) - 1;

T1 = n1 * dt; f = 1 / dt; df1 = 1 / T1; fF1 = df1 : df1 : f;

j1 = 1:n1/2+1; FF1(j1) = fF1(j1); F1 = reshape(FF1,[],1);

T2 = n2 * dt; df2 = 1 / T2; fF2 = df2 : df2 : f;

j2 = 1:n2/2+1; FF2(j2) = fF2(j2); F2 = reshape(FF2,[],1);

T3 = n3 * dt;

df3 = 1 / T3; fF3 = df3 : df3 : f;

j3 = 1:n3/2+1; FF3(j3) = fF3(j3); F3 = reshape(FF3,[],1);

T4 = n4 * dt; df4 = 1 / T4; fF4 = df4 : df4 : f;

j4 = 1:n4/2+1; FF4(j4) = fF4(j4); F4 = reshape(FF4,[],1);

T5 = n5 * dt; df5 = 1 / T5; fF5 = df5 : df5 : f;

j5 = 1:n5/2+1; FF5(j5) = fF5(j5); F5 = reshape(FF5,[],1); else n1 = numel(t_int_1); nI1 = 2 : numel(t_int_1); nF1 = 1 : numel(t_int_1) - 1; n2 = n1; nI2 = nI1; nF2 = nF1; n3 = n1; nI3 = nI1; nF3 = nF1; n4 = n1; nI4 = nI1; nF4 = nF1; n5 = n1; nI5 = nI1; nF5 = nF1;

T1 = n1 * dt; f = 1 / dt; df1 = 1 / T1; fF1 = df1 : df1 : f;

j1 = 1:n1/2+1; FF1(j1) = fF1(j1); F1 = reshape(FF1,[],1);

F2 = F1; F3 = F1; F4 = F1; F5 = F1; end

%%%%%%%%%%%%%%%%%%% X %%%%%%%%%%%%%%%%%%%%%%% acc_X1 = (x_int_1(nI1) - x_int_1(nF1)) / dt;

acc_X2 = (x_int_2(nI2) - x_int_2(nF2)) / dt; acc_X3 = (x_int_3(nI3) - x_int_3(nF3)) / dt; acc_X4 = (x_int_4(nI4) - x_int_4(nF4)) / dt; acc_X5 = (x_int_5(nI5) - x_int_5(nF5)) / dt;

%%%%%%%%%%%%%%%%%%% Y %%%%%%%%%%%%%%%%%%%%%%% acc_Y1 = (y_int_1(nI1) - y_int_1(nF1)) / dt; acc_Y2 = (y_int_2(nI2) - y_int_2(nF2)) / dt; acc_Y3 = (y_int_3(nI3) - y_int_3(nF3)) / dt; acc_Y4 = (y_int_4(nI4) - y_int_4(nF4)) / dt; acc_Y5 = (y_int_5(nI5) - y_int_5(nF5)) / dt;

%%%%%%%%%%%%%%%%%%% Z %%%%%%%%%%%%%%%%%%%%%%% acc_Z1 = (z_int_1(nI1) - z_int_1(nF1)) / dt; acc_Z2 = (z_int_2(nI2) - z_int_2(nF2)) / dt; acc_Z3 = (z_int_3(nI3) - z_int_3(nF3)) / dt; acc_Z4 = (z_int_4(nI4) - z_int_4(nF4)) / dt; acc_Z5 = (z_int_5(nI5) - z_int_5(nF5)) / dt;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %%%%%%%%%%%% Fast Fourier Transform %%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %%%%%%%%%%%%%%%%%% First Set %%%%%%%%%%%%%%%%%%% acc_X1_fft = fft(acc_X1); acc_Y1_fft = fft(acc_Y1); acc_Z1_fft = fft(acc_Z1); acc_X1_fftp = abs(acc_X1_fft / n1); acc_Y1_fftp = abs(acc_Y1_fft / n1); acc_Z1_fftp = abs(acc_Z1_fft / n1); j1 = 1:n1/2+1; acc_X1_fftp1 = acc_X1_fftp(j1); acc_Y1_fftp1 = acc_Y1_fftp(j1); acc_Z1_fftp1 = acc_Z1_fftp(j1); acc_X1_fftp1(2:end-1) = 2 * acc_X1_fftp1(2:end-1); acc_Y1_fftp1(2:end-1) = 2 * acc_Y1_fftp1(2:end-1); acc_Z1_fftp1(2:end-1) = 2 * acc_Z1_fftp1(2:end-1);

%H1(j1)= (((acc_X1_fftp1(j1).^2) + (acc_Y1_fftp1(j1).^2))/2).^0.5;

%%%%%%%%%%%%%%%%%% Second Set %%%%%%%%%%%%%%%%%% acc_X2_fft = fft(acc_X2); acc_Y2_fft = fft(acc_Y2); acc_Z2_fft = fft(acc_Z2); acc_X2_fftp = abs(acc_X2_fft / n2); acc_Y2_fftp = abs(acc_Y2_fft / n2); acc_Z2_fftp = abs(acc_Z2_fft / n2); j2 = 1:n2/2+1;

acc_X2_fftp1 = acc_X2_fftp(j2); acc_Y2_fftp1 = acc_Y2_fftp(j2); acc_Z2_fftp1 = acc_Z2_fftp(j2); acc_X2_fftp1(2:end-1) = 2 * acc_X2_fftp1(2:end-1); acc_Y2_fftp1(2:end-1) = 2 * acc_Y2_fftp1(2:end-1); acc_Z2_fftp1(2:end-1) = 2 * acc_Z2_fftp1(2:end-1);

%H2(j2)= (((acc_X2_fftp1(j2).^2) + (acc_Y2_fftp1(j2).^2))/2).^0.5;

%%%%%%%%%%%%%%%%%% Third Set %%%%%%v%%%%%%%%%%%% acc_X3_fft = fft(acc_X3); acc_Y3_fft = fft(acc_Y3); acc_Z3_fft = fft(acc_Z3); acc_X3_fftp = abs(acc_X3_fft / n3); acc_Y3_fftp = abs(acc_Y3_fft / n3); acc_Z3_fftp = abs(acc_Z3_fft / n3); j3 = 1:n3/2+1; acc_X3_fftp1 = acc_X3_fftp(j3); acc_Y3_fftp1 = acc_Y3_fftp(j3); acc_Z3_fftp1 = acc_Z3_fftp(j3); acc_X3_fftp1(2:end-1) = 2 * acc_X3_fftp1(2:end-1); acc_Y3_fftp1(2:end-1) = 2 * acc_Y3_fftp1(2:end-1); acc_Z3_fftp1(2:end-1) = 2 * acc_Z3_fftp1(2:end-1);

%H3(j3)= (((acc_X3_fftp1(j3).^2) + (acc_Y3_fftp1(j3).^2))/2).^0.5;

%%%%%%%%%%%%%%%%%% Forth Set %%%%%%%%v%%%%%%%%%% acc_X4_fft = fft(acc_X4); acc_Y4_fft = fft(acc_Y4); acc_Z4_fft = fft(acc_Z4); acc_X4_fftp = abs(acc_X4_fft / n4); acc_Y4_fftp = abs(acc_Y4_fft / n4); acc_Z4_fftp = abs(acc_Z4_fft / n4); j4 = 1:n4/2+1; acc_X4_fftp1 = acc_X4_fftp(j4); acc_Y4_fftp1 = acc_Y4_fftp(j4); acc_Z4_fftp1 = acc_Z4_fftp(j4); acc_X4_fftp1(2:end-1) = 2 * acc_X4_fftp1(2:end-1); acc_Y4_fftp1(2:end-1) = 2 * acc_Y4_fftp1(2:end-1); acc_Z4_fftp1(2:end-1) = 2 * acc_Z4_fftp1(2:end-1);

%H4(j4)= (((acc_X4_fftp1(j4).^2) + (acc_Y4_fftp1(j4).^2))/2).^0.5;

%%%%%%%%%%%%%%%%%% Fifth Set %%%%%%%v%%%%%%%%%%% acc_X5_fft = fft(acc_X5); acc_Y5_fft = fft(acc_Y5); acc_Z5_fft = fft(acc_Z5);

acc_X5_fftp = abs(acc_X5_fft / n5); acc_Y5_fftp = abs(acc_Y5_fft / n5); acc_Z5_fftp = abs(acc_Z5_fft / n5); j5 = 1:n5/2+1; acc_X5_fftp1 = acc_X5_fftp(j5); acc_Y5_fftp1 = acc_Y5_fftp(j5); acc_Z5_fftp1 = acc_Z5_fftp(j5); acc_X5_fftp1(2:end-1) = 2 * acc_X5_fftp1(2:end-1); acc_Y5_fftp1(2:end-1) = 2 * acc_Y5_fftp1(2:end-1); acc_Z5_fftp1(2:end-1) = 2 * acc_Z5_fftp1(2:end-1);

%H5(j5)= (((acc_X5_fftp1(j5).^2) + (acc_Y5_fftp1(j5).^2))/2).^0.5;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% %%%%%%%%%%%% Fast Fourier Transform Plot Starts %%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%

%H1 = reshape(H1,[],1); %H2 = reshape(H2,[],1); %H3 = reshape(H3,[],1); %H4 = reshape(H4,[],1); %H5 = reshape(H5,[],1);

%HV1 = H1./acc_Z1_fftp1; %HV2 = H2./acc_Z2_fftp1; %HV3 = H3./acc_Z3_fftp1; %HV4 = H4./acc_Z4_fftp1; %HV5 = H5./acc_Z5_fftp1;

HV1X = acc_X1_fftp1./acc_Z1_fftp1; HV2X = acc_X2_fftp1./acc_Z2_fftp1; HV3X = acc_X3_fftp1./acc_Z3_fftp1; HV4X = acc_X4_fftp1./acc_Z4_fftp1; HV5X = acc_X5_fftp1./acc_Z5_fftp1;

HV1Y = acc_Y1_fftp1./acc_Z1_fftp1; HV2Y = acc_Y2_fftp1./acc_Z2_fftp1; HV3Y = acc_Y3_fftp1./acc_Z3_fftp1; HV4Y = acc_Y4_fftp1./acc_Z4_fftp1; HV5Y = acc_Y5_fftp1./acc_Z5_fftp1;

%%%%%%%%%%%% Smooth Data Using Non- Parametic Method %%%%%%%%%%%% perc = 0.02; %to be modified

%SHV1=mslowess(F1, HV1,'kernel','tricubic', 'span', perc); %SHV2=mslowess(F2, HV2,'kernel','tricubic', 'span', perc); %SHV3=mslowess(F3, HV3,'kernel','tricubic', 'span', perc); %SHV4=mslowess(F4, HV4,'kernel','tricubic', 'span', perc); %SHV5=mslowess(F5, HV5,'kernel','tricubic', 'span', perc);

SHV1X=mslowess(F1, HV1X,'kernel','tricubic', 'span', perc); SHV2X=mslowess(F2, HV2X,'kernel','tricubic', 'span', perc);

SHV3X=mslowess(F3, HV3X,'kernel','tricubic', 'span', perc); SHV4X=mslowess(F4, HV4X,'kernel','tricubic', 'span', perc); SHV5X=mslowess(F5, HV5X,'kernel','tricubic', 'span', perc);

SHV1Y=mslowess(F1, HV1Y,'kernel','tricubic', 'span', perc); SHV2Y=mslowess(F2, HV2Y,'kernel','tricubic', 'span', perc); SHV3Y=mslowess(F3, HV3Y,'kernel','tricubic', 'span', perc); SHV4Y=mslowess(F4, HV4Y,'kernel','tricubic', 'span', perc); SHV5Y=mslowess(F5, HV5Y,'kernel','tricubic', 'span', perc);

%%%%%%%%%%%%%%%%%%%%% HV Ratio Plot Starts %%%%%%%%%%%%%%%%%%%%%% %figure('color','white','name','Combined HV RATIO vs Frequency',... %'Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8])

%loglog(F1,SHV1,'r', F2,SHV2,'g', F3,SHV3,'b', F4,SHV4,'k', F5,SHV5,'m') %xlabel('Frequency (Hz)') %ylabel('H/V Ratio') %title ('H/V Spectra') %legend ('SET 1', 'SET 2','SET 3','SET 4','SET 5') %grid on %axis([0.1 50 0.1 50])

%saveas(gcf,'combine_plot.emf');

%set1 = [F1,SHV1]; %save -ascii dataset1.out set1; %set2 = [F2,SHV2]; %save -ascii dataset2.out set2; %set3 = [F3,SHV3]; %save -ascii dataset3.out set3; %set4 = [F4,SHV4]; %save -ascii dataset4.out set4; %set5 = [F5,SHV5]; %save -ascii dataset5.out set5;

%HVX/F figure('color','white','name','Combined HVX RATIO vs Frequency',... 'Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8])

loglog(F1,SHV1X,'r', F2,SHV2X,'g', F3,SHV3X,'b', F4,SHV4X,'k', F5,SHV5X,'m') xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra') legend ('SET 1X', 'SET 2X','SET 3X','SET 4X','SET 5X') grid on axis([0.1 100 0.1 inf]) saveas(gcf,'combine_plot X.emf'); set1X = [F1,SHV1X]; save -ascii dataset1X.out set1X; set2X = [F2,SHV2X]; save -ascii dataset2X.out set2X; set3X = [F3,SHV3X];

save -ascii dataset3X.out set3X; set4X = [F4,SHV4X]; save -ascii dataset4X.out set4X; set5X = [F5,SHV5X]; save -ascii dataset5X.out set5X;

%HVY/F figure('color','white','name','Combined HVY RATIO vs Frequency',... 'Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8])

loglog(F1,SHV1Y,'r', F2,SHV2Y,'g', F3,SHV3Y,'b', F4,SHV4Y,'k', F5,SHV5Y,'m') xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra') legend ('SET 1Y', 'SET 2Y','SET 3Y','SET 4Y','SET 5Y') grid on axis([0.1 100 0.1 inf]) saveas(gcf,'combine_plot Y.emf'); set1Y = [F1,SHV1Y]; save -ascii dataset1Y.out set1Y; set2Y = [F2,SHV2Y]; save -ascii dataset2Y.out set2Y; set3Y = [F3,SHV3Y]; save -ascii dataset3Y.out set3Y; set4Y = [F4,SHV4Y]; save -ascii dataset4Y.out set4Y; set5Y = [F5,SHV5Y]; save -ascii dataset5Y.out set5Y;

%%%%%%%%%%%%%%%%%%% Seperate Plot For HVX Ratio %%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% Set 1X %%%%%%%%%%%%%%%%%%%%%% figure('name','Set 1X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) subplot(1,1,1); loglog(F1,HV1X,'linewidth',0.5) hold on; loglog(F1,SHV1X,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 1X') axis([0.1 100 0.1 inf]) grid on

%locate the peak [y_coord11,x_coord11] = findpeaks(SHV1X,F1,'NPeaks',10,'SortStr','descend'); plot(x_coord11,y_coord11,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord11(1),y_coord11(1),'1','FontSize',16) text(x_coord11(2),y_coord11(2),'2','FontSize',16) text(x_coord11(3),y_coord11(3),'3','FontSize',16) text(x_coord11(4),y_coord11(4),'4','FontSize',16) text(x_coord11(5),y_coord11(5),'5','FontSize',16) text(x_coord11(6),y_coord11(6),'6','FontSize',16) text(x_coord11(7),y_coord11(7),'7','FontSize',16)

text(x_coord11(8),y_coord11(8),'8','FontSize',16) text(x_coord11(9),y_coord11(9),'9','FontSize',16) text(x_coord11(10),y_coord11(10),'10','FontSize',16) num11 = choosedialog; num1 = str2double(num11); x_coord1X = x_coord11(num1); y_coord1X = y_coord11(num1);

Flimit1 = x_coord1X; if Flimit1 > 11 condition1 = 0; else condition1 = 1; end close;

%reassign the peak coordinate name HVrX1 = y_coord1X; Fq1 = x_coord1X;

%%%%%%%%%%%%%%%%%%% Set 2X %%%%%%%%%%%%%%%%%%%%%% figure('name','Set 2X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F2,HV2X,'linewidth',0.5) hold on loglog(F2,SHV2X,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 2X') axis([0.1 100 0.1 inf]) grid on

%locate the peak [y_coord21,x_coord21] = findpeaks(SHV2X,F2,'NPeaks',10,'SortStr','descend'); plot(x_coord21,y_coord21,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord21(1),y_coord21(1),'1','FontSize',16) text(x_coord21(2),y_coord21(2),'2','FontSize',16) text(x_coord21(3),y_coord21(3),'3','FontSize',16) text(x_coord21(4),y_coord21(4),'4','FontSize',16) text(x_coord21(5),y_coord21(5),'5','FontSize',16) text(x_coord21(6),y_coord21(6),'6','FontSize',16) text(x_coord21(7),y_coord21(7),'7','FontSize',16) text(x_coord21(8),y_coord21(8),'8','FontSize',16) text(x_coord21(9),y_coord21(9),'9','FontSize',16) text(x_coord21(10),y_coord21(10),'10','FontSize',16) num21 = choosedialog; num2 = str2double(num21); x_coord2X = x_coord21(num2); y_coord2X = y_coord21(num2);

Flimit2 = x_coord2X; if Flimit2 > 11 condition2 = 0; else condition2 = 1; end close;

HVrX2 = y_coord2X; Fq2 = x_coord2X;

%%%%%%%%%%%%%%%%%%% Set 3X %%%%%%%%%%%%%%%%%%%%%% figure('name','Set 3X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F3,HV3X,'linewidth',0.5) hold on loglog(F3,SHV3X,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 3X') axis([0.1 100 0.1 inf]) grid on

[y_coord31,x_coord31] = findpeaks(SHV3X,F3,'NPeaks',10,'SortStr','descend'); plot(x_coord31,y_coord31,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord31(1),y_coord31(1),'1','FontSize',16) text(x_coord31(2),y_coord31(2),'2','FontSize',16) text(x_coord31(3),y_coord31(3),'3','FontSize',16) text(x_coord31(4),y_coord31(4),'4','FontSize',16) text(x_coord31(5),y_coord31(5),'5','FontSize',16) text(x_coord31(6),y_coord31(6),'6','FontSize',16) text(x_coord31(7),y_coord31(7),'7','FontSize',16) text(x_coord31(8),y_coord31(8),'8','FontSize',16) text(x_coord31(9),y_coord31(9),'9','FontSize',16) text(x_coord31(10),y_coord31(10),'10','FontSize',16) num31 = choosedialog; num3 = str2double(num31); x_coord3X = x_coord31(num3); y_coord3X = y_coord31(num3);

Flimit3 = x_coord3X; if Flimit3 > 11 condition3 = 0; else condition3 = 1; end close;

HVrX3 = y_coord3X; Fq3 = x_coord3X;

%%%%%%%%%%%%%%%%%%% Set 4X %%%%%%%%%%%%%%%%%%%%%% figure('name','Set 4X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]); loglog(F4,HV4X,'linewidth',0.5); hold on loglog(F4,SHV4X,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 4X') axis([0.1 100 0.1 inf]) grid on

[y_coord41,x_coord41] = findpeaks(SHV4X,F4,'NPeaks',10,'SortStr','descend'); plot(x_coord41,y_coord41,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord41(1),y_coord41(1),'1','FontSize',16) text(x_coord41(2),y_coord41(2),'2','FontSize',16) text(x_coord41(3),y_coord41(3),'3','FontSize',16) text(x_coord41(4),y_coord41(4),'4','FontSize',16) text(x_coord41(5),y_coord41(5),'5','FontSize',16) text(x_coord41(6),y_coord41(6),'6','FontSize',16) text(x_coord41(7),y_coord41(7),'7','FontSize',16) text(x_coord41(8),y_coord41(8),'8','FontSize',16) text(x_coord41(9),y_coord41(9),'9','FontSize',16) text(x_coord41(10),y_coord41(10),'10','FontSize',16) num41 = choosedialog; num4 = str2double(num41); x_coord4X = x_coord41(num4); y_coord4X = y_coord41(num4);

Flimit4X = x_coord4X; if Flimit4X > 11 condition4 = 0; else condition4 = 1; end close;

HVrX4 = y_coord4X; Fq4 = x_coord4X;

%%%%%%%%%%%%%%%%%%% Set 5X %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 5X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F5,HV5X,'linewidth',0.5) hold on loglog(F5,SHV5X,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 5') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord51,x_coord51] = findpeaks(SHV5X,F5,'NPeaks',10,'SortStr','descend'); plot(x_coord51,y_coord51,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord51(1),y_coord51(1),'1','FontSize',16) text(x_coord51(2),y_coord51(2),'2','FontSize',16) text(x_coord51(3),y_coord51(3),'3','FontSize',16) text(x_coord51(4),y_coord51(4),'4','FontSize',16) text(x_coord51(5),y_coord51(5),'5','FontSize',16) text(x_coord51(6),y_coord51(6),'6','FontSize',16) text(x_coord51(7),y_coord51(7),'7','FontSize',16) text(x_coord51(8),y_coord51(8),'8','FontSize',16) text(x_coord51(9),y_coord51(9),'9','FontSize',16) text(x_coord51(10),y_coord51(10),'10','FontSize',16) num51 = choosedialog; num5 = str2double(num51);

x_coord5 = x_coord51(num5); y_coord5 = y_coord51(num5);

Flimit5 = x_coord5; if Flimit5 > 11 condition5 = 0; else condition5 = 1; end close;

HVrX5 = y_coord5; Fq5 = x_coord5;

%%%%%%%%%%%%%%%%%%% Set 1Y %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 1Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F1,HV1Y,'linewidth',0.5) hold on loglog(F1,SHV1Y,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 1Y') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord61,x_coord61] = findpeaks(SHV1Y,F1,'NPeaks',10,'SortStr','descend'); plot(x_coord61,y_coord61,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord61(1),y_coord61(1),'1','FontSize',16) text(x_coord61(2),y_coord61(2),'2','FontSize',16) text(x_coord61(3),y_coord61(3),'3','FontSize',16) text(x_coord61(4),y_coord61(4),'4','FontSize',16) text(x_coord61(5),y_coord61(5),'5','FontSize',16) text(x_coord61(6),y_coord61(6),'6','FontSize',16) text(x_coord61(7),y_coord61(7),'7','FontSize',16) text(x_coord61(8),y_coord61(8),'8','FontSize',16) text(x_coord61(9),y_coord61(9),'9','FontSize',16) text(x_coord61(10),y_coord61(10),'10','FontSize',16) num61 = choosedialog; num6 = str2double(num61); x_coord6 = x_coord61(num6); y_coord6 = y_coord61(num6);

Flimit6 = x_coord6; if Flimit6 > 11 condition6 = 0; else condition6 = 1; end close;

HVrY1 = y_coord6; Fq6 = x_coord6;

%%%%%%%%%%%%%%%%%%% Set 2Y %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 2Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F2,HV2Y,'linewidth',0.5) hold on loglog(F2,SHV2Y,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 2Y') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord71,x_coord71] = findpeaks(SHV2Y,F2,'NPeaks',10,'SortStr','descend'); plot(x_coord71,y_coord71,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord71(1),y_coord71(1),'1','FontSize',16) text(x_coord71(2),y_coord71(2),'2','FontSize',16) text(x_coord71(3),y_coord71(3),'3','FontSize',16) text(x_coord71(4),y_coord71(4),'4','FontSize',16) text(x_coord71(5),y_coord71(5),'5','FontSize',16) text(x_coord71(6),y_coord71(6),'6','FontSize',16) text(x_coord71(7),y_coord71(7),'7','FontSize',16) text(x_coord71(8),y_coord71(8),'8','FontSize',16) text(x_coord71(9),y_coord71(9),'9','FontSize',16) text(x_coord71(10),y_coord71(10),'10','FontSize',16) num71 = choosedialog; num7 = str2double(num71); x_coord7 = x_coord71(num7); y_coord7 = y_coord71(num7);

Flimit7 = x_coord7; if Flimit7 > 11 condition7 = 0; else condition7 = 1; end close;

HVrY2 = y_coord7; Fq7 = x_coord7;

%%%%%%%%%%%%%%%%%%% Set 3Y %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 3Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F3,HV3Y,'linewidth',0.5) hold on loglog(F3,SHV3Y,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 3Y') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord81,x_coord81] = findpeaks(SHV3Y,F3,'NPeaks',10,'SortStr','descend');

plot(x_coord81,y_coord81,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord81(1),y_coord81(1),'1','FontSize',16) text(x_coord81(2),y_coord81(2),'2','FontSize',16) text(x_coord81(3),y_coord81(3),'3','FontSize',16) text(x_coord81(4),y_coord81(4),'4','FontSize',16) text(x_coord81(5),y_coord81(5),'5','FontSize',16) text(x_coord81(6),y_coord81(6),'6','FontSize',16) text(x_coord81(7),y_coord81(7),'7','FontSize',16) text(x_coord81(8),y_coord81(8),'8','FontSize',16) text(x_coord81(9),y_coord81(9),'9','FontSize',16) text(x_coord81(10),y_coord81(10),'10','FontSize',16) num81 = choosedialog; num8 = str2double(num81); x_coord8 = x_coord81(num8); y_coord8 = y_coord81(num8);

Flimit8 = x_coord8; if Flimit8 > 11 condition8 = 0; else condition8 = 1; end close;

HVrY3 = y_coord8; Fq8 = x_coord8;

%%%%%%%%%%%%%%%%%%% Set 4Y %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 4Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F4,HV4Y,'linewidth',0.5) hold on loglog(F4,SHV4Y,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 4Y') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord91,x_coord91] = findpeaks(SHV4Y,F4,'NPeaks',10,'SortStr','descend'); plot(x_coord91,y_coord91,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord91(1),y_coord91(1),'1','FontSize',16) text(x_coord91(2),y_coord91(2),'2','FontSize',16) text(x_coord91(3),y_coord91(3),'3','FontSize',16) text(x_coord91(4),y_coord91(4),'4','FontSize',16) text(x_coord91(5),y_coord91(5),'5','FontSize',16) text(x_coord91(6),y_coord91(6),'6','FontSize',16) text(x_coord91(7),y_coord91(7),'7','FontSize',16) text(x_coord91(8),y_coord91(8),'8','FontSize',16) text(x_coord91(9),y_coord91(9),'9','FontSize',16) text(x_coord91(10),y_coord91(10),'10','FontSize',16) num91 = choosedialog; num9 = str2double(num91); x_coord9 = x_coord91(num9); y_coord9 = y_coord91(num9);

Flimit9 = x_coord9; if Flimit9 > 11 condition9 = 0; else condition9 = 1; end close;

HVrY4 = y_coord9; Fq9 = x_coord9;

%%%%%%%%%%%%%%%%%%% Set 5Y %%%%%%%%%%%%%%%%%%%%%% figure('color','white','name','Set 5Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) loglog(F5,HV5Y,'linewidth',0.5) hold on loglog(F5,SHV5Y,'R','Linewidth',1) xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 5Y') axis([0.1 100 0.1 inf]) grid on

%Acquire user's inputs [y_coord01,x_coord01] = findpeaks(SHV5Y,F5,'NPeaks',10,'SortStr','descend'); plot(x_coord01,y_coord01,'pr','MarkerSize',5,'MarkerFaceColor','r') text(x_coord01(1),y_coord01(1),'1','FontSize',16) text(x_coord01(2),y_coord01(2),'2','FontSize',16) text(x_coord01(3),y_coord01(3),'3','FontSize',16) text(x_coord01(4),y_coord01(4),'4','FontSize',16) text(x_coord01(5),y_coord01(5),'5','FontSize',16) text(x_coord01(6),y_coord01(6),'6','FontSize',16) text(x_coord01(7),y_coord01(7),'7','FontSize',16) text(x_coord01(8),y_coord01(8),'8','FontSize',16) text(x_coord01(9),y_coord01(9),'9','FontSize',16) text(x_coord01(10),y_coord01(10),'10','FontSize',16) num01 = choosedialog; num0 = str2double(num01); x_coord0 = x_coord01(num0); y_coord0 = y_coord01(num0);

Flimit0 = x_coord0; if Flimit0 > 11 condition0 = 0; else condition0 = 1; end close;

HVrY5 = y_coord0; Fq0 = x_coord0;

%%%%%%%%%%%%% Combine Subplot of HVX Ratio %%%%%%%%%%%

figure('color','white','name','Sets 1X-5X','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) subplot(1,5,1) loglog(F1,HV1X,'B','LineWidth',1) hold on loglog(F1,SHV1X,'R','LineWidth',2) if condition1 == 1 loglog(Fq1,HVrX1,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd11 = 1/Fq1; else Fq1 = '-'; HVrX1 = '-'; Prd11 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 1X') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,2) loglog(F2,HV2X,'B','LineWidth',1) hold on; loglog(F2,SHV2X,'R','LineWidth',2) if condition2 == 1 loglog(Fq2,HVrX2,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd21 = 1/Fq2; else Fq2 = '-'; HVrX2 = '-'; Prd21 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 2X') axis([0.1 100 0.1 100]) grid on

subplot(1,5,3) loglog(F3,HV3X,'B','LineWidth',1) hold on loglog(F3,SHV3X,'R','LineWidth',2) if condition3 == 1 loglog(Fq3,HVrX3,'ro','MarkerSize',5,'MarkerFaceColor','y') Prd31 = 1/Fq3; else Fq3 = '-'; HVrX3 = '-'; Prd31 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 3X') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,4)

loglog(F4,HV4X,'B','LineWidth',1) hold on loglog(F4,SHV4X,'R','LineWidth',2) if condition4 == 1 loglog(Fq4,HVrX4,'ro','MarkerSize',5,'MarkerFaceColor','y') Prd41 = 1/Fq4; else Fq4 = '-'; HVrX4 = '-'; Prd41 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 4X') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,5); loglog(F5,HV5X,'B','LineWidth',1) hold on loglog(F5,SHV5X,'R','LineWidth',2) if condition5 == 1 loglog(Fq5,HVrX5,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd51 = 1/Fq5; else Fq5 = '-'; HVrX5 = '-'; Prd51 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VX Ratio') title ('H/VX Spectra SET 5X') axis([0.1 100 0.1 inf]) grid on saveas(gcf,'hvXratio.emf');

%%%%%%%%%%%%% Combine Subplot of HVY Ratio %%%%%%%%%%% figure('color','white','name','Sets 1Y-5Y','Position',[50 50 scrsz(3)*0.85 scrsz(4)*0.8]) subplot(1,5,1) loglog(F1,HV1Y,'B','LineWidth',1) hold on loglog(F1,SHV1Y,'R','LineWidth',2) if condition6 == 1 loglog(Fq6,HVrY1,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd61 = 1/Fq6; else Fq6 = '-'; HVrY1 = '-'; Prd61 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 1Y') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,2) loglog(F2,HV2Y,'B','LineWidth',1) hold on; loglog(F2,SHV2Y,'R','LineWidth',2) if condition7 == 1 loglog(Fq7,HVrY2,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd71 = 1/Fq7; else Fq7 = '-'; HVrY2 = '-'; Prd71 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 2Y') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,3) loglog(F3,HV3Y,'B','LineWidth',1) hold on loglog(F3,SHV3Y,'R','LineWidth',2) if condition8 == 1 loglog(Fq8,HVrY3,'ro','MarkerSize',5,'MarkerFaceColor','y') Prd81 = 1/Fq8; else Fq8 = '-'; HVrY3 = '-'; Prd81 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 3Y') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,4) loglog(F4,HV4Y,'B','LineWidth',1) hold on loglog(F4,SHV4Y,'R','LineWidth',2) if condition9 == 1 loglog(Fq9,HVrY4,'ro','MarkerSize',5,'MarkerFaceColor','y') Prd91 = 1/Fq9; else Fq9 = '-'; HVrX41 = '-'; Prd91 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 4Y') axis([0.1 100 0.1 inf]) grid on

subplot(1,5,5); loglog(F5,HV5Y,'B','LineWidth',1)

hold on loglog(F5,SHV5Y,'R','LineWidth',2) if condition0 == 1 loglog(Fq0,HVrY5,'ro','MarkerSize',5,'MarkerFaceColor','y'); Prd01 = 1/Fq0; else Fq0 = '-'; HVrY5 = '-'; Prd01 = '-'; end xlabel('Frequency (Hz)') ylabel('H/VY Ratio') title ('H/VY Spectra SET 5Y') axis([0.1 100 0.1 inf]) grid on saveas(gcf,'hvYratio.emf');

%%%%%%%%%%%%%%% Data export for set1X-5X, 1Y-5Y %%%%%%%%%%%%%%% %f = figure('Position',[100 100 1000 100]); %dat = {HVrX1,HVrX2,HVrX3,HVrX4,HVrX5,HVrY1,HVrY2,HVrY3,HVrY4,HVrY5;... % Fq1, Fq2, Fq3, Fq4, Fq5,Fq6, Fq7, Fq8, Fq9, Fq0;... % Prd11, Prd21, Prd31, Prd41, Prd51,Prd61, Prd71, Prd81, Prd91, Prd01;... % }; %columnname = {'Set1X','Set2X', 'Set3X','Set4X', 'Set5X','Set1Y','Set2Y', 'Set3Y','Set4Y', 'Set5Y',}; %rowname = {'H/VX Ratio', 'Frequency', 'Period'}; % %t = uitable('Parent',f, 'Data', dat, 'ColumnName',... % columnname, 'RowName', rowname, 'Position',[0 0 1000 80]); %saveas(gcf,'table.emf'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% f = figure('Position',[100 100 1000 100]); HVrXave = (HVrX1+HVrX2+HVrX3+HVrX4+HVrX5)./5; HVrYave = (HVrY1+HVrY2+HVrY3+HVrY4+HVrY5)./5; Fqxave = (Fq1+Fq2+Fq3+Fq4+Fq5)./5; Fqyave = (Fq6+Fq7+Fq8+Fq9+Fq0)./5; Prdxave = (Prd11+Prd21+Prd31+Prd41+Prd51)./5; Prdyave = (Prd61+Prd71+Prd81+Prd91+Prd01)./5; dat = {HVrX1,HVrX2,HVrX3,HVrX4,HVrX5,HVrY1,HVrY2,HVrY3,HVrY4,HVrY5,HVrXave,H VrYave;... Fq1, Fq2, Fq3, Fq4, Fq5,Fq6, Fq7, Fq8, Fq9, Fq0,Fqxave, Fqyave;... Prd11, Prd21, Prd31, Prd41, Prd51,Prd61, Prd71, Prd81, Prd91, Prd01, Prdxave ,Prdyave;... }; columnname = {'Set1X','Set2X', 'Set3X','Set4X', 'Set5X','Set1Y','Set2Y', 'Set3Y','Set4Y', 'Set5Y',... 'Average X', 'Average Y',}; rowname = {'H/V Ratio', 'Frequency', 'Period'}; t = uitable('Parent',f, 'Data', dat, 'ColumnName',... columnname, 'RowName', rowname, 'Position',[0 0 1000 100]);

saveas(gcf,'table.emf');

%%%%%%%%%%%%%% END %%%%%%%%%%%%%%

APPENDIX E

Table E.1: Calculation of weight on each floor for building at SMK Ranau

2 Slab Area = 450.2 m 2 Slab thickness = 0.15 m 3 Volume = 68 m 3 Density of concrete = 24 kN/m Slab Weight = 1620 kN Beam Weight = 1250 kN Column Weight = 226 kN Brick wall Weight = 800 kN 2 Live load = 2 kN/m = 900 kN 2 Dead load = 4 kN/m = 1800 kN Total = 6596 kN = 7000 kN

Table E.2: Calculation of weight on each floor for building at Hospital Ranau

2 Slab Area = 250 m 2 Slab thickness = 0.15 m 3 Volume = 37.5 m 3 Density of concrete = 24 kN/m Slab Weight = 900 kN Beam Weight = 800 kN Column Weight = 105 kN Brick wall Weight = 500 kN 2 Live load = 2 kN/m = 500 kN 2 Dead load = 4 kN/m = 1000 kN = 3805 kN = 4000 kN

APPENDIX F

0.6 0.4 0.3957 0.2 0 -0.2 -0.4 -0.6 Acceleration (g) Acceleration 0 10 20 30 40 Time (sec) Figure F.1: Ground surface acceleration time history for ground type C in EW-direction in SMK Ranau

0.4 0.2 0 -0.2 -0.3557 -0.4 Acceleration (g) Acceleration 0 10 20 30 40 Time (sec) Figure F.2: Ground surface acceleration time history for ground type C in NS-direction in SMK Ranau

0.4

0.2

-0.2 -0.3495

Acceleration (g) Acceleration -0.4 0 10 20 30 40 Time (sec)

Figure F.3: Ground surface acceleration time history for ground type C in UD-direction in SMK Ranau

0.4 0.2 0 -0.2

-0.4 -0.4417

Acceleration (g) Acceleration -0.6 0 10 20 30 40 Time (sec) Figure F.4: Ground surface acceleration time history for ground type D in EW-direction in SMK Ranau

0.6 0.4 0.2 0 -0.2 -0.4

Acceleration (g) Acceleration -0.4766 -0.6 0 10 20 30 40 Time (sec)

Figure F.5: Ground surface acceleration time history for ground type D in NS-direction in SMK Ranau

0.4 0.3 0.2 0.1 0 -0.1 -0.2

Acceleration (g) Acceleration -0.3 -0.3562 -0.4 0 10 20 30 40 Time (sec)

Figure F.6: Ground surface acceleration time history for ground type D in UD-direction in SMK Ranau

0.4 0.3 0.2 0.1 0 -0.1 -0.2

-0.3 -0.2918 Acceleration (g) Acceleration -0.4 0 10 20 30 40 Time (sec)

Figure F.7: Ground surface acceleration time history for ground type B in EW-direction in Hospital Ranau

0.4 0.3 0.2864 0.2 0.1 0 -0.1 -0.2 Acceleration (g) Acceleration -0.3 0 10 20 30 40 Time (sec) Figure F.8: Ground surface acceleration time history for ground type B in NS-direction in Hospital Ranau

0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.2986 Acceleration (g) Acceleration -0.4 0 10 20 30 40 Time (sec)

Figure F.9: Ground surface acceleration time history for ground type B in UD-direction in Hospital Ranau

0.4 0.3510 0.2

-0.2

Acceleration (g) Acceleration -0.4 0 10 20 30 40 Time (sec)

Figure F.10: Ground surface acceleration time history for ground type D in EW- direction in Hospital Ranau

0.4 0.3 0.2 0.1 0 -0.1 -0.2

-0.3 -0.3089 Acceleration (g) Acceleration -0.4 0 10 20 30 40 Time (sec)

Figure F.11: Ground surface acceleration time history for ground type D in NS- direction in Hospital Ranau

0.2 0.1 0 -0.1 -0.2 -0.2734 Acceleration (g) Acceleration -0.3 0 10 20 30 40 Time (sec) Figure F.12: Ground surface acceleration time history for ground type D in UD- direction in Hospital Ranau

APPENDIX G

Figure G.1: Demonstration of damages corresponding to drift angles on building in SMK Ranau subjected to uniform lateral load in Y-direction (arrow)

Figure G.2: Demonstration of damages corresponding to drift angles on building in Hospital Ranau subjected to uniform lateral load in Y-direction (arrow)