Abstract

Fragility curves that can be applicable for vulnerability modeling of various configurations of structures, such as bridges, are required to facilitate regional seismic risk assessment. However, it is cumbersome and time-consuming to develop unique fragility curves for each structure across a regional portfolio. One strategy that has been adopted to address this challenge is to group bridges into classes with similar design or structural performance. Traditionally, this grouping has been performed based on a relatively subjective identification of sub-classes. However, such an identification leads to a number of bridge classes with unwarranted grouping. To overcome this limitation, a new grouping technique based on an analysis of covariance on probabilistic seismic demand models is suggested in this paper. The proposed analysis of covariance based grouping helps to identify important attribute-related parameters and to create distinct bridge sub-classes. The effectiveness of the proposed approach is demonstrated in this paper through case studies of four concrete box-girder bridges in California. This is the first systematic approach to sub-binning bridge classes for the regional risk assessment. The results suggest that the proposed grouping method significantly reduces bridge sub-classes from all possible sub-class combinations. The proposed method is relevant and can be applicable to other bridge types.

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