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A framework for the generation of bridge-specific fragility utilizing thecapabilities of machine learning and stripe-based approach is presented in thispaper. The proposed methodology using random forests helps to generate orupdate fragility curves for a new set of input parameters with lesscomputational effort and expensive re-simulation. The methodology does notplace any assumptions on the demand model of various components and helps toidentify the relative importance of each uncertain variable in their seismicdemand model. The methodology is demonstrated through the case studies ofmulti-span concrete bridges in California. Geometric, material and structuraluncertainties are accounted for in the generation of bridge models andfragility curves. It is also noted that the traditional lognormality assumptionon the demand model leads to unrealistic fragility estimates. Fragility resultsobtained the proposed methodology curves can be deployed in risk assessmentplatform such as HAZUS for regional loss estimation.

Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques