Collapse fragility curve development using Monte Carlo simulation and artificial neural network

Abstract

Seismic fragility curves represent likelihood of structures meeting various damage stages. Epistemic as well as aleatory uncertainties associated with seismic loads and structural behaviors are usually taken into account in order to analytically develop such curves. Such structural analyses are time-consuming, demanding extensive computational efforts. In this study, in order to reduce this endeavor, artificial neural network method is applied to develop structural seismic fragility curves under collapse damage state, considering effects of record-to-record variability and modeling parameter uncertainties. Structural analyses are performed for a limited number of scenarios of structures under a limited number of recorded strong ground motion records. Probability distribution for each modeling parameter was used to simulate each structure scenario. Incremental dynamic analysis was used to assess spectral acceleration associated with collapse limit state for each structure scenario. The results of the analyses were used to train and validate a three-layered artificial neural network, and Monte Carlo simulation is implemented based on trained neural network for a sample moment-resisting steel frame in order to derive collapse fragility curve. Application of the proposed method enhances accuracy of identical computational run time compared with response surface–based method.