Research status and development trends of intelligent and digitalized disaster prevention and safety protection in tunneling

Abstract

Conventional tunnel disaster prevention systems are constrained by several critical limitations, such as inade-quate accuracy in geological detection, oversimplified modeling, delayed simulation responses, subjective risk assessment, and coarse decision-making. These constraints hinder comprehensive situational awareness and sci-entific disaster control in complex tunnel environments. Artificial intelligence (AI) offers essential support for the digital-intelligent upgrading of tunnel disaster prevention by enabling automated interpretation of predic-tive data, multi-source information modeling, rapid disaster scenario simulation, and scientifically-grounded risk evaluation. This study systematically investigates digital-intelligent methods for tunnel disaster prevention and safety protection following the technical chain of “geological prediction-modeling-simulation-assessment and decision-making.” For geological information prediction, research focuses on improving the accuracy and effi-ciency of data analysis through algorithmic optimization to achieve rapid and reliable perception under complex conditions. In modeling, machine learning-based intelligent modeling approaches and digital twin (DT)-driven cyber-physical integration are discussed. Regarding disaster simulation, deep-learning-based surrogate models are summarized for their applications in disaster forecasting, effectively overcoming the high computational cost and response delay inherent in conventional simulation methods. In risk assessment and decision-making, the applications of machine learning (ML) and emerging large language models (LLMs) are examined, highlight-ing advances in risk identification, prediction, and reasoning. Despite these advancements, challenges persist, including strong reliance on limited data sources, lack of physical constraints and interpretability, insufficient generalization across scenarios, and restricted real-time capability. Future developments are expected to pursue deeper integration of data-driven and physics-informed approaches, improve the utilization of multi-source het-erogeneous data and scenario generalization, enhance model robustness and interpretability, and incorporate lightweight architectures with dynamic updating mechanisms, thereby enabling real-time perception, full-chain simulation, and intelligent risk management throughout the entire tunnel engineering processes.

Keywords

Tunnel disaster prevention and safety protection / Digital-intelligent technologies / Machine learning / Artificial intelligence / State of the art

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Mingyang Wang: Investigation, Formal analysis, Conceptualiza-tion. Yifan Xu: Methodology. Jie Hu: Resources, Project adminis-tration, Methodology. Xiaoli Rong: Supervision, Project administra-tion. Weitao Wu: Supervision, Project administration. Shaoshuai Shi: Project administration. Hao Lu: Project administration.

Acknowledgements

This study was supported by the National Natural Science Founda-tion of China (Grant No. 52478397).

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