Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Abstract

AbstractEvaluating the response statistics of nonlinear structures constitutes a key issue in engineering design. Hereby, the Monte Carlo method has proven useful, although the computational cost turns out to be considerably high. In particular, around the design point of the system near structural failure, a reliable estimation of the statistics is unfeasible for complex high‐dimensional systems. Thus, in this paper, we develop a machine‐learning‐enhanced Monte Carlo simulation strategy for nonlinear behaving engineering structures. A neural network learns the response behavior of the structure subjected to an initial nonstationary ground excitation subset, which is generated based on the spectral properties of a chosen ground acceleration record. Then using the superior computational efficiency of the neural network, it is possible to predict the response statistics of the full sample set, which is considerably larger than the initial training sample set. To ensure a reliable neural network response prediction in case of rare events near structural failure, we propose to extend the initial training sample set increasing the variance of the intensity. We show that using this extended initial sample set enables a reliable prediction of the response statistics, even in the tail end of the distribution.HIGHLIGHTSA new Monte Carlo method is developed that provides the response statistics of a nonlinear system in the tail end of the distribution.A nonstationary filter is applied to a Gaussian white noise to generate realistic artificial earthquake records.An extended training strategy using neural networks is proposed to improve the reliability of the method in the tail end of the distribution.The new strategy reveals a significant speedup as well as a prediction of the response statistics in the tail end with high confidence.