Identification of damage states of load-bearing rocks using infrared radiation monitoring methods

Abstract

The online identification of rock damage states is crucial for safety monitoring in geotechnical and mining engineering. By analyzing spatiotemporal evolution patterns of infrared radiation in various rock damage states, we established the first infrared temperature field dataset for rock damage state identification. We then constructed a deep convolutional neural network, RESD-CNN, and performed its training and optimization. Results showed that infrared radiation patterns of different rock samples exhibit similarities. RESD-CNN achieved outstanding performance in identifying rock damage states with metrics of ACC 99.04%, Precision 99.39%, Recall 99.52%, and F1-score 99.46% on the validation set. Generalization tests on datasets of different rock types revealed that RESD-CNN significantly outperformed traditional classification methods, demonstrating the feasibility of infrared radiation technology for intelligent coal rock damage identification. This research provides a crucial foundation for developing online identification and early warning systems for rock damage evolution in engineering.