华中科技大学学报（自然科学版）2026，Vol.54Issue(8)：31-50,20.DOI:10.13245/j.hust.260171
深部岩体损伤演化与灾变预警中的机理约束数据驱动方法
Mechanism-constrained data-driven methods for damage evolution and disaster early warning in deep rock masses
摘要
Abstract
As deep resource exploitation continues to extend into high-stress,high-temperature,and strongly disturbed environments,damage evolution in rock masses is becoming increasingly severe,and disasters such as rockburst,spalling,and surrounding-rock instability are occurring more frequently.In view of the difficulty of traditional empirical criteria and single-parameter threshold methods in meeting the demand for real-time warning under complex geological conditions,recent progress in mechanism-constrained data-driven methods for deep rock-mass damage evolution and disaster early warning was analyzed.The multi-source data basis,task paradigms,and methodological framework were systematically summarized,with emphasis on the applications and limitations of machine learning,deep learning,interpretable learning,uncertainty representation,and physics-informed neural networks in rockburst classification,precursor identification,strength prediction,parameter inversion,and digital-twin warning.Key bottlenecks,including data quality,label reliability,cross-scale representation,mechanism embedding and closed-loop updating,were also discussed.Future work should strengthen unified dataset construction,physical-prior embedding and online updating mechanisms.Taking the"four layer embedding path of mechanism constraints"(feature layer embedding objective function layer embedding structural layer embedding system layer embedding)as the main line,the embedding form of rock mechanics mechanism knowledge in data-driven models,the special requirements and representative methods of deep environments were explained layer by layer.In typical tasks such as rock burst classification,damage stage identification,strength prediction and parameter inversion,drilling perception,and digital twin closed-loop,the form and link of mechanism constraints are clarified,and specific problems that are difficult to solve with pure data-driven methods were solved.关键词
深部岩体/损伤演化/灾变预警/机理约束数据驱动/可解释机器学习Key words
deep rock mass/damage evolution/disaster early warning/mechanism-constrained data-driven methods/interpretable machine learning分类
建筑与水利引用本文复制引用
郝家旺,靳灿肃,李浩然,张天乐,李庆文,孟繁志..深部岩体损伤演化与灾变预警中的机理约束数据驱动方法[J].华中科技大学学报（自然科学版）,2026,54(8):31-50,20.基金项目
河北省自然科学基金资助项目(E2026210081) （E2026210081）
国家自然科学基金资助项目(52574087). （52574087）
