Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning

• Published in: Applied sciences Vol. 15; no. 9; p. 4756
• Main Authors: Xu, Qiang, Xia, Ze, Huang, Gang, Li, Xuehua, Gao, Xu, Fan, Yukuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Accuracy; Aperture; Cameras; Coal mining; Cracks; Deep learning; Drilling; Geospatial data; identification method of structural planes; machine learning; Mines and mineral resources; Neural networks; program application; Roads & highways; Shear strength; structural planes of rock mass; Video recorders
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15094756

The accurate evaluation of rock mass quality and competent roadway-support decision-making requires the rapid and accurate acquisition of the distribution of structural planes in rocks. To address this need, a program was developed that uses deep learning to automatically recognize the structural plane in-borehole images. First, borehole images from 30 mines in China were collected during field tests, and the structural planes in the images were categorized into five types. Second, a deep Coral architecture based on a convolutional neural network (CNN) was established to automatically extract features from the borehole images and classify the structural planes therein. The experimental results indicate that the CNN model classifies the structural planes in the borehole images with an overall accuracy of 86%. Validation tests in field applications demonstrated recognition accuracies ranging from 0.76 to 1.0 compared to manual markings, meeting engineering requirements. Finally, based on the proposed method, an intelligent system to recognize surrounding rock fracture was developed. Engineering application cases are presented and discussed to demonstrate the method and confirm the accuracy of this approach. Compared with traditional classification methods, the proposed method rapidly recognizes and classifies structural planes in borehole images at low cost, with precision, and in a non-destructive and automated manner.