Objective Most roof accidents in coal roadways occur in potential caving zones such as primary fissure-bearing zones and rock fracture zones. A significant approach to preventing roof accidents is to understand the geological features of roof strata in an accurate and timely manner and optimize support schemes and parameters of roofs. However, current methods for identifying the geological features of roof strata in roadways suffer from issues such as slow identification speeds, low efficiency, and high costs, thus failing to meet the demand for safe, efficient, and intelligent coal mining.
Methods This study proposed a neural network model based on the 1D convolutional neural network (1DCNN), the bidirectional long short-term memory (BiLSTM), and the convolutional block attention module (CBAM) (also referred to as the 1DCNN-BiLSTM-CBAM model). The correspondence between the acceleration signals of vibrations while drilling and the geological features of roof strata was established. Then, the acceleration signals of vibrations while drilling were acquired from intact, fractured, and fissured rock samples through drilling tests. These signals were used as training samples. Accordingly, the correspondence between different rock samples and the signals of vibrations while drilling was determined. Finally, the performance of various models was assessed using four classification indices: precision, recall, and F1-score.
Results and Conclusions The results indicate that the established 1DCNN-BiLSTM-CBAM model allowed for the end-to-end intelligent identification of geological features of rock layers, featuring a simplified identification process and enhanced identification efficiency. The training results of various models revealed that the 1DCNN-BiLSTM-CBAM model exhibited average accuracy, precision, recall, and F1-score of 99.22%, 99.26%, 99.21%, and 99.23%, respectively, outperforming the support vector machine (SVM), 1DCNN, and BiLSTM models. The experimental verification demonstrates that the 1DCNN-BiLSTM-CBAM model is effective in classifying and identifying the geological features of roof strata in coal roadways, with high classification and identification performance. Therefore, this model can meet the demand for real-time intelligent identification of the geological features of roof strata in roadways. This study presents an efficient identifying-while-drilling method for roadway roofs in coal mines, providing strong technical support for identifying potential caving zones in coal roadways and formulating roof support schemes. This study can serve as a reference for improving the safety guarantee technology for roadways in coal mines.
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