Seismic Resilience Assessment for Steel-concrete Composite Bridges including Impacts of Near-fault Earthquakes
Keywords:
Seismic resilience, occurrence probability, steel-concrete composite bridges, near-fault earthquakes assessmentAbstract
This paper proposes a seismic resilience assessment method for steel-concrete composite bridges (SCCB) considering near-fault earthquake hazards. Based on conventional probabilistic seismic hazard disaggregation analysis, a correction factor is defined to represent the proportion of the occurrence probability of the near-fault pulse-like, near-fault non-pulse-like and far-field earthquake conditioned on a given intensity level concerning the total occurrence probability of all earthquakes. The parameters of functionality recovery functions are modified using the factor proposed, and then the restoration processes after each type of earthquake are estimated. Correspondingly, vulnerabilities of a typical SCCB under near-fault and far-field earthquakes are developed as a case study. Based on the seismic hazard and fragility results, the seismic risk for each type of earthquake in a 50-year horizon is estimated. After that, the modified functionality recovery function is derived from the expected functionality. For implementation of the method proposed, the expected seismic resilience indices of a typical SCCB involved in the SEQBRI project are estimated, and the seismic resilience assessment is conducted. For comparison analysis, the seismic resilience assessment without considering earthquake type is also conducted using the same bridge. The result shows that the seismic resilience of bridges in near-fault earthquake scenarios can be analyzed by the method proposed, and reducing the structural vulnerability under low-intensity level earthquakes and improving the structural recovery efficiency for slight and moderate damage states are more meaningful to enhance the seismic resilience of bridges.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Yang Liu, Da-Gang Lu, Fabrizio Paolacci, Gianluca Quinci, Sourabh Vern
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.