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A probabilistic approach for the prediction of seismic resilience of bridges
Author(s) -
Decò Alberto,
Bocchini Paolo,
Frangopol Dan M.
Publication year - 2013
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.2282
Subject(s) - latin hypercube sampling , resilience (materials science) , probabilistic logic , bridge (graph theory) , fragility , event (particle physics) , monte carlo method , engineering , seismic hazard , incremental dynamic analysis , reliability engineering , computer science , structural engineering , civil engineering , seismic analysis , statistics , mathematics , artificial intelligence , medicine , physics , chemistry , quantum mechanics , thermodynamics
This paper proposes a probabilistic approach for the pre‐event assessment of seismic resilience of bridges, including uncertainties associated with expected damage, restoration process, and rebuilding/rehabilitation costs. A fragility analysis performs the probabilistic evaluation of the level of damage (none, slight, moderate, extensive, and complete) induced on bridges by a seismic event. Then, a probabilistic six‐parameter sinusoidal‐based function describes the bridge functionality over time. Depending on the level of regional seismic hazard, the level of performance that decision makers plan to achieve, the allowable economic impact, and the available budget for post‐event rehabilitation activities, a wide spectrum of scenarios are provided. Possible restoration strategies accounting for the desired level of resilience and direct and indirect costs are investigated by performing a Monte Carlo simulation based on Latin hypercube sampling. Sensitivity analyses show how the recovery parameters affect the resilience assessment and seismic impact. Finally, the proposed approach is applied to an existing highway bridge located along a segment of I‐15, between the cities of Corona and Murrieta, in California. Copyright © 2013 John Wiley & Sons, Ltd.