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Downtime Estimation of Lifelines After an Earthquake

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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 impor- tant elements in seismic risk management because of the significant economic con- sequences. 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 gesti´onde riesgos s´ısmicoses la estimaci´ondel tiempo que estar´anlas infraestructuras despu´esde un terremoto. Esta investigaci´on se ha basado en el desarrollo de un modelo emp´ırico para estimar dicho tiempo, bas´andoseen informaci´ony datos sobre el da~norecibido por parte de las diferentes infraestructuras tras diferentes sismos ocurridos en los ´ultimoscien a~nos. 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 recuperaci´onpara cada infrastructura a partir de distribuciones gamma, bas´andoseen la media y la desviaci´ont´ıpicade las duraciones de las distintas inter- rupciones. Finalmente se ha incluido los pr´oximospasos que se podr´ıanseguir para continuar y mejorar este estudio en el futuro. Abstracte Un aspecte molt important de la gesti´ode r´ıscoss´ısmics´esl’estimaci´odel temps que quedarien les infraestructures sense oferir servei a m`aximacapacitat. 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 terratr`emolsque han succe¨ıtdurant els ´ultimscent anys. El primer pas ha estat crear una base de dades analizant el temps de baixa que ha estat cada servei durant cadasc´undels sismes. Posteriorment s'han desenvolupat corbes de recuperaci´oper a cada servei a partir de distribucions gamma, que depenen de la mitja i la desviaci´ot´ıpicade totes les dades. A m´es,s'ha incl`osels propers passos que es podrien dur a terme per a continuar i millorar aquesta investigaci´oen 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 mag- nitude . 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 . ..
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  • Failure of Showa Bridge During the 1964 Niigata Earthquake: Lateral Spreading Or Buckling Instability? 1 A

    Failure of Showa Bridge During the 1964 Niigata Earthquake: Lateral Spreading Or Buckling Instability? 1 A

    th The 14 World Conference on Earthquake Engineering October 12-17, 2008, Beijing, China FAILURE OF SHOWA BRIDGE DURING THE 1964 NIIGATA EARTHQUAKE: LATERAL SPREADING OR BUCKLING INSTABILITY? 1 A. A. Kerciku , S. Bhattacharya2, Z. A. Lubkowski 3, and H. J. Burd 4 1 Buildings’ Structural Engineer, Arup, UK 2 University Lecturer in Dynamics ,University of Bristol, UK 3 Associate Director, Arup, UK 4 University Lecturer in Engineering Science, University of Oxford, UK 4 Email: [email protected], [email protected], ABSTRACT : Following the 1964 Niigata earthquake many bridges, including the Showa Bridge, over the Shinano river collapsed. The newly-constructed Showa Bridge demonstrated one of the worst instances of damage, and there are still uncertainties and controversies regarding the causes of collapse. The collapse of the Showa Bridge has been, throughout the years, an iconic case study for demonstrating the devastating effects of the lateral spreading of liquefied soil. In this paper, this widely accepted collapse hypothesis has been challenged. The documented eyewitnesses’ observations and post-collapse damage reports have been reanalysed, and the all the major studies on the collapse of the bridge compared and contrasted. It has been shown that the current, widely accepted, failure mechanism based on bending due to lateral spreading, cannot explain the failure. This paper presents a new hypothesis based on buckling failure due to axial loads in conjunction with residual, earthquake-induced, lateral displacements. This alternative explanation has been evaluated quantitatively using the method suggested by Kerciku et al. (2008) for estimating the buckling capacity of piles in liquefied soil, and Eurocode 3 (1993) recommendations for steel members subjected to bending and axial compression.