  A framework for the generation of bridge-specific fragility utilizing the
capabilities of machine learning and stripe-based approach is presented in this
paper. The proposed methodology using random forests helps to generate or
update fragility curves for a new set of input parameters with less
computational effort and expensive re-simulation. The methodology does not
place any assumptions on the demand model of various components and helps to
identify the relative importance of each uncertain variable in their seismic
demand model. The methodology is demonstrated through the case studies of
multi-span concrete bridges in California. Geometric, material and structural
uncertainties are accounted for in the generation of bridge models and
fragility curves. It is also noted that the traditional lognormality assumption
on the demand model leads to unrealistic fragility estimates. Fragility results
obtained the proposed methodology curves can be deployed in risk assessment
platform such as HAZUS for regional loss estimation.
