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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.

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This paper presents an optimization procedure for the restoration activities associated with the bridges of a transportation network severely damaged by an earthquake. The design variables are (i) the time intervals between the occurrence of the distress and the start of the interventions on each bridge of the network; and (ii) the restoration pace of the interventions, which represents a measure of the funding allocated to each bridge. The objectives of the optimization are the maximization of the network resilience, the minimization of the time required to reach a target functionality level, and the minimization of the total cost of the restoration activities. Because the first two objectives clearly conflict with the last one, the optimization procedure does not provide a unique solution, but an entire set of Pareto solutions. A numerical example involving a complex, existing transportation network in Santa Barbara, California, illustrates the capabilities of the proposed methodology.

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