A Physics-Constrained Method for the Precise Spatiotemporal Prediction of Rock-Damage Evolution

Accurately predicting the spatiotemporal evolution of rock-damage zones is vital for underground engineering safety. Using three-dimensional data obtained from uniaxial compression–acoustic emission tests, this study addresses the key limitations of existing data-driven methods, which struggle with spatial heterogeneity and often yield predictions that deviate from fundamental fracture-mechanics principles. To overcome these challenges, we propose a physics-constrained spatiotemporal STConvLSTM framework that integrates a density-adaptive point cloud–voxel conversion mechanism for improved 3D representation, a composite loss incorporating structural and physics-based constraints, and a multi-level encoder–processor–decoder architecture enhanced by 3D convolutions, attention modules, and residual connections. Experimental results demonstrate superior accuracy and physical consistency, achieving 92.6% accuracy and an F1-score of 0.947, outperforming ConvLSTM and UNet3D baselines. The physics-aware constraints effectively suppress non-physical divergence and yield damage morphologies that better align with expected fracture-mechanics behavior. These findings show that coupling data-driven learning with physics-based regularization substantially enhances model reliability and interpretability. Overall, the proposed framework offers a robust and practical paradigm for 3D damage-evolution modeling, supporting more-dependable early-warning, stability assessment, and intelligent support-design applications in underground engineering.