Machine learning classifier-based dynamic surrogate model for structural reliability analysis

Abstract

The performance functions of large-scale complex structures are often implicit and nonlinear, which leads to problems such as high computational costs and low computational accuracy in reliability calculation. In this regard, a dynamic machine learning classifier surrogate model based on Monte Carlo simulation (DMLC-MCS) is proposed. The training sample points are generated by the Markov chain Monte Carlo (MCMC) method and numerical analysis to create the training sample dataset, and the surrogate model based on machine learning classifiers (MLCs) are used to reconstruct the limit state function (LSF). Then, samples are extracted by MCS technique, and the LSF values are predicted by the trained surrogate model. An iterative process is proposed around the most probable point (MPP), and the failure probability obtained by the MCS technique is taken as the convergence condition. If the convergence condition is not satisfied, the MPP information is added to the original sample set to refine the surrogate model. Compared with the traditional reliability method, the proposed method significantly reduces the computational cost on the premise of ensuring high accuracy. In addition, the method is easy to combine with numerical analysis and is proven to be applicable for reliability analysis of real-world complex engineering problems.