Machine learning-aided PSDM for dams with stochastic ground motions

Abstract

Probabilistic seismic demand models are widely used for structures to establish a relation between the engineering demand parameter (EDP) and ground motion intensity measures (IM). For the complex infrastructures such as dams two challenges in implementation of probabilistic seismic demand models are scarcity of the appropriate real ground motions, and computational limitation to perform hundreds of simulations. This paper addresses both concerns by using a series of stochastic ground motion records as an alternative for real ones, and also employing machine learning algorithms to predict the IM-EDP relation. A 3D concrete arch dam with foundation and reservoir interaction is used as a case study of a demanding system (as opposed to 2D framed building). A large group of about 600 real and stochastic ground motion records are used to analyze the coupled system. In addition, five machine learning algorithms were used to develop the predictive meta-models. This paper also highlights the feasibility of using stochastic ground motions to predict the probabilistic seismic demand meta-models and fragility curves from real records, and vice versa. While the outcomes illustrate promising results, they also show that the existing stochastic ground motion simulation models do not cover all the inherent characteristics of the real records.