A new methodology for estimating seismic resilience of buildings under successive damage-retrofit processes during the recovery time

References

• ACI Committee (). (2014). ACI 318-14, Building code requirements for structural concrete and commentary. American Concrete Institute.
   Google Scholar
• Almufti, I., & Willford, M. (2013). Resilience-based earthquake design initiative for the next generation of buildings (Issue October). REDi TM rating system, Arup.
   Google Scholar
• Ang, A. H.-S., & Wilson, H. T. (1984). Probability concepts in engineering planning and design, vol. 2: Decision, risk, and reliability. New York, NY: JOHN WILEY & SONS.
   Google Scholar
• Applied Technology Council. (2000). FEMA 356, Prestandard and commentary for the seismic rehabilitation of buildings. Washington: Federal Emergency Management Agency.
   Google Scholar
• Applied Technology Council. (2009). FEMA P-695 Quantification of building seismic performance factors. Washington: Federal Emergency Management Agency.
   Google Scholar
• Applied Technology Council. (2012). FEMA P-58-1 - Seismic performance assessment of buldings - volum 1 - methodology. Washington: Federal Emergency Management Agency.
   Google Scholar
• Bonstrom, H., & Corotis, R. B. (2016). First-order reliability approach to quantify and improve building portfolio resilience. Journal of Structural Engineering, 142(8), C4014001. doi:10.1061/(ASCE)ST.1943-541X.0001213
   Web of Science ®Google Scholar
• Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O'Rourke, T. D., Reinhorn, A. M., … von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. doi:10.1193/1.1623497
   Web of Science ®Google Scholar
• Bruneau, M., & Reinhorn, A. M. (2007). Exploring the concept of seismic resilience for acute care facilities. Earthquake Spectra, 23(1), 41–62. doi:10.1193/1.2431396
   Web of Science ®Google Scholar
• Burton, H. V., Deierlein, G., Lallemant, D., & Lin, T. (2015). Framework for incorporating probabilistic building performance in the assessment of community seismic resilience. Journal of Structural Engineering, 142(8), 142. doi:10.1061/(ASCE)ST.1943-541X.0001321
   Web of Science ®Google Scholar
• Burton, H. V., Miles, S. B., & Kang, H. (2018). Integrating performance based engineering and urban simulation to model post-earthquake housing recovery. Earthquake Spectra, 34(4), 1763–1785. doi:10.1193/041017EQS067M
   Web of Science ®Google Scholar
• Caverzan, A., & Solomos, G. (2014). Review on resilience in literature and standards for critical built-infrastructure. JCR Science and policy report, JRC90900. Publications Office of the European Union, Luxembourg.
   Google Scholar
• Chioccarelli, E., Giorgio, M., & Iervolino, I. (2021). Modelling seismic damage accumulation and recovery in aftershock sequences. 31st European Safety and Reliability Conference.
   Google Scholar
• Cimellaro, G. P., Fumo, C., Reinhorn, A. M., & Bruneau, M. (2009). Quantification of disaster resilience of health care facilities. Technical Report MCEER-09-0009, MCEER, University at Buffalo, State University of New York.
   Google Scholar
• Cimellaro, G. P., Reinhorn, A., & Bruneau, M. (2005). Seismic resilience of a health care facility. In Proceedings of Annual Meeting of the Asian Pacific Network of Centers for Earthquake Engineering Research (ANCER), Session III, November 10–13. https://www.researchgate.net/publication/239924294_Seismic_Resilience_of_a_Health_care_facility.
   Google Scholar
• Cimellaro, G. P. (2013). Resilience-based design (RBD) modelling of civil infrastructure to assess seismic hazards. In Handbook of seismic risk analysis and management of civil infrastructure systems (pp. 268–303). Woodhead Publishing. doi:10.1533/9780857098986.2.268
   Google Scholar
• Cimellaro, G. P. (2016). Downtime and recovery models. In Urban resilience for emergency response and recovery (pp. 93–108). Cham: Springer. doi:10.1007/978-3-319-30656-8
   Google Scholar
• Cimellaro, G. P., Nagarajaiah, S., & Kunnath, S. (2015). Introduction to resilience-based design (RBD). In Computational methods, seismic protection, hybrid testing and resilience in earthquake engineering (pp. 151–183). Cham: Springer. doi:10.1007/978-3-319-06394-2
   Google Scholar
• Cimellaro, G. P., & Reinhorn, A. M. (2011). Multidimensional performance limit state for hazard fragility functions. Journal of Engineering Mechanics, 137(1), 47–60. doi:10.1061/(ASCE)EM.1943-7889.0000201
   Web of Science ®Google Scholar
• Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2010a). Framework for analytical quantification of disaster resilience. Engineering Structures, 32(11), 3639–3649. doi:10.1016/j.engstruct.2010.08.008
   Web of Science ®Google Scholar
• Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2010b). Seismic resilience of a hospital system. Structure and Infrastructure Engineering, 6(1–2), 127–144. doi:10.1080/15732470802663847
   Web of Science ®Google Scholar
• Cimellaro, G. P., Renschler, C., Reinhorn, A. M., & Arendt, L. (2016). PEOPLES: A framework for evaluating resilience. Journal of Structural Engineering, 142(10), 04016063. doi:10.1061/(ASCE)ST.1943-541X.0001514
   Web of Science ®Google Scholar
• De Iuliis, M., Kammouh, O., Cimellaro, G. P., & Tesfamariam, S. (2019). Downtime estimation of building structures using fuzzy logic. International Journal of Disaster Risk Reduction, 34, 196–208. doi:10.1016/j.ijdrr.2018.11.017
   Web of Science ®Google Scholar
• FEMA. (2014). Multi-hazard loss estimation methodology, earthquake model, HAZUS-MH5. Technical Manual, Department of Homeland Security, Federal Emergency Management Agency , Mitigation Division, Washington, D.C.
   Google Scholar
• Gentile, R., & Galasso, C. (2021). Hysteretic energy‐based state‐dependent fragility for ground‐motion sequences. Earthquake Engineering & Structural Dynamics, 50(4), 1187–1203. doi:10.1002/eqe.3387
   Web of Science ®Google Scholar
• Ghasemi, H., Zare, M., Fukushima, Y., & Koketsu, K. (2009). An empirical spectral ground-motion model for Iran. Journal of Seismology, 13(4), 499–515. doi:10.1007/s10950-008-9143-x
   Web of Science ®Google Scholar
• Ghobarah, A. (2004). On drift limits associated with different damage levels. International Workshop on Performance-Based Seismic Design Concepts and Implementation, 28, 321–332. http://peer.berkeley.edu/publications/peer_reports/reports_2004/reports_2004.html.
   Google Scholar
• Hancilar, U., Çaktı, E., Erdik, M., Franco, G. E., & Deodatis, G. (2014). Earthquake vulnerability of school buildings: Probabilistic structural fragility analyses. Soil Dynamics and Earthquake Engineering, 67, 169–178. doi:10.1016/j.soildyn.2014.09.005
   Web of Science ®Google Scholar
• Haukaas, T. (2007). Engineering decision making with numerical simulation models. Reliability, sensitivity, and optimization analysis for performance-based engineering, University of British Columbia, Vancouver, Canada. LECTURE NOTES, CIVL 518.
   Google Scholar
• Ibarra, L. F., Medina, R. A., & Krawinkler, H. (2005). Hysteretic models that incorporate strength and stiffness deterioration. Earthquake Engineering & Structural Dynamics, 34(12), 1489–1511. doi:10.1002/eqe.495
   Web of Science ®Google Scholar
• Iervolino, I., & Giorgio, M. (2015). Stochastic modeling of recovery from seismic shocks. 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12.
   Google Scholar
• Jamalreyhani, M., Rezapour, M., Cesca, S., Heimann, S., Vasyura-Bathke, H., Sudhaus, H.,Isken, M.P., Dahm, T. (2020). The 2017 November 12 Mw 7. 3 Sarpol-Zahab (Iran-Iraq border region) earthquake : source model, aftershock. In EGU General Assembly Conference Abstracts. doi: 10.5194/egusphere-egu2020-759.
   Google Scholar
• Kafali, C., & Grigoriu, M. (2005). Rehabilitation decision analysis. ICOSSAR‘05: Proceedings of the Ninth International Conference on Structural Safety and Reliability.
   Google Scholar
• Kang, H. (2018). From rupture to recovery: Integrating probabilistic building performance assessment, decision-making and socioeconomic vulnerability to model post-earthquake housing recovery. Doctoral dissertation. UCLA.
   Google Scholar
• Khansefid, A., & Bakhshi, A. (2019). Advanced two-step integrated optimization of actively controlled nonlinear structure under mainshock–aftershock sequences. Journal of Vibration and Control, 25(4), 748–762. doi:10.1177/1077546318795533
   Web of Science ®Google Scholar
• Khansefid, A., & Bakhshi, A. (2022). New model for simulating random synthetic stochastic earthquake scenarios. Journal of Earthquake Engineering ISSN, 26(2), 1072–1089. doi:10.1080/13632469.2019.1699207
   Google Scholar
• Klein, R., Nicholls, R., & Thomalla, F. (2003). Resilience to natural hazards : How useful is this concept? Environmental Hazards, 5(1), 35–45. doi:10.1016/j.hazards.2004.02.001
   Google Scholar
• Luco, N., Bazzurro, P., & Cornell, A. C. (2004). Dynamic versus static computation of the residual capacity of a mainshock-damaged building to withstand an aftershock. 13th World Conference on Earthquake Engineering. https://www.iitk.ac.in/nicee/wcee/article/13_2405.pdf.
   Google Scholar
• Mander, J. B. (1999). Fragility curve development for assessing the seismic vulnerability of highway bridges. Technical Report, MCEER Highway Project/FHWA.
   Google Scholar
• Miles, S. B., & Chang, S. E. (2003). Urban disaster recovery: A framework and simulation model. Buffalo, NY: Multidisciplinary Center for Earthquake Engineering Research.
   Google Scholar
• Miranda, E., & Aslani, H. (2003). Probabilistic response assessment for building-specific loss estimation. PEER Report 2003/03, Pacific Earthquake Engineering Research Center College of Engineering, University of California Berkeley.
   Google Scholar
• Moehle, J., & Deierlein, G. G. (2004). A framework methodology for performance-based earthquake engineering. 13th World Conference on Earthquake Engineering, (Vol. 679). Vancouver: WCEE.
   Google Scholar
• Moshref, A., Khanmohammadi, M., & Tehranizadeh, M. (2017). Assessment of the seismic capacity of mainshock-damaged reinforced concrete columns. Bulletin of Earthquake Engineering, 15(1), 291–311. doi:10.1007/s10518-016-9952-1
   Web of Science ®Google Scholar
• National Academies (U.S.). (2012). Disaster resilience: A national imperative. Washington, D.C.: National Academies Press.
   Google Scholar
• Pejovic, J., & Jankovic, S. (2016). Seismic fragility assessment for reinforced concrete high-rise buildings in Southern Euro-Mediterranean zone. Bulletin of Earthquake Engineering, 14(1), 185–212. doi:10.1007/s10518-015-9812-4
   Web of Science ®Google Scholar
• Reinhorn, A. M., Barron-Corverra, R., & Ayala, A. G. (2001). Spectral evaluation of seismic fragility of structures. In Proceedings ICOSSAR. http://www.dipra.org/pdf/DIPvsHDPE.pdf.
   Google Scholar
• Renschler, C. S., Frazier, A. E., Arendt, L. A., Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2010). Developing the ‘PEOPLEs’ resilience framework for defining and measuring disaster resilience at the community scale. 9th U.S. National and 10th Canadian Conference on Earthquake Engineering.
   Google Scholar
• Renschler, C. S., Frazier, A. E., Arendt, L. A., Cimellaro, G. P., Reinhorn, A. M., & Michel, B. (2010). A framework for defining and measuring resilience at the community scale: The PEOPLES resilience framework (pp. 10-0006). Buffalo: MCEER.
   Google Scholar
• Ribeiro, F. L. A., Barbosa, A. R., & Neves, L. C. (2013). Reliability-based robustness assessment of structures subjected to aftershock hazard events. In Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures-Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013.
   Google Scholar
• Rossetto, T., & Elnashai, A. (2003). Derivation of vulnerability functions for European-type RC structures based on observational data. Engineering Structures, 25(10), 1241–1263. doi:10.1016/S0141-0296(03)00060-9
   Web of Science ®Google Scholar
• Ryu, H., Luco, N., Uma, S., & Liel, A. (2011). Developing fragilities for mainshock-damaged structures through incremental dynamic analysis. Proceedings of the Ninth Pacific Conference on Earthquake Engineering.
   Google Scholar
• Shinozuka, M., Feng, M., Lee, J., & Naganuma, T. (2000). Statistical analysis of fragility curves. Journal of Engineering Mechanics, 126(12), 1224–1231. doi:10.1061/(ASCE)0733-9399(2000)126:12(1224)
   Web of Science ®Google Scholar
• Tirca, L., Serban, O., Lin, L., Wang, M., & Lin, N. (2015). Improving the seismic resilience of existing braced-frame office buildings. Journal of Structural Engineering, 142(8), C4015003. doi:10.1061/(asce)st.1943-541x.0001302
   Web of Science ®Google Scholar
• United Nations. (2005). Report of the world conference on disaster reduction, Inter-Agency secretariat of the International Strategy for Disaster Reduction (UN/ISDR), Kobe, Hyogo, Japan.
   Google Scholar
• Vamvatsikos, D., & Allin Cornell, C. (2002). Incremental dynamic analysis. Earthquake Engineering & Structural Dynamics, 31(3), 491–514. doi:10.1002/eqe.141
   Web of Science ®Google Scholar
• Vamvatsikos, D., & Cornell, C. A. (2004). Applied incremental dynamic analysis. Earthquake Spectra, 20(2), 523–553. doi:10.1193/1.1737737
   Web of Science ®Google Scholar
• Yang, T. Y., Moehle, J. P., & Stojadinovic, B. (2009a). Performance evaluation of innovative steel braced frames. Pacific Engineering Research Center, College of Engineering, University of California, Berkeley.
   Google Scholar
• Yang, T. Y., Moehle, J., Stojadinovic, B., & Kiureghian Der, A. (2009b). Seismic performance evaluation of facilities: Methodology and implementation. Journal of Structural Engineering, 135(10), 1146–1154. doi:10.1061/_ASCE_0733-9445_2009_135:10_1146
   Web of Science ®Google Scholar
• Yeo, G. L., & Cornell, C. A. (2005). Stochastic characterization and decision bases under time-dependent aftershock risk in performance-based earthquake engineering. Stanford, CA: Stanford University.
   Google Scholar
• Zhang, Y., & Burton, H. V. (2021). Optimal decision-making for tall buildings in the aftershock environment. Automation in Construction, 122, 103472. doi:10.1016/j.autcon.2020.103472
   Web of Science ®Google Scholar