School of Environment and Resources, Xiangtan University, Xiangtan, 411105, China.
Changsha Institute of Mining Research Co., Ltd., Changsha, 410012, China.
Sci Rep. 2023 Jun 18;13(1):9867. doi: 10.1038/s41598-023-37098-w.
The destructive behavior of rocks and the evolution behavior of cracks are highly correlated. With the continuous development process of crack, the stress state of rock is constantly broken until entirely failed, so it is necessary to study the spatial and temporal behavior characteristics of the crack in the process of rock destruction. In this paper, the destruction process of phyllite specimens is analyzed by thermal imaging technology, and the temperature evolution process of the crack is studied to explore the infrared characteristics of the crack evolution process. Furthermore, a model for predicting rock destruction time is proposed based on Bi-LSTM recurrent neural network model combined with Attention mechanism. The results show that: (1) During the development of rock cracks, the rock surface shows a stable dynamic infrared response, and shows different evolutionary characteristics in different stages, mainly including temperature reduction in the compaction stage, temperature rise in the elastic and plastic stages, and temperature peaks in the failure stage; (2) During the evolution of the crack, rock destruction has a significant control effect on the IRT field along the fracture tangential and normal direction, and its distribution has the volatility controlled by the time; (3) The recurrent neural network method is used to predict the rock failure time, the results can be used as a method to predict the time of rock destruction, and it can be further put forward the corresponding protective measures accordingly, to maintain the long-term stability of the rock mass.
岩石的破坏行为与裂缝的演化行为高度相关。随着裂缝的不断发展，岩石的应力状态不断被破坏，直至完全失效，因此有必要研究岩石破坏过程中裂缝的时空行为特征。本文利用热成像技术分析了千枚岩试件的破坏过程，研究了裂缝的温度演化过程，探讨了裂缝演化过程的红外特征。此外，还提出了一种基于 Bi-LSTM 递归神经网络模型结合注意力机制的岩石破坏时间预测模型。结果表明：（1）在岩石裂缝的发育过程中，岩石表面呈现出稳定的动态红外响应，并在不同阶段表现出不同的演化特征，主要包括压密阶段的温度降低、弹塑性阶段的温度升高和破坏阶段的温度峰值；（2）在裂缝的演化过程中，岩石破坏对裂缝切向和法向的 IRT 场具有显著的控制作用，其分布具有时间控制的波动性；（3）利用递归神经网络方法对岩石破坏时间进行预测，其结果可作为预测岩石破坏时间的方法，可据此采取相应的防护措施，以维持岩体的长期稳定。
