Risk assessment of structures subjected to natural hazards is one of the primary objectives of the performance based engineering. Nevertheless, current practices evaluate the risk imposed by each hazard independently. This presentation will deal with the simultaneous risk imposed on civil infrastructure by seismic and wind hazards and will quantify it in terms of probabilities of damage and potential economic losses through a multi-hazard probabilistic framework.

The paper presents a computationally efficient algorithm to integrate a probabilistic, non-Gaussian parameter estimation approach for nonlinear finite element models with the performance-based earthquake engineering (PBEE) framework for accurate performance evaluations of instrumented civil infrastructures. The algorithm first utilizes a minimum variance framework to fuse predictions from a numerical model of a civil infrastructure with its measured behavior during a past earthquake to update the parameters of the numerical model that is, then, used for performance prediction of the civil infrastructure during future earthquakes. A nonproduct quadrature rule, based on the conjugate unscented transformation, forms an enabling tool to drive the computationally efficient model prediction, model-data fusion, and performance evaluation. The algorithm is illustrated and validated on Meloland Road overpass, a heavily instrumented highway bridge in El Centro, CA, which experienced three moderate earthquake events in the past. The benefits of integrating measurement data into the PBEE framework are highlighted by comparing damage fragilities of and annual probabilities of damages to the bridge estimated using the presented algorithm with that estimated using the conventional PBEE approach.

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