Downtime Estimation of Lifelines After an Earthquake

Paciﬁc Earthquake Engineering Research Center

University of California, Berkeley

Master Research:

DOWNTIME ESTIMATION OF LIFELINES AFTER AN EARTHQUAKE

Alejandro D´ıaz-DelgadoBragado

Supervised by: Stephen Mahin Gian Paolo Cimellaro i

Many thanks to the University of California, Berkeley, for the opportunity to be able to work in such an inspiring environment and also my home university, BarcelonaTech, for making things convenient. I would also like to show my gratitude to all the professionals that have helped me in any way: S. Mahin, G.P. Cimel- laro & The Resilience Group, L. Johnson, V. Terzic and C. Scawthorn. Abstract

Downtime estimation of lifelines after an earthquake is one of the most important elements in seismic risk management because of the signiﬁcant economic consequences. This research is focused on the development of a empirical model for the estimation of duration of lifeline disruption based on damage data of earthquakes during the last hundred years. First of all a database of lifeline earthquake damage was created with emphasis on the duration of the restoration process. Afterwards, restoration curves are modeled for each lifeline with gamma cumulative distribution functions, based on the average and standard deviation of the duration of lifeline disruption. Future works are also presented in the research in order to eventually continue and improve this study in the future.

Keywords: Downtime, Lifelines, Utilities, Outages, Infrastructures, Power, Wa- ter, Gas, Telecommunications, Restoration curves, Earthquakes. Abstracto

Un aspecto muy importante de la gestiónde riesgos s´ısmicoses la estimacióndel tiempo que estaránlas infraestructuras despuésde un terremoto. Esta investigación se ha basado en el desarrollo de un modelo emp´ırico para estimar dicho tiempo, basándoseen informacióny datos sobre el dañorecibido por parte de las diferentes infraestructuras tras diferentes sismos ocurridos en los últimoscien años. Primero de todo se ha creado una base de datos analizando el tiempo que las infraestructuras han estado inoperativas durante cada terremoto. Posteriormente se han desarrollado curvas de recuperaciónpara cada infrastructura a partir de distribuciones gamma, basándoseen la media y la desviaciónt´ıpicade las duraciones de las distintas inter- rupciones. Finalmente se ha incluido los próximospasos que se podr´ıanseguir para continuar y mejorar este estudio en el futuro. Abstracte

Un aspecte molt important de la gestióde r´ıscoss´ısmicsésl’estimaciódel temps que quedarien les infraestructures sense oferir servei a màximacapacitat. En aquest estudi s’ha desenvolupat un model emp´ıricper a estimar aquest temps sense servei, basant-se en dades sobre l’inoperabilitat de diferents serveis durant terratrèmolsque han succe¨ıtdurant els últimscent anys. El primer pas ha estat crear una base de dades analizant el temps de baixa que ha estat cada servei durant cadascúndels sismes. Posteriorment s’han desenvolupat corbes de recuperacióper a cada servei a partir de distribucions gamma, que depenen de la mitja i la desviaciót´ıpicade totes les dades. A més,s’ha inclòsels propers passos que es podrien dur a terme per a continuar i millorar aquesta investigacióen un futur. Contents

List of Figures x

List of Tables xi

1 Introduction and Motivations 1

2 State of the art 3 2.1 Comerio 2005 ...... 3 2.2 Terzic, V. et al. 2015 ...... 6 2.3 Lifeline interdependency study of the City and County of San Francisco 9 2.4 REDiTM ...... 11 2.5 Dong et al. 2014 ...... 13 2.6 Nojima et al. 2002 ...... 14 2.7 Conclusion of the state of art ...... 14

3 Database 16 3.1 Earthquakes analyzed ...... 17 3.1.1 Kanto 1923, Japan ...... 20 3.1.2 Valdivia 1960, Chile ...... 20 3.1.3 Alaska 1964, U.S.A...... 21 3.1.4 Niigata 1964, Japan ...... 22 3.1.5 Tokachi-oki 1968, Japan ...... 22 3.1.6 San Fernando 1971, U.S.A...... 23 3.1.7 Oﬀ-Miyagi 1978, Japan ...... 23 3.1.8 El Asnam 1980, Algeria ...... 24 3.1.9 Nihonkai-Chubu 1983, Japan ...... 24 3.1.10 Michoacan 1985, Mexico ...... 25 3.1.11 Loma Prieta 1985, U.S.A...... 26 3.1.12 Luzon 1990, Philippines ...... 27

v CONTENTS vi

3.1.13 Kushiro-oki 1993, Japan ...... 27 3.1.14 Northridge 1994, U.S.A...... 27 3.1.15 Hokkaido Toho-oki 1994, Japan ...... 28 3.1.16 Sanriku 1994, Japan ...... 29 3.1.17 Kobe 1995, Japan ...... 30 3.1.18 Izmit 1999, Turkey ...... 30 3.1.19 Chi-Chi 1999, Taiwan ...... 31 3.1.20 Arequipa 2001, Peru ...... 32 3.1.21 Nisqually 2001, U.S.A...... 32 3.1.22 Alaska 2002, U.S.A...... 33 3.1.23 Bam 2003, Iran ...... 33 3.1.24 Niigata 2004, Japan ...... 34 3.1.25 Maule 2010, Chile ...... 35 3.1.26 Darﬁeld 2010, New Zealand ...... 36 3.1.27 Christchurch 2011, New Zealand ...... 36 3.1.28 Tohoku 2011, Japan ...... 37 3.1.29 Samara 2012, Costa Rica ...... 38 3.1.30 Napa 2014, U.S.A...... 38 3.1.31 Illapel 2015, Chile ...... 39 3.2 Creation of the database ...... 39

4 Restoration curves 42 4.1 Diﬀerent types of approaches ...... 42 4.1.1 Exponential distribution ...... 43 4.1.2 Lognormal distribution ...... 44 4.1.3 Gamma distribution ...... 45 4.1.4 Modiﬁed gamma distribution ...... 46 4.2 Development of the restoration curves ...... 47 4.3 Restoration curves for each lifeline ...... 48 4.3.1 Power systems ...... 48 4.3.2 Water systems ...... 51 4.3.3 Gas systems ...... 53 4.3.4 Telecommunications systems ...... 55

5 Conclusions & Future work 57 5.1 Conclusions of the research ...... 57 5.2 Future work ...... 60 5.2.1 Increase the database ...... 60 CONTENTS vii

5.2.2 Analysis of the transportation system ...... 60 5.2.3 Interdependencies ...... 60 5.2.4 Standarize the collection of data ...... 61

Appendices 63

A Complete Database 64

B Individual restoration curves 73 List of Figures

1.1 Recovery process of a lifeline back to pre-event functionality ...... 2

2.1 Downtime by Comerio 2005 ...... 5 2.2 PBEE approach by PEER ...... 6 2.3 Workﬂow scheme for the PBEE framework ...... 7 2.4 Restoration curves of the lifelines of the City and County of Sanfran- cisco for a hypothetical 7.9 magnitude earthquake ...... 11 2.5 Main categories of REDiTM to estimate building donwtime ...... 13

3.1 Location of the diﬀerent earthquakes ...... 17 3.2 Epicenter of 1923 Kanto earthquake ...... 20 3.3 Epicenter of 1960 Valdivia earthquake ...... 21 3.4 Epicenter of 1964 Alaska earthquake ...... 21 3.5 Epicenter of 1964 Niigata earthquake ...... 22 3.6 Epicenter of 1968 Tokachi-oki earthquake ...... 23 3.7 Epicenter of 1971 San Fernando earthquake ...... 23 3.8 Epicenter of 1978 Oﬀ-Miyagi earthquake ...... 24 3.9 Epicenter of 1980 El-Asnam earthquake ...... 25 3.10 Epicenter of 1983 Nihonkai-Chubu earthquake ...... 25 3.11 Epicenter of 1985 Michoacan earthquake ...... 26 3.12 Epicenter of 1985 Loma-Prieta earthquake ...... 26 3.13 Epicenter of 1990 Luzon earthquake ...... 27 3.14 Epicenter of 1993 Kushiro-oki earthquake ...... 28 3.15 Epicenter of 1994 Northridge earthquake ...... 28 3.16 Epicenter of 1994 Hokkaido Toho-oki earthquake ...... 29 3.17 Epicenter of 1994 Sanriku earthquake ...... 29 3.18 Epicenter of 1995 Kobe earthquake ...... 30 3.19 Epicenter of 1999 Izmit earthquake ...... 31 3.20 Epicenter of 1999 Chi-Chi earthquake ...... 31

viii LIST OF FIGURES ix

3.21 Epicenter of 2001 Arequipa earthquake ...... 32 3.22 Epicenter of 2001 Nisqually earthquake ...... 33 3.23 Epicenter of 2002 Alaska earthquake ...... 33 3.24 Epicenter of 2003 Bam earthquake ...... 34 3.25 Epicenter of 2004 Niigata earthquake ...... 35 3.26 Epicenter of 2010 Maule earthquake ...... 35 3.27 Epicenter of 2010 Darﬁeld earthquake ...... 36 3.28 Epicenter of 2010 Darﬁeld earthquake ...... 37 3.29 Epicenter of 2011 Tohoku earthquake ...... 37 3.30 Epicenter of 2012 Costa Rica earthquake ...... 38 3.31 Epicenter of 2014 Napa earthquake ...... 39 3.32 Epicenter of 2015 Illapel earthquake ...... 39 3.33 Distribution of the earthquakes by location ...... 40 3.34 Distribution of the earthquakes by the date of occurance and its magnitude ...... 41

4.1 Restoration curve of power systems ...... 49 4.2 Restoration curve of power systems with a semilog x axis ...... 50 4.3 Restoration curve of water systems ...... 51 4.4 Restoration curve of water systems with a semilog x axis ...... 52 4.5 Restoration curve of gas systems ...... 53 4.6 Restoration curve of gas systems with a semilog x axis ...... 54 4.7 Restoration curve of telecommunications systems ...... 55 4.8 Restoration curve of telecommunications systems with a semilog x axis 56

5.1 Distribution of the work done during the research ...... 59

B.1 Restoration curves of the water systems after the earthquake of Alaska 1964 ...... 74 B.2 Restoration curves of the water systems after the earthquake of Niigata 1964 ...... 74 B.3 Restoration curves of the gas systems after the earthquake of Oﬀ- Miyagi 1978 ...... 75 B.4 Restoration curves of the water systems after the earthquake of Mi- choacan 1985 ...... 75 B.5 Restoration curves of the water and gas systems after the earthquake of Loma Prieta 1985 ...... 76 B.6 Restoration curves of the water and gas after the earthquake of Northridge 1994 ...... 76 LIST OF FIGURES x

B.7 Restoration curves of the water and gas after the earthquake of Kobe 1995 ...... 77 B.8 Restoration curves of the water systems after the earthquake of Izmit 1999 ...... 77 B.9 Restoration curves of the water systems after the earthquake of Are- quipa 1999 ...... 78 B.10 Restoration curves of the water systems after the earthquake of Niigata 2004 ...... 78 B.11 Restoration curves of the different lifelines after the earthquake of Maule 2010 ...... 79 B.12 Restoration curves of the power systems after the earthquake of Darfield 2010 ...... 79 B.13 Restoration curves of the different lifelines after the earthquake of Tohoku 2011 ...... 80 B.14 Restoration curves of the water systems after the earthquake of Napa 2014 ...... 80 List of Tables

2.1 Simpliﬁed Method for Estimating Downtime in the UC Berkeley Cam- pus Loss Study ...... 6

3.1 Summary of earthquake database ...... 19

xi Chapter 1

Introduction and Motivations

There is a lot of uncertainty regarding the disruption time of the diﬀerent lifelines after an earthquake. How long is going to be the population without electricity after an earthquake? And without water? Without gas? Without being able to establish connectiong with their relatives? When is the city going to get back to normal? These are frequently asked questions by the stakeholders, leaders, local governments and also the population in regions frequently hit by earthquakes. It is of a great common interest to know how long the disruption of lifeline functions will continue in the following days after a quake. Specially important for the private sectors, since the business disruption and consequent economic losses can be a great threat.

Due to the complexity of the concept of downtime, the are different ways to define it as we will see later. In any case, downtime can be simply defined as the time span between the moment that the earthquake hits, t0 = 0, when the funcionality Qi(t) drops to Qi(0), until the functionality of the utility is completely restored. Figure 1.1 shows grapichally this behavior [11].

One of the main motivations of this research was the fact that there are not a big array of studies of downtime applied to lifelines, whereas there are several researches on downtime of building after an earthquake [63]. The goal was to start a research in a ﬁeld not completetly explored and with large room for improvement.

1 CHAPTER 1. INTRODUCTION AND MOTIVATIONS 2

Figure 1.1: Recovery process of a lifeline back to pre-event functionality

First of all, Chapter 2 presents the state of the art of the diﬀerent studies related to the estimation of downtime to buildings and lifelines, describing all of them and analyzing the possible useful aspects of each study. Afterwards, in Chapter 3, is described the process followed to developed the database with information regarding the disruption of the diﬀerent lifelines, describing all the earthquakes and writing down the references. Finally, in Chapter 4 is described the used method to create the restoration curves with the corresponding results and analysis. The complete database can be checked in Appendix A. Chapter 2

State of the art

Since the concept of downtime is relatively new, there is no a clear procedure of how to address its estimation and also does not exist a common or approved method. Hence in this state-of-art there are presented diﬀerent downtime studies, each one with its singularities and diﬀerent from each other.

2.1 Comerio 2005

Comerio deﬁned downtime as a sum of rational and irrational components in her work Downtime modeling for risk management , Figure 2.1 [18]. The rational components include construction costs and repair time, while irrational components take into account the time needed to mobilize resources and make decisions. These irrational elements are trickier to quantify and it is not very common to deal with them [17] [19], and depend on:

• Financing. It is critical to obtain financing as soon as possible after the hit of a hazard in order to start the clear up and the restoration processes. An example of financing can be found after the 1989 Loma Prieta earthquake at Stanford University in Palo Alto, California. University officials and county inspectors closed approximately 25 buildings out of 400 due to major damage, while another 242 buildings had minor cosmetic or nonstructural damage. The 25 closed buildings represented the 8% of the total campus space; of these, 40% (9 buildings) were repaired quickly but 41% were delayed by financing issues, with constructions starting four to eight years after the earthquake. The 40% quickly repaired buildings were defrayed with internal funds without waiting

3 CHAPTER 2. STATE OF THE ART 4

for federal assistance. The primary reason for the delay on the repair of other buildings was the extensive process required to obtain funding from the Federal Emergency Management Agency (FEMA).

• Relocation of Functions. After a disruptive event is important to try to recover the previous level of activity, and one way to achieve so is to relocate the functions in order to prevent a decrease in the level of production. At the 1989 Loma Prieta earthquake, Stanford University limited program disruption, which was a key to resumption of academic program. Both Stanford Univer- sity and University of California, Berkeley have estimated that they could not continue to operate the campus if more than 15 to 25% of research or teaching space was closed by an earthquake. Fortunately at the 1989 Loma Earthquake, Stanford did not reach this level.

• Management and Manpower. Any institution or business will have limits on the number of trained staff who can contract for professional services, and supervise or administer construction contracts. At some point, the number of repair projects an organization or a region can undertake is limited by the available manpower managers, professional service firms, and construction workers. An example would be what happened in Turkey after the 1999 earthquake, where the small professional engineering community was taxed. In Miami Dade County, after Hurricane Andrew in 1993, construction workers migrated into the region from around the United States, but rebuilding still took two to five years, even when funding was readily available, simply because of the sheer volume of work to be done.

• Economic and Regulatory Uncertainity. The local economic situation is a big incertitude that has to be dealt with. After the 1994 Northridge (Los Angeles) earthquake, householder suffered even more due to the recession at the time. The U.S. Department of Housing and Urban Development (HUD) conducted a survey to owners and tenants. Only 65% of the properties met financial criteria that would allow owners to obtain financing and one third of the owners were likely to abandon their properties or allow bank for closure. This situation is direct result of the economic conditions of the time in the place of the earthquake. Moreover, building damage can also have a big impact on economy, even though if businesses are not affected. After the Nisqually earthquake in Seattle in 2001, small businesses in the affected area were closed even if their buildings were undamaged, but street closure and neighboring buildings were closed so it also affected economically to undamaged businesses, CHAPTER 2. STATE OF THE ART 5

which experienced nearly the same rate of revenue loss as those housed in damaged buildings.

Examples as the presented below are very useful in order to try to estimate downtime in the future. Data from past disasters suggests that the time needed for speciﬁc facility repairs will increase as the overall scale of losses increases. Thus, the time- added impact from each of the four factors described above is typically dependent on the scale of the event.

Figure 2.1: Downtime by Comerio 2005

There are other factors which downtime is dependent on but are not related to the building such as: geotechnical hazard and lifeline interruption (depend on the scale of the event), toxic release (depends on occupancy) and economic hardship (impossible to pre-judge but can be modeled).

In order to estimate downtime it is essential to carry on a loss model. The Paciﬁc Earthquake Engineering Research (PEER) provided a framework for the next generation of Performance-Based Earthquake Engineering (PBEE) models [76]. First of all are selected the ground motions or Intensity Measures (IM), with diﬀerent levels of earthquake hazard. These measures are used in a dynamic analysis software to simulate the range of responses that the structure might experience. Engineering Demand Parameters (EDP) are selected for all the components of the system and are related to Damage Measures (DM) by fragility curves. These damage measures are used to estimate losses such as repair costs, downtime, and casualties, which are the Decision Variables (DV), Figure 2.2. CHAPTER 2. STATE OF THE ART 6

Figure 2.2: PBEE approach by PEER

Comerio workes on a simple model to estimate downtime on buildings depending on the structural damage rating and the key assumption of this study is that the time needed for repair for damages suﬃcient to close a building includes time for professional engineering design and review, contract procurement, as well as construction repair time, Table 2.1 [60].

Table 2.1: Simpliﬁed Method for Estimating Downtime in the UC Berkeley Campus Loss Study

2.2 Terzic, V. et al. 2015

This the paper, Using PBEE in Seismic Design to Improve Performance of Mo- ment Resisting Frames by Base-Isolation, by Terzic et al. (2014) [93] presents the results of a study undertaken to illustrate the ability of simpliﬁed PBEE procedures to identify cost-eﬀective strategies for reducing life-cycle earthquake induced costs CHAPTER 2. STATE OF THE ART 7 during the preliminary stages of seismic design, including business downtime.

Figure 2.3: Workﬂow scheme for the PBEE framework

Performance-based earthquake evaluation (PBEE) methods quantify overall life- cycle earthquake induced cost that account for the initial construction costs as well as the expected future repairs, loss of functionality, and so on. The Paciﬁc Earthquake Engineering Research Center (PEER) has developed a PBEE methodology [55] [56], which ﬁrst selects the ground motion records at the site and performs a simulation (with the software OpenSees) of the range of response that the structure might experience throughout its lifetime. Afterwards, the engineering demand parameters (EDPs) are selected and related by fragility curves to the damage measures (DMs). Then the DMs are used to estimate earthquake losses, which include downtime. The losses are also named decision variables (DVs) and provide valuable information for stakeholders and decision makers. With this methodology one can determine the earthquake-related losses in a given event or over the lifetime on the building.

In this study business downtime is considered as the time required to identify damage, design repairs or upgrades, obtain permits and ﬁnancing, to mobilize supplies and manpower, and to restart operations. To obtain the repair time of each damage cost, it has been used the computer software Performance Assessment Cal- culation Tool (PACT), electronic tool developed by FEMA which help to capture building inventory data, select a given earthquake shaking probability or intensity, apply speciﬁc fragilities and consequences to each building component, and present the results of a large number of runs, or realizations in a logical format and the results are presented in terms of losses and downtime. CHAPTER 2. STATE OF THE ART 8

Once the repair time is obtained, business downtime is calculated considering the order of building repair and accounting for mobilizing factors that can signiﬁcantly delay the start of the building repairs [17] [19]. These mobilization factors are related to the components that are essential for a building’s functionality and the extent of damage of the components and their eﬀect of the business interruption.

The problem with the mobilization factors are the uncertainties associated with the mobilization times of the different activities, since they depend greatly of in- numerable factors as socio-political, economy of the affected area, and size and importance of the affected region. Terzic et al. [94] employ lognormal functions to describe the mobilization times, but among other parameters, these are related to the severity of damage, total number of damage components and building’s loss ratio.

They assumed that in order to calculate downtime to damage repair, some of the tasks can be done in sequence (series), while other can be done simultaneously (parallel):

1. First of all are repaired the structural elements in order to insure overall structural integrity.

2. Then are repaired the stairs, elevators, and electric power in parallel.

3. Finally, simultaneous repairs for piping and interior ﬁnishing; exterior enclo- sure; and mechanical units.

The business downtime due to repair is equal to the maximum repair time for any ﬂoor level [94]. At last, the total business downtime is estimated as the sum of the business downtime due to mobilization factors and due to repair.

Finally, downtime is computed for different hazards and for different retrofits for the building. Terzic et al. 2015 [92] considered three different situations (three different magnitudes for an earthquake) with five different retrofit equipments. The final results showed the importance of mitigating damage to the structure and other critical components, since delays due to building closure and subsequent mobilization issues can significantly increase business downtime. CHAPTER 2. STATE OF THE ART 9 2.3 Lifeline interdependency study of the City and County of San Francisco

The Lifelines Council of the City and County of San Francisco completed the study Lifeline interdependency study of the City and County of San Francisco in 2014 about the interdependencies of the diﬀerent operators within the city limits and its performance following a hypothetical major earthquake of 7.9 magnitude on the San Andreas Fault [46]. A team of earthquake loss estimation experts developed a estimate of the potential ground motions, building damage and losses, and consequences if the 1906 earthquake were to happen with 2006 exposures of people and buildings.

The research is a descriptive summary of the potential damages provided by study participants and should not be used as a predictive model of performance in a future earthquake or other disaster. Eleven lifeline operators managing twelve types of lifelines systems participated in a structured interview process regarding lifeline system impacts and consequences, response and restoration schemes, and dependencies upon other lifelines systems, similar to the plans developed in the states of Washington and Oregon [73]. The principal remarks for the diﬀerent lifelines are:

• Regional roads. The redundancy of the highway network guaranties some level of accessibility to the region, including San Francisco. Crews could start working almost immediately with minor repairs. However, it would be necessary 12 to 18 hours to assess the travel conditions and where more intensive structural inspections would occur. Limited access to major bridges and overpasses during the ﬁrst week until they are inspected.

• City streets. First of all would be cleared all routes necessary for emergency responders and other priority users. An initial damage assessment could take 12 to 24 hours and then more detailed assessments could take weeks. On the other hands, debris clearance would take several months and street reconstruction should be also underway. Demolition of buildings would add more debris and it may take a year to complete all the street clearance and initial repairs.

• Electric power. Outages are expected all over the city. Much of the electric distribution system of San Francisco is underground and challenging to repair. PG&E estimates that for a M7.9 San Andreas earthquake after 48 hours would be restored the 25% of the system, 95% in one week and 100% in a month. CHAPTER 2. STATE OF THE ART 10

• Natural gas. Restoration could not begin until 3 weeks later if the gas transmission service is lost, due to necessary integrity testing before re-pressurizing the lines. PG&E estimates that for a M7.9 San Andreas earthquake it could take up to 6 months for full restoration in San Francisco. • Telecommunications. Although it is coupled with electric power service, system functionality is expected to be high (90%) after the earthquake since there is a strong supply of generators and network redundancy. Despite of that, many of San Francisco cell sites do not have back-up generators so cellular service would be decline in the first 8 hours if power is not restored. • Water. The water system would achieve most of its post-seismic performance objectives within the first 24 hours in a M7.9 San Andreas earthquake. There are about 3 to 4 days of potable water stored in reservoirs in the city. Water grid in streets would be repaired first, with service connections to individual properties repaired later. It is expected to achieve 70% of transmission capacity after 24 hours and reach fully capacity within 30 days of a major earthquake. • Auxiliary water. The regional water transmission system is expected to be able to replenish the Twin Peaks reservoir within 24 hours. It is also possible to pump seawater from San Francisco Bay to the auxiliary water supply system. • Wastewater. No matter the level of damage, the wastewater treatment plants would shut down for inspection immediately following a major earthquake. The majority of the wastewater system is expected to back up and running in 72 hours after electric power is restored. • Transit. Rail and electric trolley bus services would be initially shut down by municipal transit for safety inspections. Back-up power supplies can maintain subway lighting and ventilation for several hours. If the power system is not restored, municipal diesel bus fleet might become the primary form of transit. • Port. Initial inspections would be completed in 72 hours but it would not be fully inspected until 30 to 40 days after the earthquake. It would likely take a month to several months to relocate essential functions, stabilize any ground failure, building and pier damage areas and reopen key access points. Repairs to the seawall and reconstruction of damaged waterfront piers and buildings could take years to complete. • Airport. After a major earthquake, the main priorities of an airport are life safety, facility, assessments, business resumption, and runaway repair. This CHAPTER 2. STATE OF THE ART 11

last priority is critical to both emergency use and restoration of commercial operations. SFO has signiﬁcant power redundancy and emergency generator power capabilities.

• Fuel. Operators would shut down many critical system components for a minimum of 24 to 48 hours for inspection and restoration. There may be enough supply in the system to cover a short-term shutdown but there could be a shortage of certain fuel products. If a major reﬁnery was shut down, waterborne fuel cargo (produced elsewhere) could be brought into ports and fed into pipelines and other system distribution points. Minor repairs to the critical fuel system could take days to week, and major repairs could take months.

Figure 2.4: Restoration curves of the lifelines of the City and County of Sanfrancisco for a hypothetical 7.9 magnitude earthquake

The study revealed that some of the lifelines systmes are closely coupled and inderdependent with the performance and restoration of the other lifelines systems. This fact could be responsilbe for signiﬁcant delays of lifelines systems where the infrastuctures have only experienced moderate damage.

2.4 REDiTM

Resilience-based Earthquake Design Initiative (REDiTM) is a tool developed by Arup in 2013 in order to provide owners and other stakeholders a framework for im- CHAPTER 2. STATE OF THE ART 12 plementing resilience-based earthquake design, a holistic beyond-code design, planning and assessment approach for achieving much higher performance [3].

REDiTM is a set of guidelines with specific criteria which aim to minimize building damage and also promote contingency planning for utility disruption. The used framework establishes three rating tiers (Platinium, Gold and Silver), each one with resilience objectives which aim to substantially reduce earthquake risks relative to the code objectives for ordinary buildings. REDiTM is based on FEMA P-58, which provides estimates of repair time for earthquake damage, but it does not calculate the downtime of the facilities, which may be much longer than the repair time. REDiTM provides several significant improvements in order to estimate downtime: • Definition of Repair Classes, which describe whether the extent of damage and criticality of various building components will hinder achievement of specific recovery states like re-occupancy, functional recovery, and full recovery. • A modified approach for allocating labor and sequencing repairs. • Estimates of delays to initiation of repairs (impeding factors) based on lessons from past natural disasters and expert opinion. • Estimates of utility disruption for electricity, water, and gas based on data from past earthquakes and predicted regional disruptions for hypothetical future earthquake scenarios published by experts. • Sequential logic for calculating the time required to achieve re-occupancy, functional recovery and/or full recovery due to impeding factors, utility disruption, and building repairs. Although the tool is mainly address to estimate the downtime of buildings and businesses, REDiTM also takes into account somewhat the downtime of lifelines. Operability of the main lifelines are crucial in order to achieve functional and full recovery. Because of that, utility disruption is taken into account as a parameter to estimate the loss assessment of the building and proceed to obtain the downtime of the bussiness. Figure 2.5 shows the different categories that REDiTM takes into account and where ”utility disruption” fits the overall process. Despite its contribution to the total building downtime is not very significant, its study has been very useful to understand the procedure.

Utility disruption curves were developed for a design level earthquake, based on qualitative ﬁtting of restoration curves obtained from previous earthquakes and CHAPTER 2. STATE OF THE ART 13

Building Building Loss Utility disrup- Resilience Downtime Assesment tion

Ambient Org. Resilience Resilience

Figure 2.5: Main categories of REDiTM to estimate building donwtime predicted disruptions from several studies of hypothetical design level earthquake and using the tool HAZUS [15] [41] [42] [83]. The utility disruption curves were plotted based on the number of days required to restore service to customers that lost service immediately after the earthquake and it was assumed that utility disruption would occur.

2.5 Dong et al. 2014

The paper Sustainability of Highway Bridge Networks Under Seismic Hazard is focused on transportation network, which are one of the most critical systems for the economy and society [22]. After an earthquake, the functionality of highway networks can be signiﬁcantly decreased, leading to disastrous eﬀects on the economy. Dong et al. 2014 present a methodology in order to evaluate risk of transportation networks that integrates the probabilities of occurrence of seismic events in a region, vulnerability of civil infrastructure, and consequences.

Subsequent to a disruptive event like an earthquake, the performance of the transportation network might be affected, especially critical aspects as bridges. The traffic volume on a link might be reduced, or travelers might have to follow detours to arrive at their destinations or traffic jam might be expected. All of these possible CHAPTER 2. STATE OF THE ART 14 consequences depend on the damage state of the bridges on the link.

Dong et al. 2014 present a simple way to estimate downtime of travelers in a transportation network due to seismic hazard, based on [74]:

n 4 ( ) = ∑ ∑ ( ) ⋅ DT t PLDSij ∣IM t dij (2.1) j=1 i=1 where n is the number of links in the transportation network; i stands for the damage state (intact state, minor, moderate, and major damage state) of the link j ; ( ) PLDSij ∣IM t is the conditional probability of the link j being in damage state i after having an earthquake at time t with certain ground motion intensity; and dij is the downtime associated with the damage states of each link.

2.6 Nojima et al. 2002

The study Empirical Estimation of outage and duration of lifeline disruption due to earthquake disaster by N. Nojima and M. Sugito [65] presents two empirical models for simple estimation of lifeline disruption based on the damage records of recent earthquakes, but only earthquakes that took place in Japan.

In this paper, Nojima et al. developed a database of lifeline earthquake damage with emphasis on both physical and functional aspect of restoration process. Then, restoration curves were estimated using the statistical relationship between the number of physical damage and the duration of lifeline disruption.

Nojima et al. modeled the restoration curves with diﬀerent distribution: lognormal distribution, gamma distribution and exponential distribution. Finally, the paper showed that the gamma distribution best model restoration curves. More detailed analysis will be later presented about the distribution used by Nojima et al..

2.7 Conclusion of the state of art

All the examples presented show that downtime is a key component of loss modeling. Since downtime estimation is meaningful to individual, corporates, or insti- tutional owners that depend on speciﬁc physical space for operations. Downtime estimates are equally important for insurance analysts and loss modelers to calculate CHAPTER 2. STATE OF THE ART 15 the economic impacts of natural and man-made disasters.

Despite its importance, there is not a global understanding of how it should be estimated and at the moment of this present work there are several ways to study it: just taking into account repair time, others also consider mobilization times with a lot of uncertainties and even these uncertainties are estimated in very diﬀerent ways. For that very reason there is still a long path to go all over in this study and can be addressed in very diﬀerent ways.

The proposal of my work will be to try to estimate downtime for infrastructures and lifelines, all of them with a similar procedure. For this purpose, it has been very inspirating the works of Comerio 2005, Lifeline interdependency study of the City and County of San Francisco, REDiTM and Nojima et al. 2002:

• Comerio 2005. The way she described downtime helped me to avoid to relate the concept of downtime just with the reparation time.

• Lifeline interdependency study of the City and County of San Francisco. Very important to understand all the key factors of each lifeline and also is very clear the explanation of the interdependencies among all the diﬀerent lifelines.

• REDiTM. How they developed the database was very useful in order to develope mine.

• Nojima et al. 2002. They compared diﬀerent ways to model restorarion curves and determined that the best one is gamma distribution, although exponential distribution is appropiate too. Chapter 3

Database

The first step in the research was to create a database with the most important data from all the different earthquakes. The goal of this process was to gather as much information as possible from a relevant number of earthquake, so the final result would be consistent. The quality and completeness of the database are extremely important in determining the reliability of the resulting empirical fragility function, and are often linked to the method of collection of the empirical data.

Initially, the database stocked up different qualitative and quantitative parameters for each event, but then the number of inputs (data analyzed) was simplified in favor of further earthquakes analyzed. This change on the initial strategy was due to the fact that studies from different earthquakes, different locations and different periods present different data: there is not a clear procedure to analyze the behavior of lifelines after and earthquake, there is not a standard, and this fact supposed an initial obstacle. In earlier years the investigators concentrated on the fundamental lessons to be learnt regarding the performance of the built environment (mainly buildings but also lifelines) under strong shaking, and information such as the economic losses and the building and utility damage distribution were of less importance. However, has been a shift in this sense and performance study of utilities are becoming more importance due to its relevance for the economy of the region.

All the information gathered has been extracted from renowned authors or of- ﬁcial institutions. It is important to remark that the further in the past, the less information was available about the performance of the lifelines [9].

16 CHAPTER 3. DATABASE 17

Alaska Alaska 2002 1964 PPq ¡ ¡ Nihonkai-chubu Nisqually Napa 1983 Hokkaido Toho-oki P 2014 2001 PPq C ¢ 1994 Tokachi-oki ¡ Izmit Niigata C Kushiro-oki 1968 Loma Prieta Northridge ¢ ¨¨ 1993 1989 PPq ¡ 1994 1999 H 2004 CW 1 © Hj @@R Sanriku Tokachi-oki ¨* 1HY 1994 1968 San Fernando Costa Rica ¨ Niigata H Kobe 1971 2012 El Asnam 1964 PiP 1995 Kanto Oﬀ-Miyagi 1923 1978 © 1980 Taiwan Bam HYH 1999 Michoacan 2003 1985 Arequipa Philippines 2001 1990 © Illapel 2015 Maule X PPq 2010 XXXz Darﬁeld : 2010 Valdivia P 1Pq 1960 Christchurch 2011

Figure 3.1: Location of the diﬀerent earthquakes

3.1 Earthquakes analyzed

In order to obtain an heterogeneous database, the scheme has been to consider all damaging earthquakes for which ﬁeld reconnaissance has been reported and published, irrespective of the size, location or damage levels, since all of these variables are relevant.

Figure 3.1 shows the location of the all different earthquakes analyzed. A total of 31 earthquakes have been analyzed, but each earthquake could have affected more than one system of utilities, fact that has also been taken into account. It is remarkable that the 90% of the earthquakes analyzed in this research, took place along the Ring of Fire, which is a string of volcanoes and sites of seismic activity, or earthquakes, around the edges of the Pacific Ocean. The USGS stated that the 90% of the earthquakes of the history took place along this area. The other 10% of the earthquakes took place along the Alpide belt, an area that extends from Mediter- ranean region, eastward through Turkey, Iran, and northern India. CHAPTER 3. DATABASE 18

Table 3.1 lists all the earthquakes reviewed during this research (a more thorough table is presented in the Appendix A - Complete Database). As stated below, there were, inevitably, a number of other damaging earthquakes during this long period that have not been included on this study, generally because no engineering damage reports for these events were obtained or simply that there was no notable damage reported on the utilities or lifelines. The number and variety of those included are suﬃcient to provide some very useful illustrations and conclusions for all of the four diﬀerent lifelines analayzed in this research:

• Power systems: 63 inputs.

• Water systems: 84 inputs.

• Gas systems: 47 inputs.

• Telecommunications systems: 34 inputs CHAPTER 3. DATABASE 19

Table 3.1: Summary of earthquake database

Lifeline Location Year M w Reference E W G T Kanto, Japan 1923 7,8 Kawata 1996 EW Valdivia, Chile 1960 9,5 Kausel 1960 E W G T Alaska, U.S.A. 1964 9,2 Eckel 1967 E W G Niigata, Japan 1964 7,6 Dynes et al. 1964 EG Tokachi-oki, Japan 1968 8,3 Katayama et al. 1977 E G T San Fernando, U.S.A. 1971 6,6 Jennings et al. 1971 E W G T Off-Miyagi, Japan 1978 7,4 Katayama 1980 W El Asnam, Algeria 1980 7,1 Nakamura et al. 1983 E W G Nihonkai-Chubu, Japan 1983 7,8 Hamada et al. 1985 E W G T Michoacan, Mexico 1985 8,1 O’Rourke 1996 E W G T Loma Prieta, U.S.A. 1989 6,9 Schiff 1998 E W T Luzon, Philippines 1990 7,8 Sharpe 1994 E W G Kushiro-oki, Japan 1993 7,8 Yamazaki et al. 1995 E W G T Northridge, U.S.A. 1994 6,7 Lund et al. 1995 EW Hokkaido Toho-oki, Jp. 1994 8,2 Yamazaki et al. 1995 EW Sanriku, Japan 1994 7,5 Yamazaki et al. 1995 E W G T Kobe, Japan 1995 6,9 Kuraoka et al. 1996 E W G T Izmit, Turkey 1999 7,4 Gillies et al. 2001 E W G T Chi-Chi, Taiwan 1999 7,6 MCEER 2000 EW Arequipa, Peru 2001 8,4 Edwards et al. 2001 E Nisqually, U.S.A. 2001 6,8 Reed et al. 2003 G Alaska, U.S.A. 2002 7,9 EERI 2003a E W T Bam, Iran 2003 6,6 Ahmadizadeh et al. 2004 E W G Niigata, Japan 2004 6,6 Scawthorn 2006 E W G T Maule, Chile 2010 8,8 Evans et al. 2011 E W G Darfield, New Zealand 2010 7,1 Knight et al. 2012 E W G Christchurch, N. Z. 2011 6,3 Giovanazzi et al. 2011 E W G T Tohoku, Japan 2011 9,0 Nojima 2012 E W T Samara, Costa Rica 2012 7,6 C.N.E. 2012 E W G Napa, U.S.A. 2014 6,0 Scawthorn 2014 EW Illapel, Chile 2015 8,4 ONEMI 2015 CHAPTER 3. DATABASE 20

3.1.1 Kanto 1923, Japan The 1st of September of 1923 a killer earthquake of 7,8 magnitude struck the japanese region of Kanto. There were more than 100.000 deads reported and 400.000 people injured. The port city of Yokohama was completely destroyed, and the surrounding cities of Tokio, Chiba, Kanagawa and Shizuoka were severely damaged as well. The earthquake broke water mains all over the cities, and putting out the ﬁres took nearly two full days until late in the morning of September the 3rd, but the diﬀerent water systems were down more than 40 days. The power restoration of the region was quick enough, and so it was completely restored in a week. On the other hand, it took more than half a year for the full restoration of the gas system, which was severly damage [89].

Epicenter ☀

Figure 3.2: Epicenter of 1923 Kanto earthquake

3.1.2 Valdivia 1960, Chile The Valdivia earthquake was the strongest shaking ever recorded, with a magnitude of 9,5 on the Richter scale and a intensity of XI to XII on the Mercalli scale [51]. This earthquake shook all South America and destroyed the chilean city of Valdivia. More than 5.000 people died and more than 2 million people were forced to leave their homes. The shock was of such a strong magnitude that new lakes sprang and even some rivers shifted their course. After the big shake, a huge tsunami devastated all the coastline, destroying houses, bridges, boats and ports. Despite the power of the earthquake, the Chilean utilities of the region performed quite well, because of the preparation of the country for this kind of hazards. For the purpose of this study, only damage caused by the earthquake has been taken into account. CHAPTER 3. DATABASE 21

Epicenter ☀

Figure 3.3: Epicenter of 1960 Valdivia earthquake

3.1.3 Alaska 1964, U.S.A. The earthquake took place on March 27, 1964, and wrecked or severly hampered all utilities, all forms of transportation, and all communications systems over a very large part of south-central Alaska [28]. Despite of the severity of the shock, 9,2 on the Richter scale, temporary repairs were eﬀected in remarkably short times and full restoration was achieve in two days for the electric grid and two weeks for the water, gas and telecommunication system. The power-generating system was disrupted in many places by vibration damage to equipment and by broken transmission lines; water-supply and sewer lines were also broken in many towns; landslides in An- chorage broke gas-distribution lines in many places, but the main transmission line from the Kenai Peninsula was virtually undamaged; communications systems were silenced almost everywhere by loss of power or by downed lines.

Epicenter ☀

Figure 3.4: Epicenter of 1964 Alaska earthquake CHAPTER 3. DATABASE 22

3.1.4 Niigata 1964, Japan On June 16, 1964, Japan was jarred by the strongest earthquake to hit the country since the Kanto Earthquake of 1923 [23]. The shake, which measured 7,7 on the Richter scale, was felt by over two-third of the main Japanese island of Honshu, but the most aﬀected region was the Niigata prefecture, and the namesake capital. The earthquake destroyed more than 8.000 houses, disrupted all public utilities, severly interrupted all means of communication, and put of commission almost all the land, sea, and air transport facilities. The restoration of the lifelines was a slow process in most of the systems: some region were without power for almost a month and gas restoration took even more than ﬁve months in some parts of the system.

Epicenter ☀

Figure 3.5: Epicenter of 1964 Niigata earthquake

3.1.5 Tokachi-oki 1968, Japan The 1968 Tokachi earthquake occurred on May 16 and was located in the oﬀshore area of Aomori and Hokkaido [49]. The magnitude of this earthquake was put at 8.3 on the Richter scale. The earthquake was responsible for a total of 52 fatalities, 47 of which were in Aomori Prefecture. The primary damage included landslides and the collapse of cliﬀs and houses. Damage was extensive in areas with soft ground, including hilly areas comprised of volcanic deposit, reclaimed land, and low marshy areas. The most damaged lifelines was the gas system, which took up to a month to obtain full restoration. CHAPTER 3. DATABASE 23

Epicenter ☀

Figure 3.6: Epicenter of 1968 Tokachi-oki earthquake

3.1.6 San Fernando 1971, U.S.A. A destructive earthquake struck the northern portion of the Los Angeles metropolitan area on February 9, 1971 [45]. The shock of magnitude 6.6 was not a great quake, but it centered on the northern edge of a metropolitan area of over 8 milions inhabitants. Approximately 400.000 people were subjected to very strong ground shaking, and more than 2 milions persons felt a moderately strong shaking [4] [16]. The gas system and the telecommunications were the lifelines more aﬀected by the quake, with downtime of more than two weeks [68]. The water system was also extremely damaged because of the aﬀectation of the lower San Fernando dam [30].

Epicenter ☀

Figure 3.7: Epicenter of 1971 San Fernando earthquake

3.1.7 Off-Miyagi 1978, Japan The Off-Miyagi earthquake occurred on June 12, 1978, in the northern part of the Japanese island of Honshu [44]. Its magnitude was 7.4 and the epicenter was located in the Pacific Ocean at a point about 100 km east of Sendai, in spite of that CHAPTER 3. DATABASE 24 it took place in the middle of the ocean, the quake was felt along the east coast of the island with an intensity of V on the Mercalli scale. The shock was not as devastating as that observed in Niigata in 1964, but it damaged severely the principal utilities, such as water and gas system, which were not working at full capacity until two and three weeks after the earthquake, respectively [50].

Epicenter ☀

Figure 3.8: Epicenter of 1978 Oﬀ-Miyagi earthquake

3.1.8 El Asnam 1980, Algeria The 10th of October of 1980, an earthquake of magnitude 7.1 and focal depth of 15 km struck the city of El Asnam in northern Algeria. The city was destroyed 26 years earlier by a 6.7 earthquake, and even afther that event, 23,5% of the buildings of the city collapsed during the 1980 quake. More than 6.500 people died after the shock and 9.000 were injured. This earthquake was an example of poor study after the event, so that there was only few informationr regarding the downtime of the waster system, which was inoperative for two weeks [64].

3.1.9 Nihonkai-Chubu 1983, Japan On May 16, 1983, a major earthquake named the Nihonkai-chubu, Japan sea, occurred in the Central Sea of Japan [40]. The quake generated a major local tsunami which was destructive in Japan as well as in Korea. The magnitude of the event was 7,7 and caused extensive damage to dwellings, roads, and vessels along the coastline from Hokkaido to the Niigata area, in the island of Honshu. Most of the earthquake damage to buildings and utilities resulted from ground liquefaction after the shock, moreover the data gathered in this study regarding this event only takes into account CHAPTER 3. DATABASE 25

Epicenter ☀

Figure 3.9: Epicenter of 1980 El-Asnam earthquake damage generated by the earthquake, and not the tsunami. The power grid was restored the day after the event. On the other hand the water and gas system was not fully restored until one month after the earthquake, due to the severe damaged to the ground pipelines [64].

Epicenter ☀

Figure 3.10: Epicenter of 1983 Nihonkai-Chubu earthquake

3.1.10 Michoacan 1985, Mexico A 8,1 magnitude earthquake struck the Metropolitan Mexico City Area on Septem- ber of 1985 [35]. This event was specially important because it took place in the largest city of the world and the disaster left an estimated 5,3 million people without water and electricity, a condition never previously experienced in a major city. This amount of physical damage to the power and water systems as well as the eﬀects this damage had upon the population also represente a very important issue [69] [70]. It took more than one week to restored all the water systems of Mexico City and more than a month for the full restoration of the water distribution system [5]. CHAPTER 3. DATABASE 26

Epicenter ☀

Figure 3.11: Epicenter of 1985 Michoacan earthquake

3.1.11 Loma Prieta 1985, U.S.A. The moderately large, 7,1 on the Richter Scale, Loma Prieta earthquake of Oc- tober 17, 1989, took 63 lives and damaged more than 27.000 structures in Norhern California [8]. It resulted from a slip along a 25-mile segment of the San Andreas fault where it traverses the Santa Cruz Mountains, approximately 60 miles south of San Francisco and Oakland. Because the fault ruptured bilaterally, propagat- ing north and south simultaneously, the duration of shaking was surprisingly short, about 10 seconds [84]. The most severe property damage occurred in Oakland and San Francisco, about 100 kilometer north of the fault segment that slipped on the San Andreas. Several houses were completely destroyed in Oakland and San Fran- cisco, the Bay Bridge collapsed and the gas and water system were harshly damaged and full restoration was achieved in two weeks [99].

Epicenter ☀

Figure 3.12: Epicenter of 1985 Loma-Prieta earthquake CHAPTER 3. DATABASE 27

3.1.12 Luzon 1990, Philippines The July 16, 1990, earthquake is attributed to the Philippines Fault Zone and its major branch, the Digdig Fault [86]. The quake had a magnitude of 7,6 on the Richter scale and caused panic and stampedes all over the country, including the capital, Manila. The restoration process of the electric grid was quite faster and all the systems were restored within a week following the hit of the earthquake. On the other hand, the water distribution system was more aﬀected by the shock and it took over two weeks to achieve full restoration.

Epicenter ☀

Figure 3.13: Epicenter of 1990 Luzon earthquake

3.1.13 Kushiro-oki 1993, Japan On January 15, 1993, a 7,8 magnitude earthquake struck the Kushiro Bay Area with disastrous eﬀects on the highly developed modern urban district in the bay area. This earthquake is of special interest seismologically because it was triggered by the activity of a segment of the main. The performance of the diﬀerent lifelines of the region were extremely succesful and only the water systems were critically damaged, which were without service for up to nine days [97].

3.1.14 Northridge 1994, U.S.A. A magnitude 6,8 earthquake in the community of Northridge in the San Fernando Valley shook the entire Los Angeles metropolitan area on Monday, January 17, 1994 [20] [21]. Moderate damage to the built environment was widespread; severe damage included collapsed buildings, utilities and highway overpasses. A total of 58 deaths CHAPTER 3. DATABASE 28

Epicenter ☀

Figure 3.14: Epicenter of 1993 Kushiro-oki earthquake were attributed to the earthquake by the Los Angeles Coroner [53]. About 1.500 people were admitted to hospitals with major injuries; another 16.000 or so were treated and released. Estimates of the number of people temporarily or permanently displaced because of damage to their houses or apartments ranged from 80.000 to 125.000. Water and gas system, both using pipelines, were the more damaged lifelines, with a disrupted time of more than one month [59]. On the other hand, electric grid and telecommunication system, although damaged too, performed better and the downtime was just one week [13] [90].

Epicenter ☀

Figure 3.15: Epicenter of 1994 Northridge earthquake

3.1.15 Hokkaido Toho-oki 1994, Japan The Hokkaido Toho-oki earthquake had a magnitude of 8,2 and occurred on October 4 in 1994. This quake was of relevant importance because of its unique features: had a different mecanism from those of the largest thrust earthquakes in close regions, was felt in an extremely large area and presented an anomalously large CHAPTER 3. DATABASE 29 peak horizontal acceleration. The different water systems of the region were the most affected utility and countless household did not had water until nine days after the shock. By contrast, the electric grid of the area performed perfectly fine and achieved full restoration just the day after the earthquake, what showed a great resilience [97].

Epicenter ☀

Figure 3.16: Epicenter of 1994 Hokkaido Toho-oki earthquake

3.1.16 Sanriku 1994, Japan The 28th of December of 1994 a 7,5 earthquake hit the northeast Japanese region of Sanriku. The epicenter of the quake was located in the Paciﬁc Ocean at about 180 km east of the coast and the shock was even felt in the capital of the country, Tokio, about 600 km south from the Sanriku region. Power outages were reported but lasted just for one day, however the water systems were more damaged and some towns were more than two weeks without water service [97].

Epicenter ☀

Figure 3.17: Epicenter of 1994 Sanriku earthquake CHAPTER 3. DATABASE 30

3.1.17 Kobe 1995, Japan The January 17, a 6,9 magnitude earthquake struck the Japanese city of Kobe and its surrounding area. The quake, also known as the Hyogoken-Nanbu earhtquake, resulted in more than 6.000 deaths and over 30.000 injuries [2] [52]. One of the most critical consequences after the shock were the fires that happened in the city, since 150.000 buildings were destroyed and left more than 300.000 people homeless. This fact was worsen because the quake inflicted severe damage to the water distribution system, not only disrupting daily life activities and industrial operations but also causing major problems in fighting fires. The earthquake severly damaged all the utilities of the area of Kobe, and with specially harmful with the water and gas system [57]. These system were not fully restored until two months after the event [98]; on the other hand, the electric grid and telecommunications were fully operational just one week after the earthquake [14] [48].

Epicenter ☀

Figure 3.18: Epicenter of 1995 Kobe earthquake

3.1.18 Izmit 1999, Turkey On August 17, 1999, the regions of Kocaeli and Sakaraya, northwestern of Turkey, were struck by a magnitude 7,4 earthquake [37]. These regions are densely populated and include the industrial heartland of the country, which contribute to rise the number of collapses of residential and industrial buildings. The rapid growth in population in this region since the early 1980s had created a strong demand for housing; this resulted in a concentration of multiple level apartment buildings in poor areas of the city, where 18.000 people were killed. There was signiﬁcant damage to the regional lifelines, specially the water network distribution which did not work at its fullest until almost a month after the shock [80] [95]. The gas and electric systems perfomed well during the earthquake, but the pipelines of oil were severely CHAPTER 3. DATABASE 31 damaged, what reported more than $350 million in losses.

Epicenter ☀

Figure 3.19: Epicenter of 1999 Izmit earthquake

3.1.19 Chi-Chi 1999, Taiwan A devastating earthquake of magnitude 7,6 struck the central region of the southeastern asian island of Taiwan on September 21, 1999 [53]. There were several important aftershocks during the following week that caused the collapse of the already damaged structures of the area. Over 2.400 people died and approximately 10.000 buildings and homes collapsed because of the earthquake. In addition, there was widespread destruction and disruption of lifelines, including roads, bridges, communication systems, water and gas systems and electric grid [79]. All of these utilities were not working at full capacity during the following two weeks after the quake, and the power grid was the most damaged, what hindered the business restoration and daily life for the inhabitants of the region [71].

Epicenter ☀

Figure 3.20: Epicenter of 1999 Chi-Chi earthquake CHAPTER 3. DATABASE 32

3.1.20 Arequipa 2001, Peru The Arequipa earthquake or South Peru earthquake, hit the southamerican country the 23rd of June of 2001 with a magnitude of 8,4 on the Richter scale. The quake was felt in all the southern regions of Peru as well as in the Bolivian capital, La Paz, and in the Chilean cities of Arica and Iquique. Lifeline damage was much less than has been seen in events of such magnitude. This was apparently due to the distribution of the population in Peru, with large distances between major population centers and the general lack of lifelines in the rural regions, sparsely populated areas aﬀected by the earthquake. Interestingly, lifeline damage was greater in cities 300 km southeast of the epicenter than it was in cities closer to the epicenter. In those cities, the water distribution network was critically aﬀected and full restoration was not achieved until one month after the hit of the earthquake [29].

Epicenter ☀

Figure 3.21: Epicenter of 2001 Arequipa earthquake

3.1.21 Nisqually 2001, U.S.A. On Frebruary 28, 2001, a magnitude 6,8 earthquake struck western Washington State in the U.S.A., the epicenter was approximately 60 km southwest of the city of Seattle [78]. Although the damage was less than observed at most large urban earthquakes, serious damage was found in Olympia, Seattle, Tacoma and elsewhere. About 400 people were injured after the shock and major damage in the Seattle- Tacoma-Olympia area was reported. Damage types observed after the earthquake included: structural, nonstructural, contents, lifelines, landslides, liquefaction, lateral spreading, sand boils, and settlement. The electric grid was severely damaged and outages were common during the ﬁrst week following the quake and full restoration was not achieved until six days after [77]. CHAPTER 3. DATABASE 33

Epicenter ☀

Figure 3.22: Epicenter of 2001 Nisqually earthquake

3.1.22 Alaska 2002, U.S.A. The 3rd of November, 2002, a 7,9 earhtquake hit the state of Alaska, U.S.A., at the Denali fault and was the largest strike-slip earthquake in Norht America in almost 150 years [24]. Due to its remote location, the large earthquake resulted in no loss of life and relatively minor damage to the built environment. With a few exceptions, only slight damage occurred to buried lifelines, largely because they were located some distance away from the earthquake. The gas pipelines managed by Alyeska, the operator of the Trans-Alaska Pipeline, decided to close the distribution network for inspections and initial repairs, which were made at a fast pace and the pipeline was reopened 66 hours later.

Epicenter ☀

Figure 3.23: Epicenter of 2002 Alaska earthquake

3.1.23 Bam 2003, Iran A powerful earthquake of magnitude 6,6 struck the southeastern region of Iran on December 26, 2003 [1]. The historical city of Bam was critically affected by CHAPTER 3. DATABASE 34 the earthquake, killing more than 40.000 and injuring over 25.000 people [7]. The construction quality is not generally competent in Iran, particularly in rural areas, with small towns and villages. This makes the buildings in these regions highly vulnerable to quakes, because no engineering considerations are used to project these buildings. Consequently, most of the adobe bricks buildings of the region collapsed after the shock. The water piping system in the region was critically affected and it took over a week to achieve full restoration. The power supply of the region was also harshly affected because a big substation close to the city of Bam was severely damaged and failed to operate continuosly, it took about four days for this substation to recover its regular level of service.

Epicenter ☀

Figure 3.24: Epicenter of 2003 Bam earthquake

3.1.24 Niigata 2004, Japan The 2004 Niigata earthquake took place in the central-western Japanese region of Niigata on October 23 and had a magnitude of 6,6 on the Richter scale, the most significant earthquake to affect Japan since the 1995 Kobe earthquake [25]. Forty people were killed, almost 3.000 were injured, and numerous landslides destroyed entire upland villages. After landslides, damage to lifelines was the most notable consequence of this earthquake. Roads, highways and rail lines were critically damaged at several locations [85]. The quake cut off all power, gas, water and telephone service to the city of Ojiya. The electric power was restored steadily but it took more than one week to provide electric service to all the households of the region. Damage was more extensive on the water and gas distribution systems, where full restoration was not achieved until late November of 2004 [43] [81]. CHAPTER 3. DATABASE 35

Epicenter ☀

Figure 3.25: Epicenter of 2004 Niigata earthquake

3.1.25 Maule 2010, Chile

th The 27 of February an Mw 8,8 earthquake struck the central south region of Chile, aﬀecting an area with a population of more than eight millions, including the cities of Santiago, Valparaiso and Concepcion [10]. After the earthquake there was also a tsunami, but the data gathered on this study referred to the Maule 2010 earthquake only takes into account damage generated by the quake. The earthquake caused damage to highways, raildroads, lifelines, ports, and airports due to ground shaking and liquefaction. The transmission network of the electric grid performed reasonably well and was ready to provide power 24 hours after the main shock, but it was not fully restored after two weeks following the quake [26] [34]. The telecommunications systems, both landline and wireless, were bedeviled by commercial power outages, equipment failures, and loss of reserve power in most distributed network facilities, therefore there were congestion problems during the two weeks preceding the earthquake. Water and gas system was extremely damaged, afecting more than four million people, and the supply companies were not able to provide regular service to every household until early May [32].

Epicenter ☀

Figure 3.26: Epicenter of 2010 Maule earthquake CHAPTER 3. DATABASE 36

3.1.26 Darfield 2010, New Zealand On September 4, 2010, a magnitude 7,1 earthquake struck the Canterbury region, in the center of the southern island of New Zealand [31]. During the event, extensive liquefaction, differential subsidence, and ground cracking associated with lateral spreading occurred in areas close population locations. Following the earthquake, the power service was out for all the town of Kaiapoi, some parts of the town were not reconnected to the power network for several days. The water system of the region was also affected and it took over a week to supply drinking water to all the households of the area [54]. On the other hand, the gas network system performed extremely well and no disruption was observed, only brief interruption due to mandatory inspections.

Epicenter ☀

Figure 3.27: Epicenter of 2010 Darﬁeld earthquake

3.1.27 Christchurch 2011, New Zealand A magnitude 6,3 earthquake struck the city of Christchurch, south island of New Zealand, on Frebruary 22, 2011 [31]. The quake cause more than 180 fatalities, a large number of injuries, and resulted in widespread damage to the built environment of the area, inlcuding signiﬁcant disruption to the lifelines. The event caused the largest utility disruption in a New Zealand city in 80 years, with much of the damage resulting from extensive and severe liquefaction in the Christchurch urban area, in the Canterbury region [96]. The main problem was that its lifelines systems were at the early stage of recovering from the 2010 Darﬁeld earthquake [58]. All of the basic utilities of the area were damaged and its restoration process was long, with more than a month in some cases due to the high number of pipe breaks [39] [47] [91]. CHAPTER 3. DATABASE 37

☀ Epicenter

Figure 3.28: Epicenter of 2010 Darﬁeld earthquake

3.1.28 Tohoku 2011, Japan On March 11th a devastating earthquake of magnitude 9,0 on the Richter scale hit the Pacific coast of Japan. The quake, and the following tsunami, caused widespread catastrophic disaster to Tohoku and Kanto regions, strong ground shaking and liquefaction, what brought about more than 16.000 fatalities and 3.000 missing persons [67]. Due to its strong and destructive power, the aftermath was named as the Great East Japan Earthquake Disaster. Lifelines systems were extremely damaged, causing significant social impacts on the affected areas as well as its surrounding areas. For the purpose of this study, only damage by the earthquake has been taken into account. The electric network was strongly affected by the quake and there were outages of electric power supply in almost nine million household, including six different prefectures. Water, gas and telecommunications systems were as well severly damaged and the restoration process took more than a month [66].

Epicenter ☀

Figure 3.29: Epicenter of 2011 Tohoku earthquake CHAPTER 3. DATABASE 38

3.1.29 Samara 2012, Costa Rica A powerful magnitude 7,6 earthquake rattled Costa Rica on May 5, 2012. The quake was one of the strongest in Central America, but caused little damage to the built environment [12]. Despite that the Costa Rican infrastructures are not extremely prepared for this kind of events, all utilities performed well and the full restoration of the diﬀerent systems was achieved a couple of days following the event. The international community congratulated the Costa Rican population and community because they show the world they could react properly to a major earthquake.

Epicenter ☀

Figure 3.30: Epicenter of 2012 Costa Rica earthquake

3.1.30 Napa 2014, U.S.A. The 2014 Napa earthquake hit the Californian valley, just north of San Francisco, on August 24 and had a magnitude of 6,6 on the Richter scale [27]. Fortunetly just one fatality and approximately 200 injuries were attributed to the earthquake, this number of severe injuries and fatalities would have been much higher if the earthquake had struck during the day. There was signiﬁcant damage to structures and lifelines, what drove to important business disruptions. Severe strucural damage was mostly limited to older wood-frame households. Damage to lifelines consisted mainly of pipe breaks, and there was generally only limited loss of service [82]. Power and gas system networks were restored during the following two days, but the restoration of the water distribution system was slower due to the signiﬁcant damage of one of the main water tanks of the city of Napa[33]. CHAPTER 3. DATABASE 39

Epicenter ☀

Figure 3.31: Epicenter of 2014 Napa earthquake

3.1.31 Illapel 2015, Chile The 16th of September, 2015, big quake of magnitude 8,4 shook the Chilean town of Illapel, located in the center of the south-american country [72]. The epicenter was at the northernmost of the aftershocks zone of the 2010 Maule earthquake. The earthquake induce a tsunami that killed several people along the coastline, mostly tourist that were not aware of the tsunami protocols. The diﬀerent lifelines system performed well due to the resilience and preparation of the country and also because the earthquake hit a rural are with a low density of population.

☀ Epicenter

Figure 3.32: Epicenter of 2015 Illapel earthquake

3.2 Creation of the database

Variability among the earthquakes studied, apart from the obvious distributions of locations (Figure 3.33) and magnitudes, includes the quality and quantity of re- porting and damage data of the lifelines, the level of damage caused and the vulnerability of the aﬀected region, where, as would be expected, the impact of earthquakes CHAPTER 3. DATABASE 40 is typically much greater in highly vulnerable regions. Despite it was considered to normalized the analyzed data, so more vulnerable regions would not be harmed in the study, ﬁnally it has been used raw data because the main goal of the research is to study the downtime of the lifelines, which mean the precise number of days without services of the utilities, therefore a normalized data would not make sense for the main purpose of the study.

U.S.A. Japan

35.48 % 22.58 %

6.45 % Oceania 12.90 % 19.35 % 3.23 %

East & South Asia Mexico & South America Africa

Figure 3.33: Distribution of the earthquakes by location

Figure 3.34 shows the distribution of all the earthquakes by the date of occur- rance and also deﬁning the magnitude of each event. First of all is necessary to remark that it is not true that in the past (period from the 20’s trough the 60’s) there were less earthquakes than now. The point is that were less reported and there exists less documentation of these past events. However, this graph presents the distribution of the earthquakes analyzed and studied on this paper, and reﬂects the need to complete this database, and the the database of the overall community, with documentation from the past. To have a strong database was always a main goal for this paper, but it should be also important for the earthquake engineering community.

Initially the research process was complicated because it was not clear the idea of the ﬁnal output or the data to analyze. In order to make the ﬁrst steps easier, the database from REDiTM was taken as a guideline and a mirror to look into. Never- theless, after a couple of weeks working on the database new ideas came out, what CHAPTER 3. DATABASE 41

Figure 3.34: Distribution of the earthquakes by the date of occurance and its magnitude supposed a change on the database. Finally, only quantitative data was used for this paper, although at the beginning qualitative data was also gathered.

Some events took place in great areas with large population, which means more than one system for the same lifeline was affected. This fact is also taken into account for the research and different downtime values have been used for a same event, what led to the creation of different restoration curves for these locations with different systems affected. Chapter 4

Restoration curves

There is a wealth of existing models to develop fragility curves, although are mainly addressed to buildings rather than infrastructure [36] [61] [38] [61] [75] [87] [88] [100]. The main challenge is using these curves properly with the existing data, in order to reflect in a correct way the real behavior of the different systems and take advantage of the information of the database. The present chapter aims to address this challenge by describing the different types of possible restoration curves, the development of the final restoration curves and the comparison of the obtained curves for each lifelines.

4.1 Diﬀerent types of approaches

Empirical fragility curves are constructed from a database of damage data col- lected following one or more earthquake events. As stated before on Chapter 3, is extremely important to develop a complete database, in order to obtain reliability fragility curves. It is important to have a large database, regardless the type of approach, since small sample sizes are not ideal for fragility assessments as they result in large conﬁdence bounds being associated with the constructed curves.

Several diﬀerent approaches have been analyzed and partially applied (for its study), so as to examine which was the better solution for the gathered data and the aim of the research.

42 CHAPTER 4. RESTORATION CURVES 43

4.1.1 Exponential distribution This approach assumes that recovery takes place relatively quickly in the ﬁrst few time periods after a disaster but then slows down as time continues. This model presents the advantage that they do no require transformation of the damage data and do no assume constant variance of the response variable. There are plenty of examples available on the literature, used to derive empirical fragility curves for buildings [87] [71].

The exponential distribution is one of the widely used continuous distributions and has probability density function (pdf) given by:

⎪⎧ −λx > ⎪ λe x 0 f (x; λ) = ⎨ (4.1) X ⎪ ⎩⎪ 0 otherwise

λ is the rate of change that parametrizes the distribution and is the average rate of lightbulb burnouts. But for the purpose of this research is needed the cumulative distribution function (cdf) which is ruled by the function:

⎪⎧ − −λx ≥ x ⎪ 1 e x 0 FX (x; λ) = ∫ fX (x; λ)dx = ⎨ (4.2) −∞ ⎪ ⎩⎪ 0 x < 0

For the test of this distribution, the parameter λ had the following value: 1 λ = (4.3) µD

µD are the average days required for full restoration, the average downtime of the all systems.

The results obtained with this distribution were too opthimistic in the sense that full restoration was achieved eartlier than in the real scenario. Systems with short restoration time had greater value in this distribution. This fact, in addition to the CHAPTER 4. RESTORATION CURVES 44 several scenarios were the system was quickly restored (less than a week) made that this distribution did not completely ﬁt with the obtained data.

4.1.2 Lognormal distribution The lognormal functions are spatially spread along the engineering community to develop damage functions or fragility curves. Azevedo et al. (2004) [6] modeled fragility curves as lognormal density probability function for the transportation network; Giovanazzi et al. (2009) [38] also developed fragility curves for lifelines system components as lognormal distributed functions.

A variable is lognormally distributed if f(x) = ln(x) is normally distributed. The general formula for the probability density function of the lognormal distribution is:

e−((ln((x−θ)/m))2/(2σ2)) f(x) = √ (4.4) σ(x − θ) 2π

where σ is the shape parameter (or the standard deviation of the logarithm of the function) and has to be positive; m is the scale parameter (or the median of the distribution) also greater than 0 and θ is the location parameter (all the values of the variable x must be greater than θ).

Again, for this research, rather than the probability density function (pdf) is in- teresting the values of the cumulative distribution function (cdf), with the following formula:

ln(x) F (x) = Φ( ) (4.5) σ

Φ is the cumulative distribution funcition of the normal distribution, with the following description: CHAPTER 4. RESTORATION CURVES 45

x e−x2/2 F (x) = ∫ √ (4.6) −∞ 2π

Contrary with what was observed with the exponential distribution, the lognormal cumulative distribution relied on the greater values and the results did not make sense: full restoration was never achieved with this approach, even more, for all the diﬀerent scenarios the restoration was far from close to be achieved.

4.1.3 Gamma distribution The gamma distribution is a general type of statistical distribution related to the beta distribution (which depends on two parameters) and arises naturally in the processes for which the waiting times among the events are relevant (as they are in this research). The gamma function has the following equation:

∞ Γ(k) = ∫ xk−1e−xdx (4.7) 0

where k is the shape parameter and must be positive to ensure the convergence of the integral. On the other hand, the equation for the standard gamma distribution (pdf) is:

1 f(x∣α, β) = xα−1e−x/β (4.8) βαΓ(α)

The gamma distribution is a continuous distribution on the interval (0, ∞) and is very useful to plot restoration curves because it has a rich variety of shapes. The variable α is the shape parameter, which allows the gamma distribution to take a variety of shapes, depending on the value of α; while β is the scale parameter, whose eﬀect is to stretch (scale parameter greater than one) or squeeze the distribtion (scale parameter less than one). A gamma distribution function with shape parameter α = 1 and scale parameter β is an exponential distribution, as the one seen previously on CHAPTER 4. RESTORATION CURVES 46 the section 4.1.1.

The formula for the cumulative distribution function (cdf) of the gamma distribution is:

1 x F (x∣α, β) = ∫ tα−1e−t/βdt (4.9) βαΓ(α) 0

The results obtained with the cumulative gamma distribution functions ﬁt best the data for water, electricity, gas and telecommunications outages, and so, this has been the selected approach to develop the diﬀerent restoration curves.

4.1.4 Modified gamma distribution Nojima et al. 1999 [65] proposed a different approach for implementing restoration curves using gamma distribution function. The paper Empirical estimation of lifeline outage time in seismic disaster describes the performance of the electric, water and gas system of Japan after nine different earthquakes.

Gamma distribution functions are used for creating the restoration curves, but standard deviation is not directly obtained from the statistical analysis. Nojima et al. used the following equation to relate the standard deviation to parameters directly estimated from the database:

µD + kσD = DF (4.10)

where µD is the average days required for restoration, σD is the standard deviation, DF are the days required for full restoration and k is the scale parameter, which is assumed to take a value ranging around from 2.5 to 4.

Although the results obtained with this approach were very similar, this method has been dismissed because it does not make a different or advantatge respect to the standard gamma distribution. The method described by Nojima et al. is more CHAPTER 4. RESTORATION CURVES 47 useful with smaller databases, where varying the value of the scale factor k can make a real difference. Since the database of this research is larger, stronger and more heterogeneous, it was not convinient to use this method because the scale factor would need a different range, an unkown range, so it was more accurate to use the standard gamma distribution.

4.2 Development of the restoration curves

Once all the diﬀerent procedures were analyzed, it was clear that the cumulative gamma distribution best ﬁtted the obtained data. The goal is to obtain a suitable restoration curve for each lifeline, were all the data has the same value and is portraid on the distribution.

Equation 4.9 was used for that purpose, but is necessary to obtain the values of the parameters α and β, previously described. First is essential to calculate the values of the mean and the standard deviation. The mean value (µD) denotes the average of expected value of a random data from the database and it is simply de- ﬁned by:

∑n x µ = i=1 i (4.11) D n

On the other hand, the standard deviation σD is the dispersion of a random variable of the database with respect to the mean value (µD). The value of the standard deviation is obtained with the following formula:

√ ∑n (x − µ )2 σ = i=1 i D (4.12) D n − 1

Both values, µD and σD have been estimated with the data of all the earthquakes from the database. It would be also possible to develop a restoration curve, based on the downtime data, for each earthquake and then sum all the curves together to get just one restoration curve for each lifeline, but the problem was that for some CHAPTER 4. RESTORATION CURVES 48 earthquakes there was not too many information and the restoration curve, based on the downtime data, would not make sense. Afterwards, is possible to calculate the parameters α and β as:

σ2 β = D (4.13) µD

µ α = D (4.14) β

Once all the parameters were deﬁned for each lifelines, the restoration curves were created with the software MATLAB® [62]. Also in some cases, where there was enough data, individual curves for each earthquakes were created too.

4.3 Restoration curves for each lifeline

Restoration curves were developed for the power, water, gas and telecommunications systems based on the quantitative data obtained from the studies and research on previous earthquakes. The curves are plotted based on the number of days required to restore full service to customers (horizontal axis) that lost service immediately after the earthquake, since it is assumed that utility disruption will occur. The vertical axis (Probability of exceedance) can be interpreted as the likelihood that the utility will be restored to the customers within the corresponding framework.

4.3.1 Power systems The distribution for the power systems reflects that this utility presents a speedy recovery, since the first days after the earthquake there is a high percentage of restoration, as seen in Figure 4.1. The figure shows the restoration curve of the overall systems all together, red curve, and the restoration curves of the different systems individually, in gray. It is important to remark that these individual restoration curves are generated also with a cumulative gamma distribution, using the gathered data and may not reflect the precise pace of the restoration process of these events, but are useful to understand the different downtimes of all the systems after the CHAPTER 4. RESTORATION CURVES 49 earthquakes.

The result of a nimble restoration is not surprising, since most of the systems analyzed were fully restored in less than two weeks, as seen in the graph. Only a couple of systems were not restored after the two weeks following the earthquake: the electric system of Chi-Chi, Taiwan, and the one from the prefecture of Tohoku, Japan.

The cumulative gamma distribution for the power systems has an α of 0,48 while the β is 12,05. The small value of the α parameter is responsible of the initial steep- ness of the curve, whereas the also relatively small value of β is accountable for the stretching of the curve, what makes greater the area under the red curve. This area represents the resilience of the system under the shock of an earthquake, how fast does this system is typically restored. The integral of the red curve has a value of 942,21, which is the largest among all the four utilities analyzed in this research.

Figure 4.1: Restoration curve of power systems

Figure 4.2 reflects the same information but in a logaritmic scale, what helps to CHAPTER 4. RESTORATION CURVES 50 understand better the behavior during the first hours and days after the earthquakes. In fact, this graph reassert the argument that the electric power system has a very quick recovery and in the first hours after the earthquake, the restoration has tipically started and half of the system is restored in less than two days.

Figure 4.2: Restoration curve of power systems with a semilog x axis

One of the possible reasons of the quickness in the restoration process of the electric grid could be the fact that the underground distribution network is more reliable or resistent against these kind of ground movements. Also, the power distribution network has the ability of easily reroute power and decrease the systems vulnerability to a major power loss.

From Figure 4.1 and Figure 4.2 it can be noticed that there is a 20% probability that the system will be restored within the 15 ﬁrst hours after the earthquake, 50% to be restored in the next 38 hours following the quake and in 4 days there is a probability of 70% to be fully active. The probability to be fully restored is achieved within 46 days after the hit. It is important to remark the fact that these values are orientative and a simple estimation. It is not a rule to follow for the lifeline companies and stakeholders, rather than an orientation of what to expect. CHAPTER 4. RESTORATION CURVES 51

4.3.2 Water systems The restoration curve for the water systems (Figure 4.3) is less steep at the begining of the distribution, which means that the restoration process does not typically starts immediately after the shock of the earthquake. Again, this graph plots the restoration process of all the diﬀerent earthquakes, in blue, and the restoration of the 31 earthquakes individually, in light gray.

The diﬀerent water systems analyzed in this reasearch performed in a very heterogeneous manner, there is a great array of diﬀerent perfomances. There are some systems that only needed a few days to reach full restoration, like Illapel, while some other it took more than a month to reach full capacity, such as Kobe or Northridge. This heterogeneous nature of performances of the water systems among the earthquakes analyzed, contributes to the shape of the curve, more streched out.

Figure 4.3: Restoration curve of water systems

The cumulative gamma distribution for the water systems has an α of 0,95 while the β is 17,21. The α parameter is greater than the one of the power systems, what explains the intial struggles on the restoration process, also the β parameter is greater, accountable for the streching of the distribution in the two weeks following the earthquake. The area under the blue curve represents an important value, but CHAPTER 4. RESTORATION CURVES 52 it is smaller than the power grid, with a value of 837,01. The resilience in this case is less than the electric system, probably because of the distribution network for the water systems, which is more breakeable and also is more complicated and time consuming to restored the infrastructes, make up with large concrete tanks, old cast iron pipes, storage facilities and pump stations.

Figure 4.4: Restoration curve of water systems with a semilog x axis

As previously seen for the power systems, Figure 4.4 represents the restoration process in a logaritmic scale. Although the restoration starts in the ﬁrst day, the process is diﬀerent than the power grid because it tooks longer the begin the reparation and there is a 50% probability that the system would restored after ten days of the quake, while for the power grid the probability is an 80% to be restored in the ten days following the hit of the earthquake. CHAPTER 4. RESTORATION CURVES 53

4.3.3 Gas systems The restoration process of the gas systems are slightly diﬀerent from the previous analyzed restoration curves. This curve, Figure 4.5, presents a concavity at the begining, during the ﬁrst days, which means that the repairs for the gas systems took longer than the other utilies and did not started until several days after the earthquake.

This is due to the fact that in the data obtained there are not too many scenarios where the gas restoration process started just in the following days after the quake and also there are cases where full restoration was not achieved until more than three months later. The reasons behind this behavior could be that the gas system network is similar to the water distribution network, easily breakeable (pipelines are more seismically-vulnerable than wires) and old, but also because in most of the cases the gas distribution network was shut down due to integrity testing before re- pressurizing the lines of the system, even if it was not damaged apparently.

Figure 4.5: Restoration curve of gas systems CHAPTER 4. RESTORATION CURVES 54

Cause to these facts, the resilience of these systems are less, compared to the other analyzed. The integral under the green curve has a value of 792,51, what means this system is 6% less resilient than the water system and 16% less than the electric grid. In this case the cumulative gamma distribution for the gas systems has an α parameter of 1,08 while the β is 19,34. The α parameter is the greatest among the diﬀerent utilities analyzed on this research and is accountable for the intial struggles on the restoration process, with the slowest pace among all the lifelines. The β parameter is large too, accountable for the streching of the distribution in the three weeks following the earthquake, what represents a larger streching than the water systems. The restoration curve in a semilogaritmic scale is also revealing, since it is observed that in any case the gas distribution network would be repaired in less than one day. After ten days of the earthquake, the probability that the system is fully restored is 36%, much more less than for power and water systems, while there is a 60% probability to be restored in 13 days. It is also noticiable the fact that the probability to be restored in the ﬁrst day is just 3,79%, again, this is probably due to the mandatory test that must be undertaken in the gas distribution network.

Figure 4.6: Restoration curve of gas systems with a semilog x axis CHAPTER 4. RESTORATION CURVES 55

4.3.4 Telecommunications systems The restoration curve for the telecommunications systems, Figure 4.7 is quite similar to the restoration curve of the power systems, Figure 4.1. Although a couple of diﬀerences stand out: the start of the curve is similar but even steeper, which means that the restoration of this systems typically start almost immediately after the earthquake because of its possibility to switch from one center to another, network redundancy, and cause of the numerous back-up generators and batteries of these facilities; the second is that even if the start, ﬁrst few days, is instantaneous, it takes longer to achieve full restoration. There is a high concentration of light gray curves at the beginning of the graph, representing the quick restoration of most of the events.

Figure 4.7: Restoration curve of telecommunications systems

The parameters governing the cumulative gamma distribution for the telecommunication systems are 0,30 for the α parameter and 61,25 for the β. The α parameter for this lifeline is the smallest among all the utilities analyzed, therefore the beginning of the curve is really steep, similar to the power systems, what can be explained CHAPTER 4. RESTORATION CURVES 56 because of the coupling of these two utilities. On the other side the β parameter is the largest of all the systems, what explains that the system does not reach the full capacity after the 100 days following the earthquake, due to the slow recovery process of the telecom systems of Michoacan and Tohoku. The area under the curve, yellow line, is similar to the water system, with a total value of the integral of 853,32. The resilience of this system is greater than the water and gas distribution systems, but less than the electric grid. This is an expected behavior since the telecommunications infrastructure is similar to the power grid but more complex and extremely coupled with the electric systems.

The logaritmic graphic reﬂects the same idea, that the restoration process starts almost afterwards the event, or it may not even experience serviciability disruption (due to the strength of the system or the back-up batteries), and in the ten days following the quake there is a 65% probability that the system is completely restored, while for the power system after 10 days of the earthquake there is 80% probability.

Figure 4.8: Restoration curve of telecommunications systems with a semilog x axis Chapter 5

Conclusions & Future work

5.1 Conclusions of the research

This research required for a deep study of the performance of the power, water, gas and telecommunications systems after important earthquakes during the last hundred of years. It has been a heavy task due to the lack of a worldwide database with standarized information of the aftermath of the different hazards. Moreover, the farther in the past it took place an earthquake, the more difficult was to obtain information because sometimes there was no an available report. In spite of everything, carrying out this research at the University of California, Berkeley, in collaboration with the Pacific Earthquake Engineering Research, has been extremely helpful due to the large array of possibilities to obtain information: professors, professionals, online database and libraries. I truly believe that this research would not have been possible without these resources.

The main goal of the database was that it had to be heterogeneous and repre- sentative all areas of the world, from Japan to the U.S.A., from Sout-America to the South-Eastern Asia. At the beginning, the state of the art and the database as well were skewed to the U.S.A., due to the strong inﬂuence of being studying in this country, but later more information was included from all over the world.

Afterwards the database was complete, it was time to explore the different kinds of distribution that could possibly be used to develop restoration curves. Several cumulative distribution function were kept in mind but just a few of them were studied: exponential distribution, lognormal distribution, gamma distribution and modified gamma distribution. Finally the gamma distribution was selected because it fits bet-

57 CHAPTER 5. CONCLUSIONS & FUTURE WORK 58 ter the data obtained and reﬂects in a proper way the behavior of the overall systems.

The ﬁnal results show expected behavior for all the utilities. The power system is the more likely to be restored the ﬁrst, since the distribution network is more resistant due to the fact that does not require concrete or cast iron pipes. Moreover, because of its nature is easier to recover the electricity of the system, since in most of the cases there exist back-up batteries, which might also mean that there is no disruption at all.

The telecommunications systems reﬂect a similar behavior to the electric grid. The restoration process starts really fast, also because of the back-ups, and it might not be a complete disruption, but then the probability to be restored in the following days after the quake is less than the power system. This fact is probably due to the coupling of this system with the electricity grid, and so, if the power system is not fully restored the telecommunications system will not be restored neither.

On the other side there are the performances of the gas and water distribution systems. These lifelines present a slower recovery pace due to the nature of its infrastructure, with iron cast and concrete pipelines and older facilities. The gas distribution system is the utility that takes longer to be completely restored because of the mandatory test and investigations to be done after a hazardous event as an earthquake, what force the utility to be closed for some days. Meanwhile, the water system has a similar behavior to the gas network but with a quicker recovery pace.

Figure 5.1 reflects the work load of each of the different steps of this research. The database took a long time to be completed, because of the difficulties to find some types of informations and due to whenever a suitable source was found, was easier to find more sources. After that, the creation of the restoration curves and its analysis was a faster task as well as more interested and enjoyable. CHAPTER 5. CONCLUSIONS & FUTURE WORK 59

Creation of the database

Analysis of the results

Development of the restoration curves

Figure 5.1: Distribution of the work done during the research CHAPTER 5. CONCLUSIONS & FUTURE WORK 60 5.2 Future work

While working on this study some new ideas came out, however were not possible to carry through because a lack of time, data or knowledge. Even though, it is important to name them for future endevors.

5.2.1 Increase the database The database used for this research consist of 31 earthquakes that happened during the last century. Nevertheless, during the last hundred of years took place more earthquakes than the ones analyszed and could be incorporated to the database in order to get more reliable data. Some earthquakes were not included because of the lack of information about the performance of the utilities, others were not included because they took place a long time ago and there was not a digital copy of the report, and others were not considered because the source was not reliable enough.

5.2.2 Analysis of the transportation system One of the initial goals of the research was to analyse the restoration process of the transportation system as a whole and only. There is not such a study at the moment, although there are similar ones that have studied the restoration process of diﬀerent elements of the transportation system as the bridges or the freeways. This approach was dismissed due to the heterogeneus nature of the system and the complication behind a such a study [74] and it could perfectly be the main topic of a deep research.

5.2.3 Interdependencies During the analysis of the results, some interdependencies have been observed with the power and telecommunications systems, where there exists an important coupling. However, the truth is that all the systems are extremely dependent on electricity and, in a lower level, on telecommunications. At the time of the creation of the restoration curves this fact was not took into account, but was just and output inferred from the graphs. The fact of considering these interdependencies will give an extra value to this study and will make it more reliable. CHAPTER 5. CONCLUSIONS & FUTURE WORK 61

5.2.4 Standarize the collection of data Probably the main problem faced while creating the database was to deal with different studies, with different analysis and different formats. There is not an international standard to analyse the performance of the utilities afterwards a hazardous event. In a very profitable meeting with Laure Johnson, principal and founder of Laurie Johnson Consulting, she pointed out this fact too and mentioned the need of a standard proceeding to analyse the performance of the different lifelines, what would be really helpful upon the study of those analysis. Acknowledgements

I would first like to thank my american thesis advisor, Stephen Mahin, of the University of California, Berkeley and The Pacific Earthquake Engineering Research center. The door to Prof. Mahin office’s was always open whenever I ran into a trouble spot or had a question about my research or writing. Moreover he always put me in contact with experts who could help me in any way of my research, such as Laurie Johnson, Vesna Terzic and Charles Scawthorn.

I would also like to thank to Gian Paolo Cimellaro for giving me the opportunity to have this astounding opportunity. Since the beginning he was always willing to help me to accomplish this experience and also this research. He consistently allowed this study to be my own work, but steered me in the right direction whenever he thought I needed it.

Finally I would like to thank my home university too. BarcelonaTech, and Miquel Estrada in particular, who has been always very understanding with the opportunity I had ahead of me. Even if the bilateral agreement between the two universities was not possible, Miquel Estrada encouraged me to catch this train. He has been very empathetic, easy to work with and eased all the process and situation.

Last but not least, I will always be thankful to my family and girlfriend. Without their support, faith and encouragement this experience and this research would not have been possible at all. Appendices

63 Appendix A

Complete Database

The following tables show the completed database used to create the restoration curves for each lifelines. The diﬀerent earthquakes are exhibited in cronological order, with their main seismic characteristic and assessing the disruption time of the power, water, gas, telecommunications, wastewater and transportation systems.

The database gathers more information than the really used to create the different curves. This is due to the fact that at the beginning of the research was not clear which would be the method or model used to develop the curves. Ii is also important to remark that not all the earthquakes present the same information, either because not all the lifelines were affected during the earthquake or either because there is not a study including this information. Those cases were the information was not available are marked with a dash (-) inside the cell. Likewise, the transportation system analysis was initially more exhaustive, examining the different aspects of transportation (roads, railway, bridges, airports, ports...), but it was finally discarded and so all the information is not shown here because it is incompleted.

64 APPENDIX A. COMPLETE DATABASE 65

Lifeline Kanto 1923 Valdivia Alaska 1964 Niigata 1960 1964 Country Japan Chile U.S.A. Japan Magnitude 7,8 9,5 9,2 7,6 PGA 0,90g 0,27g 0,33g 0,16g Reference Kawata 1996 Kausel 1960 Eckel 1976 Dynes et al. 1961; Wind and Seismic Eﬀects Power systems Downtime 5 days, 7 days 5 days 0,75 days, 1 1 day, 24 days day, 2 days Disruption - - - - Water systems Downtime 42 days 5 days 1 day, 5 days, 4 days, 10 7 days, 14 days, 15 days days, 14 days Disruption - - - - Gas systems Downtime 60 days, 180 - 1,5 days, 2 2 days, 180 days days, 14 days days Disruption - - - - Telecommunications systems Downtime 13 days - 1 day, 21 days - Disruption - - - - Wastewater systems Downtime - - 1 day - Disruption - - - - Transportation systems Downtime - - - - Disruption - - - - APPENDIX A. COMPLETE DATABASE 66

Lifeline Tokachi-oki San Fer- Oﬀ-Miyagi El Asnam 1968 nando 1971 1978 1980 Country Japan U.S.A. Japan Algeria Magnitude 8,3 6,6 7,4 7,1 PGA 1,0g 0,52g 0,44g 0,40g Reference Katayama Katayama Kuribayashi 1977 1980 et al. 1983 Power systems Downtime 2 days 1 day 1 day, 2 days - Disruption - - - - Water systems Downtime - - 12 days 14 days Disruption - - Distribution - & Outages Gas systems Downtime 20 days, 30 9 days, 10 3 days, 18 - days days days, 27 days Disruption Distribution Transmission - Telecommunications systems Downtime - 90 days 8 days - Disruption - Building Transmission - damage Wastewater systems Downtime - - 11 days - Disruption - - Damage of - plant Transportation systems Downtime - 90 days - - Disruption - Roads closed - - APPENDIX A. COMPLETE DATABASE 67

Lifeline Nihonkai- Michoacan Loma Pri- Luzon 1990 chubu 1983 1985 eta 1985 Country Japan Mexico U.S.A. Philippines Magnitude 7,8 8,1 6,9 7,8 PGA 0,24g 0,14g 0,65g 0,10g Reference Hamada et al. Ramirez et National Sharpe 1994 1985 al. 1988, Research O’Rourke Council 1994, 1988, Schiﬀ 1998, O’Rourke Yashinsky 1990 1998 Power systems Downtime 1 day 1 day, 3 days, 0,5 days, 2 2 days, 3 4 days, 7 days days days, 7 days Disruption - Substations Substations - Water systems Downtime 30 days 14 days, 30 1 day, 1,5 10 days, 14 days, 40 days, days, 2 days, days, 14 days 45 days 3 days, 3 days, 4 days, 4 days, 7 days, 14 days Disruption - Distribution - - Gas systems Downtime 30 days - 10 days, 10 - days, 11 days, 16 days, 30 days Disruption - - Relighting - Telecommunications systems Downtime - 160 days 0,1 days, 1,5 0,4 days, 1 days, 3 days, day, 5 days 3 days, 3 days, 4 days Disruption - Building Congestion - damage Wastewater systems Downtime - - - - Disruption - - - - Transportation systems Downtime 16 days - 30 days - Disruption Railroad Roads closed Roads closed - APPENDIX A. COMPLETE DATABASE 68

Lifeline Kushiro-oki Northridge Hokkaido Sanriku 1993 1994 Toho-oki 1994 1994 Country Japan U.S.A. Japan Japan Magnitude 7,8 6,7 8,2 7,5 PGA 0,72g 0,94g 1,60g 1,0g Reference Yamazaki et Lund et al. Yamazaki et Yamazaki et al. 1995 1995, Schiﬀ al. 1995 al. 1995 et al. 1997, Davis et al. 2012, Cooper 1997 Power systems Downtime 1 day 0,5 days, 2 1 day 1 day days, 3 days Disruption Substations Water systems Downtime 3 days, 5 2 days, 7 3 days, 5 5 days, 12 days, 6 days days, 12 days, days, 9 days days, 14 days 46 days, 58 days, 67 days Disruption - Transmission - - & Distribu- tion Gas systems Downtime 3 days, 22 4 days, 5 - - days days, 7 days, 30 days Disruption - Relighting - - Telecommunications systems Downtime - 1 day, 2 days, - - 4 days Disruption - Switch fail- - - ures Wastewater systems Downtime - - - - Disruption - - - - Transportation systems Downtime - - - - Disruption - - - - APPENDIX A. COMPLETE DATABASE 69

Lifeline Kobe 1995 Izmit 1999 Chi-Chi Arequipa 1999 2001 Country Japan Turkey Taiwan Peru Magnitude 6,9 7,4 7,6 8,4 PGA 0,50g 0,41g 0,26g 0,30g Reference EQE Interna- Gillies et al. MCEER 2000 Edwards et tional 1995, 2001 al. 2001 Kuraoka et al. 1996, Chung et al. 1996 Power systems Downtime 2 days, 3 1 day 4 days, 14 1 day days, 5 days, days, 19 days 6 days, 8 days Disruption Distribution Water systems Downtime 0,5 days, 8 5 days, 29 9 days 32 days, 34 days, 73 days days days Disruption Distribution - - - Gas systems Downtime 11 days, 25 - 14 days - days, 84 days Disruption Distribution - - Telecommunications systems Downtime 1 day, 5 days, - 10 days - 7 days Disruption Congestion - - Wastewater systems Downtime 7 days - - - Disruption Pump station - - - Transportation systems Downtime - - - - Disruption Roads closed - - - APPENDIX A. COMPLETE DATABASE 70

Lifeline Nisqually Alaska 2002 Bam 2003 Niigata 2001 2004 Country U.S.A. U.S.A. Iran Japan Magnitude 6,8 7,9 6,6 6,6 PGA 0,30g - 1,0g 1,33g Reference Reed et al. EERI 2003a Ahmadizadeh EERI 2005, 2003 et al. 2004 Scathorn 2006 Power systems Downtime 2 days, 6 - 4 days 1 day, 4 days, days, 3 days 11 days Disruption - - - - Water systems Downtime - - 10 days, 14 14 days, 28 days days, 35 days Disruption - - - Distribution Gas systems Downtime - 3 days - 28 days,35 days, 40 days Disruption - - - Telecommunications systems Downtime - - 1 day - Disruption - - - Wastewater systems Downtime - - - - Disruption - - - - Transportation systems Downtime - - - - Disruption - - - - APPENDIX A. COMPLETE DATABASE 71

Lifeline Maule 2010 Darﬁeld Christchurch Tohoku 2010 2010 2011 Country Chile New Zealand New Zealand Japan Magnitude 8,8 7,1 6,3 9,0 PGA 0,78g 0,8g 2,1g 2,7g Reference Evans et Knight et Giovinazzi Nojima 2012 al. 2011, al. 2012, et al. 2011, Eidinger 2012 Kwasinski et Tang et al. al. 2012, Ei- 2014 dinger 2012, Wood et al. 2012 Power systems Downtime 1 day, 3 days, 1 day, 2 days, 0,16 days, 14 2 days, 2 10 days, 14 12 days days days, 3 days, days, 14 days 8 days, 40 days, 45 days Disruption Distribution Distribution Distribution Distribution Water systems Downtime 4 days, 6 1 day, 7 days 30 days 1 day, 1 day, days, 16 days, 26 days, 47 42 days days, 47 days, 47 days, 47 days Disruption Transmission Distribution Distribution Transmission & Distribu- & Distribu- tion tion Gas systems Downtime 10 days, 90 - 9 days, 14 2 days, 13 days days days, 18 days, 30 days, 35 days, 54 days Disruption Re- Distribution Re- Distribution pressurizing pressurizing Telecommunications systems Downtime 3 days, 7 2 days, 3 9 days, 15 21 days, 49 days, 17 days, days, 9 days days days, 49 days 17 days Disruption Power failure Power failure Power failure Power failure Wastewater systems Downtime 42 days 30 days 90 days - Disruption Reclamation Sewage Lines Sewage Lines - Plants & Sewage Lines Transportation systems Downtime - 42 days 1 day - Disruption Roads closed Roads closed - - APPENDIX A. COMPLETE DATABASE 72

Lifeline Samara Napa 2014 Illapel 2015 2012 Country Costa Rica U.S.A. Chile Magnitude 7,6 6,0 8,4 PGA 1,53g 0,65 0,30g Reference C.N.E. 2012 EERI 2014, ONEMI 2015 Scawthorn 2014 Power systems Downtime 1 day 2 days 3 days Disruption Substations Wire-wire Distribution contact Water systems Downtime 2 days 0,75 days, 3 days 0,9 days, 2,5 days, 11 days, 12 days, 20 days Disruption Water tanks Transmission Distribution & Distribu- tion

Gas systems Downtime - - - Disruption - Damage to - customer facilities Telecommunications systems Downtime 1 day - - Disruption Distribution - - Wastewater systems Downtime - 2 days - Disruption - Inﬂow- - Transportation systems Downtime - - - Disruption - - - Appendix B

Individual restoration curves

Appendix B presents individual restoration curves for diﬀerent lifelines of some of the earthquakes. This curves have been created following the same process previously described, cumulative gamma distribution function. For that reason, not all the earthquakes analyzed are presented in the graphs below, since for some cases there was not enough data available to create these curves. It is important to remark that this are not the real restoration process of these lifelines, but represents an estimation of the process.

73 APPENDIX B. INDIVIDUAL RESTORATION CURVES 74

Figure B.1: Restoration curves of the water systems after the earthquake of Alaska 1964

Figure B.2: Restoration curves of the water systems after the earthquake of Niigata 1964 APPENDIX B. INDIVIDUAL RESTORATION CURVES 75

Figure B.3: Restoration curves of the gas systems after the earthquake of Oﬀ-Miyagi 1978

Figure B.4: Restoration curves of the water systems after the earthquake of Michoa- can 1985 APPENDIX B. INDIVIDUAL RESTORATION CURVES 76

Figure B.5: Restoration curves of the water and gas systems after the earthquake of Loma Prieta 1985

Figure B.6: Restoration curves of the water and gas after the earthquake of Northridge 1994 APPENDIX B. INDIVIDUAL RESTORATION CURVES 77

Figure B.7: Restoration curves of the water and gas after the earthquake of Kobe 1995

Figure B.8: Restoration curves of the water systems after the earthquake of Izmit 1999 APPENDIX B. INDIVIDUAL RESTORATION CURVES 78

Figure B.9: Restoration curves of the water systems after the earthquake of Arequipa 1999

Figure B.10: Restoration curves of the water systems after the earthquake of Niigata 2004 APPENDIX B. INDIVIDUAL RESTORATION CURVES 79

Figure B.11: Restoration curves of the diﬀerent lifelines after the earthquake of Maule 2010

Figure B.12: Restoration curves of the power systems after the earthquake of Darﬁeld 2010 APPENDIX B. INDIVIDUAL RESTORATION CURVES 80

Figure B.13: Restoration curves of the diﬀerent lifelines after the earthquake of Tohoku 2011

Figure B.14: Restoration curves of the water systems after the earthquake of Napa 2014 Bibliography

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