Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review

Abstract

Enhancements in monitoring and computational technology have facilitated data accessibility and utilization. Machine learning, as an integral component of the realm of computational technology, is renowned for its universality and efficacy, rendering it pervasive across various domains. Geotechnical disaster early warning systems serve as a crucial safeguard for the preservation of human lives and assets. Machine learning exhibits the capacity to meet the exigencies of prompt and precise disaster prediction, prompting substantial interest in the nexus of these two domains in recent decades. This study accentuates the deployment of machine learning in addressing geotechnical engineering disaster prediction issues through an examination of four types of engineering-specialized research articles spanning the period 2009 to 2024. The study elucidates the evolution and significance of machine learning within the domain of geotechnical engineering disaster prediction, with an emphasis on data analytics and modeling. Addressing the lacunae in existing literature, a user-friendly front-end graphical interface, integrated with machine learning algorithms, is devised to better cater to the requisites of engineering professionals. Furthermore, this research delves into a critical analysis of the prevalent research limitations and puts forth prospective investigational avenues from an applied standpoint.