Probabilistic Fragility Analysis and Resilience Assessment of Bridges Subjected To Earthquake Mainshocks and Aftershocks

Abstract eng:
Notable past seismic events, such as the 2010 Chile earthquake, the 2011 Tohoku earthquake in Japan and the seismic events in Christchurch, New Zealand in 2011, have shown that the mainshock seismic event can trigger sequences of aftershocks. In such cases the time between the primary shock and the aftershocks might be not enough for repairing the mainshock-induced damages to structures; hence their vulnerability can significantly increase and consequently their recovery is delayed. This issue can be even more critical for highway bridges that are one of the most vulnerable components of transportation networks when exposed to earthquakes, since the higher risk imposed due to the successive shocks can have devastating effects to entire communities relying on them for their smooth functioning. Therefore, it is important to develop methodologies that capture the impact of incorporating aftershocks in the seismic vulnerability and resilience of highway bridges. This paper discusses a computationally efficient methodology for probabilistic fragility analysis and resilience assessment of bridges that explicitly incorporates the effects of aftershock seismic events in the seismic hazard description. This methodology is based on nonlinear time-history analysis for simulating the structural response, whereas a procedure is developed for generating mainshock-aftershock sequences through stochastic ground motion modeling to support the mainshock-aftershock earthquake hazard characterization. In this setting, mainshockinduced damage state-dependent aftershock fragilities are developed, which are ultimately utilized in conjunction with appropriate recovery models for evaluation of bridge functionality and resilience. The various model parameters characterizing the seismic hazard, structural and recovery models are considered as uncertain, and the bridge performance metrics (i.e., fragility, functionality, resilience) are probabilistically calculated. To facilitate adoption of complex structural and probability models, an efficient computational framework based on kriging surrogate modeling is used for estimating the bridge performance metrics through stochastic (Monte Carlo) simulation. The surrogate model is established in an input parameter space, composed of uncertain seismic hazard and structural parameters, and deterministic structural/geometrical bridge parameters, and therefore is utilized to facilitate development of parameterized fragilities. As an illustrative example fragility and resilience assessment of a typical bridge class in California under the effects of mainshocks and aftershocks is performed.

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