PSI - Issue 78
Yang Liu et al. / Procedia Structural Integrity 78 (2026) 2030–2037
2037
stages, the expected seismic resilience of bridges can be assessed including the impact of near-fault earthquakes. • The seismic resilience of the bridge in RM and SM condition have been compared and the relative error is about 4%, with more conservative results for RM. • Involving the uncertainties of the parameters in the functionality recovery functions, the relative errors in using the SM conditions may reach 15%. This indicates that the proposed method (RM) may significantly improve the accuracy of the seismic resilience assessment. In conclusion, the proposed method is recommended for assessing the seismic resilience of bridges located close to fault lines. Although these results are based on a particular steel-concrete composite bridge, they could serve as a useful starting point when examining other types of bridge. The calculation process and methodology presented in this paper could be employed to analyse the impact on various bridges at a regional level. For this, however, an additional database of vulnerable components would be required. This would certainly be a worthwhile extension to the present investigation. References Abbiati G., Cazzador E., Alessandri S., Bursi O.S., Paolacci F., De Santis S., (2018), Experimental characterization and component-based modeling of deck-to-pier connections for composite bridges, Journal of Constructional Steel Research, Vol. 50, pag. 31-50, DOI: 10.1016/j.jcsr.2018.08.005 Bocchini, P., & Dan, M. F. (2012). Probabilistic functionality recovery model for resilience analysis. Bridge Maintenance, Safety, Management, Resilience and Sustainability (pp.1920-1927). Bruneau M, Chang SE, Eguchi RT, Lee GC, O’Rourke TD, Reinhorn AM, Shinozuka M, Tierney K, Wallace AW, von Winterfeldt D (2003) A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra 19(4):733 – 752 Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2010). Framework for analytical quantification of disaster resilience. Engineering Structures, 32(11), 3639-3649. Decò A, Bocchini P, Frangopol DM (2013) A probabilistic approach for the prediction of seismic resilience of bridges. Earthquake Engineering & Structural Dynamics 42(10): 1469-1487 Giovenale P, Cornell C, Esteva L. Comparing the adequacy of alternative ground motion intensity measures for the estimation of structural responses. Earthq Eng Struct Dyn. 2004;33(8):951-979. HAZUS. (2009) MR4 earthquake model technical manual. Whashington (DC): Department of Homeland Security, Federal Emergency Management Agency, Mitigation Division. Kalemi B., Caputo A.C., Corritore D., Paolacci F.(2023), A probabilistic framework for the estimation of resilience of process plants under Na‑Tech seismic events. Bulletin of Earthquake Engineering, DOI:10.1007/s10518 -023-01685-z Karamlou A, Bocchini P (2015) Computation of bridge seismic fragility by large-scale simulation for probabilistic resilience analysis. Earthquake Engineering & Structural Dynamics 44(12): 1959-1978 Liu Y., Lu D., & Paolacci F. (2016). Probabilistic seismic resilience analysis for bridges shocked by near-fault pulse-like ground motions //Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks-Proceedings of the Eighth International Conference on Bridge Maintenance, Safety and McKenna F, Mazzoni S, Scott MH, Fenves GL (2007) OpenSees command language manual. Pacific Earthquake Engineering Research Center, University of California, Berkeley, July Paolacci F, et al., “Performance -based earthquake engineering analysis of short medium span steel- concrete composite bridges”, Final Report, SEQBRI Project, Contr. No: RFSR-CT-2012-00032, Research Fund for Coal and Steel. ISBN 978-92-79 65612-5, ISSN 1831-9424, DOI:10.2777/48012 Paolacci F., Corritore D., (2023), Performance-Based Earthquake Engineering Analysis of Short-Medium Span Steel-Concrete Composite Bridges, Lecture Notes in Civil Engineering, Volume 351, Pages 682 - 6962023 Italian Concrete Conference, ICC 2021Virtual, Online14 April 2021through 17 April 2021, DOI: 10.1007/978-3-031-37955-0_49 Shahi, S. K. (2013). A probabilistic framework to include the effects of near-fault directivity in seismic hazard assessment (Doctoral dissertation, Stanford University). Shinozuka, M., Zhou, Y., Kim, S. H., Murachi, Y., Banerjee, S., Cho, S., & Chung, H. (2005). Socio-economic effect of seismic retrofit implemented on bridges in the Los Angeles highway network. Final Report to the California Department of Transportation. Tothong, P., & Cornell, C. A. (2008). Structural performance assessment under near-source pulse-like ground motions using advanced ground motion intensity measures. Earthquake Engineering & Structural Dynamics, 37(37), 1013-1037. U.S. geological hazard science center (2017a) https://geohazards.usgs.gov/deaggint/2008/. Accessed 01 February 2017 Zhai C, Chang Z, Li S, Chen ZQ, Xie L (2013) Quantitative identification of near-fault pulse-like ground motions based on energy. Bulletin of the Seismological Society of America 103(5): 2591-2603.
Made with FlippingBook Digital Proposal Maker