Comprehensive review of machine learning in geotechnical reliability analysis: Algorithms, applications and further challenges

• Published in: Applied soft computing Vol. 136; p. 110066
• Main Authors: Zhang, Wengang, Gu, Xin, Hong, Li, Han, Liang, Wang, Lin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023
• Subjects: Geotechnical engineering; Machine learning; Reliability analysis; Uncertainty; ANN; CNN; Uncertainty; BF; RLEM; RV; ELM; SLFN; RVM; GPR; Geotechnical engineering; SVM; FS; SVR; BPNN; 2D; LS-SVM; XGBoost; Pf; RBF; Reliability analysis; GA; LSF; ML; AI; CSRSM; PSO; RSM; FEM; 3D; PCE; BCS; RF; MARS; ARVM; ASVM; Machine learning; RFEM; GCV; MCS
• DOI: 10.1016/j.asoc.2023.110066
• ISSN: 1568-4946

Geotechnical reliability analysis provides a novel way to rationally take the underlying geotechnical uncertainties into account and evaluate the stability of geotechnical structures by failure probability (or equivalently, reliability index) from a probabilistic perspective, which has gained great attention in the past few decades. With the rapid development of artificial intelligence techniques, various machine learning (ML) algorithms have been successfully applied in geotechnical reliability analysis and the number of relevant papers has been increasing at an accelerating pace. Although significant advances have been made in the past two decades, a systematic summary of this subject is still lacking. To better conclude current achievements and further shed light on future research, this paper aims to provide a state-of-the-art review of ML in geotechnical reliability analysis applications. Through reviewing the papers published in the period from 2002 to 2022 with the topic of applying ML in the reliability analysis of slopes, tunneling, and excavations, the pros and cons of the developed methods are explicitly tabulated. The great achievements that have been made are systematically summarized from two major aspects. In addition, the four potential challenges and prospective research possibilities underlying geotechnical reliability analysis are also outlined, including multisensor data fusion, time-variant reliability analysis, three-dimensional reliability analysis of practical cases, and ML model selection and optimization. •A state-of-the-art review of ML in geotechnical reliability analysis applications.•Several commonly used ML algorithms and some latest advanced ML methods are summarized.•The potential challenges and prospective research possibilities are outlined.