Deep learning-based methods in structural reliability analysis: a review

Abstract

One of the most significant and growing research fields in mechanical and civil engineering is structural reliability analysis (SRA). A reliable and precise SRA usually has to deal with complicated and numerically expensive problems. Artificial intelligence-based, and specifically, Deep learning-based (DL) methods, have been applied to the SRA problems to reduce the computational cost and to improve the accuracy of reliability estimation as well. This article reviews the recent advances in using DL models in SRA problems. The review includes the most common categories of DL-based methods used in SRA. More specifically, the application of supervised methods, unsupervised methods, and hybrid DL methods in SRA are explained. In this paper, the supervised methods for SRA are categorized as multi-layer perceptron, convolutional neural networks, recurrent neural networks, long short-term memory, Bidirectional LSTM and gated recurrent units. For the unsupervised methods, we have investigated methods such as generative adversarial network, autoencoders, self-organizing map, restricted Boltzmann machine, and deep belief network. We have made a comprehensive survey of these methods in SRA. Aiming towards an efficient SRA, DL-based methods applied for approximating the limit state function with first/second order reliability methods, Monte Carlo simulation (MCS), or MCS with importance sampling. Accordingly, the current paper focuses on the structure of different DL-based models and the applications of each DL method in various SRA problems. This survey helps researchers in mechanical and civil engineering, especially those who are engaged with structural and reliability analysis or dealing with quality assurance problems.