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Damage to highway systems from recent earthquakes has emphasized the need for pre-event damage assessment of the existing highway systems so that the effective measures can be taken to reduce the loss from the future earthquake. In this paper, a Monte Carlo technique is presented which can be effectively used to simulate the states of bridge as well as highway network damage. With the aid of damage data, collected after the 1994 Northridge earthquake, the fragility curves are developed for each bridge on a limited access expressway network in Los Angeles County and Orange County in California, and classified in accordance with such attributes as whether it is a single span or multiple span, how much it is skewed and the condition of soil on which it is constructed. Monte Carlo simulations of states of bridge damage are performed based on these facility curves. A set of criteria and indices are then introduced, upon calibration with Northridge experience, for simulation of the damage state of each link and then the state of network damage when subjected to scenario earthquakes. The calibration is achieved by adjusting the criteria and indices by comparing the simulated states with the actual states of network damage under the Northridge earthquake. Computational time for one realization (simulation) of states of network damage is of the order of a few seconds. Therefore, the Monte Carlo simulation-based method of network damage assessment presented here can provide a post-earthquake response decision support system for highway networks; Since the purpose of the present study is to demonstrate the efficacy of the Monte Carlo simulation method, numerical examples are performed under the conditions that only bridges are seismically vulnerable, bridge fragility curves are available, and knowledge of the state of network damage suffices for government agencies and emergency response profession to make decisions as to how search/rescue/medical teams and emergency repair crews can be dispatched and how food, potable water and other emergency supplies can be transported by an optimal use of the remaining network capacity. In this study, the spatial distribution of ground motion intensity is estimated deterministically in terms of peak ground acceleration (PGA). The spatial variation of PGA due to modeling and other sources of uncertainty is expected to have some effect on the final result. This issue is currently under study.
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