Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

The development of fragility curves is an important step in seismic risk assessment within the scope of performance-based earthquake engineering. The goal of this work is to use machine learning methods (regression-based tools) to forecast the large-span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large-span precast parking structures, including: neural networks, genetic algorithm-based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large-span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization-based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention.