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UBC Theses and Dissertations

Probabilistic models, methods, and software for evaluating risk to civil infrastructure Mahsuli, Mojtaba


The fundamental objective in this thesis is to advance the state-of-the-art in the field of infrastructure risk analysis. To meet this objective, probabilistic models, methods, and software are developed and applied. The work is conducted within a new reliability-based approach, in which reliability methods are employed to compute risk. Risk, in this context, means the probability of exceeding monetary loss. Evaluating such probabilities requires probabilistic models for hazards, response, damage, and loss. This motivates the contributions in this thesis, which are summarized as follows: First, a new computer program, called Rt, is developed. It is tailored to conduct reliability analysis with many probabilistic models. It orchestrates the interaction of models by means of a new object-oriented software design. Each model and analysis algorithm is represented by an object. As a result, new models and algorithms are easily implemented without modifying existing code. Another novelty is the parameterization of uncertainties, decisions, and model responses. This has several implications; one being that, in each step of an analysis, only the models affected by new parameter realizations are evaluated. Another novelty is the computation of “direct differentiation” response sensitivities in a multi-model analysis. Second, a library of new probabilistic models are developed and implemented in Rt. The models are intended for use in regional seismic risk analysis. The library includes new models for location and magnitude of earthquakes, and response, damage, and loss of building. The library also features damage and loss models for entire regions. Third, the models are applied in a risk analysis for the Vancouver metropolitan region in Canada. The primary results are “loss curves” and “hazard curves,” which show the probability of exceeding loss and spectral acceleration, respectively. As another example of results, it is found that Richmond is the most vulnerable municipality. Finally, new sensitivity measures are developed to prioritize the allocation of resources to mitigate risk and to reduce model uncertainty. In particular, these measures identify the buildings whose retrofit yields the most reduction in regional risk. They also identify the models whose improvement yields the most reduction in uncertainty.

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