Probabilistic Seismic Multi-hazard Risk and Restoration Modeling for Resilience-informed Decision Making in Railway Networks

References

• R-CRISIS [Accessed: 2022-09-18]. (2018).
   Google Scholar
• Ahamri, N., Mahdavinejad, M., & M, F. (2016). Interaction of natural landscape and modern heritage in case of Veresk, Iran. eQscientific Herald of the Voronezh State University of Architecture & Civil Engineering, 32(4), 70–91.
   Google Scholar
• Akkar, S., & Bommer, J. (2010). Empirical equations for the prediction of PGA, PGV, and spectral accelerations in Europe, the Mediterranean region, and the Middle East. Seismological Research Letters, 81(2), 195–206. https://doi.org/10.1785/gssrl.81.2.195
   Web of Science ®Google Scholar
• Akyol, N., & Karagöz, Ö. (2009). Empirical attenuation relationships for western Anatolia, Turkey. Turkish Journal of Earth Sciences, 18(3), 351–382.
   Web of Science ®Google Scholar
• Ambraseys, N., Douglas, J., Sarma, S., & Smit, P. (2005). Equations for the estimation of strong ground motions from shallow crustal earthquakes using data from Europe and the Middle East: Horizontal peak ground acceleration and spectral acceleration. Bulletin of Earthquake Engineering, 3(1), 1–53. https://doi.org/10.1007/s10518-005-0183-0
   Web of Science ®Google Scholar
• Amiri, G. G., Khorasani, M., Hessabi, R. M., & Amrei, S. R. (2009). Ground-motion prediction equations of spectral ordinates and arias intensity for Iran. Journal of Earthquake Engineering, 14(1), 1–29. https://doi.org/10.1080/13632460902988984
   Web of Science ®Google Scholar
• Campbell, K. W., & Bozorgnia, Y. (2010). A ground motion prediction equation for the horizontal component of cumulative absolute velocity (CAV) based on the PEER-NGA strong motion database. Earthquake Spectra, 26(3), 635–650. https://doi.org/10.1193/1.3457158
   Web of Science ®Google Scholar
• Chousianitis, K., Del Gaudio, V., Pierri, P., & Tselentis, G.-A. (2018). Regional ground- motion prediction equations for amplitude-, frequency response-, and duration-based parameters for Greece. Earthquake Engineering & Structural Dynamics, 47(11), 2252–2274. https://doi.org/10.1002/eqe.3067
   Web of Science ®Google Scholar
• Chousianitis, K., Del Gaudio, V., Sabatakakis, N., Kavoura, K., Drakatos, G., Bathrellos, G. D., & Skilodimou, H. D. (2016). Assessment of earthquake-induced landslide hazard in Greece: From arias intensity to spatial distribution of slope resistance demand. Bulletin of the Seismological Society of America, 106(1), 174–188. https://doi.org/10.1785/0120150172
   Web of Science ®Google Scholar
• Decò, A., Bocchini, P., & Frangopol, D. M. (2013). A probabilistic approach for the prediction of seismic resilience of bridges. Earthquake Engineering & Structural Dynamics, 42(10), 1469–1487. https://doi.org/10.1002/eqe.2282
   Web of Science ®Google Scholar
• Del Gaudio, V., Pierri, P., & Wasowski, J. (2003). An approach to time-probabilistic evaluation of seismically induced landslide hazard. Bulletin of the Seismological Society of America, 93(2), 557–569. https://doi.org/10.1785/0120020016
   Web of Science ®Google Scholar
• Dolce, M., Prota, A., Borzi, B., da Porto, F., Lagomarsino, S., Magenes, G., Moroni, C., Penna, A., Polese, M., & Speranza, E., et al. (2021). Seismic risk assessment of residential buildings in Italy. Bulletin of Earthquake Engineering, 19(8), 2999–3032. https://doi.org/10.1007/s10518-020-01009-5
   Web of Science ®Google Scholar
• Du, W., & Wang, G. (2016). A one-step Newmark displacement model for probabilistic seismic slope displacement hazard analysis. Engineering Geology, 205, 12–23. https://doi.org/10.1016/j.enggeo.2016.02.011
   Web of Science ®Google Scholar
• Estêvão, J. M. (2019). An integrated computational approach for seismic risk assessment of individual buildings. Applied Sciences, 9(23), 5088. https://doi.org/10.3390/app9235088
   Google Scholar
• Farahani, S., Behnam, B., & Tahershamsi, A. (2020a). Macrozonation of seismic transient and permanent ground deformation of Iran. Natural Hazards and Earth System Sciences, 20(11), 2889–2903. https://doi.org/10.5194/nhess-20-2889-2020
   Web of Science ®Google Scholar
• Farahani, S., Behnam, B., & Tahershamsi, A. (2020b). Probabilistic seismic multi-hazard loss estimation of Iran gas trunklines. Journal of Loss Prevention in the Process Industries, 66, 104176. https://doi.org/10.1016/j.jlp.2020.104176
   Web of Science ®Google Scholar
• Farahani, S., Tahershamsi, A., & Behnam, B. (2020). Earthquake and post-earthquake vulnerability assessment of urban gas pipelines network. Natural Hazards, 101(2), 327–347. https://doi.org/10.1007/s11069-020-03874-4
   Web of Science ®Google Scholar
• Faturechi, R., & Miller-Hooks, E. (2015). Measuring the performance of transportation infrastructure systems in disasters: A comprehensive review. Journal of Infrastructure Systems, 21(1), 04014025. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000212
   Web of Science ®Google Scholar
• FEMA. (2012). Multi-hazard loss estimation methodology: Earthquake model. Department of Homeland Security, FEMA, Washington, DC, 235–260.
   Google Scholar
• Foulser-Piggott, R., & Goda, K. (2015). Ground-motion prediction models for arias intensity and cumulative absolute velocity for Japanese earthquakes considering single-station sigma and within-event spatial correlation. Bulletin of the Seismological Society of America, 105(4), 1903–1918. https://doi.org/10.1785/0120140316
   Web of Science ®Google Scholar
• Geological Survey of Iran[ Accessed: 2022-09-18]. (2001).
   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. https://doi.org/10.1007/s10950-008-9143-x
   Web of Science ®Google Scholar
• Guha-Sapir, D., Vos, F., Below, R., & Ponserre, S. (2012). Annual disaster statistical review 2011: The numbers and trends.
   Google Scholar
• Kameda, H. (2000). Engineering management of lifeline systems under earthquake risk. Bulletin of the New Zealand Society for Earthquake Engineering, 33(3), 248–264. https://doi.org/10.5459/bnzsee.33.3.248-264
   Google Scholar
• Karimiparidari, S. (2014). Seismic hazard analysis in Iran (475 years return period). International Institute of Earthquake Engineering and Seismology (IIEES).
   Google Scholar
• Koike, T., Takada, S., Ogawa, Y., Matsumoto, M., Tajima, T., & Hassani, N. (2004). Seismic damage predictions for the gas distribution systems in great Tehran, Iran. 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada, paper, (3394).
   Google Scholar
• Komakpanah, A., & Farajzadeh, M. (1996). Liquefaction susceptibility and opportunity macrozonation of Iran. Proceedings of the Fifth International Conference on Seismic Zonation. Nice, France, October 17-19, 1651–1658.
   Google Scholar
• Kothalkar, S., & Kadam, S. (2016). Seismic vulnerability assessment of an existing railway bridge. Journal of Civil and Environmental Engineering, 3, 553–557.
   Google Scholar
• Lai, T., Nasseri, A., Yin, Y., Katiyar, V., & Turel, M. (2014). Modeling Railway Damage due to Shake, Liquefaction, and Tsunami for the 2011 Tohoku Earthquake. International Efforts in Lifeline Earthquake Engineering, 267–274.
   Google Scholar
• Liu, Y., Wotherspoon, L., Nair, N. -K.C., & Blake, D. (2021). Quantifying the seismic risk for electric power distribution systems. Structure and Infrastructure Engineering, 17(2), 217–232. https://doi.org/10.1080/15732479.2020.1734030
   Web of Science ®Google Scholar
• Makdisi, F., Seed, H., & Norouzi, A. (1978). Attenuation relations for peak horizontal and vertical accelerations of earthquake ground motion in Iran: A preliminary analysis. The Railways of Islamic Republic of Iran, 104(2), 109–128.
   Google Scholar
• Norouzi, A. (2005). Attenuation relations for peak horizontal and vertical accelerations of earthquake ground motion in Iran: A preliminary analysis.
   Google Scholar
• Poulos, A., Espinoza, S., De la Llera, J., & Rudnick, H. (2017). Seismic risk assessment of spatially distributed electric power systems. 16th World Conf on Earthquake Eng Santiago, 1949–3029.
   Google Scholar
• The Railways of Islamic Republic of Iran [ Accessed: 2022-09-18]. (2005).
   Google Scholar
• Rathje, E. M., & Saygili, G. (2011). Estimating fully probabilistic seismic sliding displacements of slopes from a pseudoprobabilistic approach. Journal of Geotechnical and Geoenvironmental Engineering, 137(3), 208–217. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000431
   Web of Science ®Google Scholar
• Rathje, E. M., & Saygili, G. (2011). Estimating fully probabilistic seismic sliding displacements of slopes from a pseudoprobabilistic approach. Journal of Geotechnical and Geoenvironmental Engineering, 137(3), 208–217. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000431
   Web of Science ®Google Scholar
• Reiter, L. (1991). Earthquake hazard analysis: Issues and insights. Columbia University Press.
   Google Scholar
• Roohi, M., & Hernandez, E. M. (2020). Performance-based post-earthquake decision making for instrumented buildings. Journal of Civil Structural Health Monitoring, 10(5), 775–792. https://doi.org/10.1007/s13349-020-00416-1
   Web of Science ®Google Scholar
• Roohi, M., Li, J., & van de Lindt, J. (2022). Seismic functionality analysis of interdependent buildings and lifeline systems. 12th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Salt Lake City, UT.
   Google Scholar
• Roohi, M., van de Lindt, J. W., Rosenheim, N., Hu, Y., & Cutler, H. (2020). Implication of building inventory accuracy on physical and socio-economic resilience metrics for informed decision-making in natural hazards. Structure and Infrastructure Engineering, 17(4), 534–554.
   Web of Science ®Google Scholar
• Sadigh, K., Egan, J., & Youngs, R. (1986). Specification of ground motion for seismic design of long period structures,earthquake notes 57(1), 13.
   Google Scholar
• Saygili, G., & Rathje, E. M. (2009). Probabilistically based seismic landslide hazard maps: An application in Southern California. Engineering Geology, 109(3–4), 183–194.
   Web of Science ®Google Scholar
• Shinozuka, M., Shiraki, N., Kameda, H., Shinozuka, M., Murachi, Y., Dong, X., Y, Z., M, O., B, T., & S, P. (2000). A new approach to estimate a mixed model-based ground motion prediction equation, earthquake spectra. Effect of seismic retrofit of bridges on transportation networks. Earthquake Engineering and Engineering Vibration, 2, 665–684.
   Google Scholar
• Shojaeian, A., Farahani, S., Behnam, B., & Mashayekhi, M. (2021). Seismic resilience assessment of Tehran’s southern water transmission pipeline using GIS-based analyses. Journal of Numerical Methods in Civil Engineering, 6(2), 93–106.
   Google Scholar
• Tavakoli, B., & Pezeshk, S. (2007). A new approach to estimate a mixed model–based ground motion prediction equation. Earthquake Spectra, 23(3), 665–684.
   Web of Science ®Google Scholar
• Tsuchiya, S., Tatano, H., & Okada, N. (2007). Economic loss assessment due to railroad and highway disruptions. Economic Systems Research, 19(2), 147–162.
   Google Scholar
• Zhu, W., Liu, K., Wang, M., & Koks, E. E. (2020). Seismic risk assessment of the railway network of China’s mainland. International Journal of Disaster Risk Science, 11(4), 452–465.
   Web of Science ®Google Scholar