人工智能在深部巷道围岩动力响应的应用研究进展

Review of artificial intelligence applications in dynamic response analysis of deep mine rock masses

摘要

随着全球金属矿山深度逐步进入千米以下，深部“三高一扰动”环境诱发的岩爆、冲击地压、大变形和分区破裂等动力灾害日益频发。传统力学方法在时效性、精度与不确定性量化方面面临严峻挑战。人工智能（artificial intelligence，AI）技术为小样本、高维非线性的围岩动力响应问题提供了新的解决路径。基于Web of Science、Scopus和CNKI数据库，检索2000年至今的相关文献，经系统评价、去重与择优筛选后最终保留125篇，结合CiteSpace与VOSviewer进行计量分析，发现AI在该领域的应用呈“萌芽-发展-爆发”三阶段演化特征。将现有研究成果整合为“感知-预测-控制”三大模块系统，详细评述了多源传感网络、智能预测模型与自适应控制策略的研究进展，重点分析了物理信息神经网络（physics-informed neural network，PINN）、数字孪生（digital twin，DT）与联邦学习（federated learning，FL）等前沿技术方向，并指出当前面临的小样本、可解释性、数据融合、实时性与标准化等挑战。最后，展望了AI在深部矿山动力灾害智能防控中的未来发展方向，为相关研究与实践提供参考。

Abstract

As global metal mines progressively extend below 1 000 meters，dynamic disasters induced by the “three highs and one disturbance” environment in deep zones-including rock bursts，large deformations，and zonal disintegration-are occurring with increasing frequency. Traditional mechanical methods face severe challenges in timeliness，accuracy，and uncertainty quantification. Artificial intelligence （AI） technology offers new solutions to addressing small-sample，high-dimensional nonlinear rock mass dynamic response problems. This study retrieved relevant literature from 2 000 to the present based on Web of Science，Scopus，and CNKI databases. After systematic evaluation，deduplication，and quality screening，a total of 125 papers were ultimately retained. Combining CiteSpace and VOSviewer for quantitative analysis，it was found that AI applications in this field exhibit a three-stage evolutionary pattern:“infancy-development-explosion”. This paper integrates the existing research into a three-module system:“perception-prediction-control”. It provides a detailed review of progress in research on multi-source sensor networks，intelligent prediction models，and adaptive control strategies. Key analysis focuses on cutting-edge technologies such as physics-informed neural network （PINN），digital twin （DT），and federated learning （FL），while identifying current challenges including small sample sizes，explainability，data fusion，real-time performance，and standardization. Last，this paper outlines future development directions for AI in the intelligent prevention and control of dynamic disasters in deep mines，providing reference for related research and practice.

关键词

深部巷道 / 围岩动力响应 / 人工智能 / 机器学习 / 岩爆预警 / 智能支护

Key words

deep tunnel / rock mass dynamic response / artificial intelligence / machine learning / rockburst early warning / intelligent support

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基金资助

国家自然科学基金重点资助项目(52438007)

北京建筑大学培育项目专项资金资助项目(X25027)

2024年北京市高层次留学人才回国资助计划(21997124001)