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

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, timevariant reliability analysis, three-dimensional reliability analysis of practical cases, and ML model selection and optimization.

Automated summary

Geotechnical reliability analysis has gained great attention in recent decades by considering geotechnical uncertainties and evaluating the stability of geotechnical structures from a probabilistic perspective. With the rapid development of artificial intelligence techniques, various machine learning algorithms have been successfully applied in this field. However, a systematic summary of the subject is still lacking. This paper aims to provide a comprehensive review of machine learning applications in geotechnical reliability analysis, summarizing achievements and outlining future research possibilities.