Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning
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 pl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4756 |
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| author | Qiang Xu Ze Xia Gang Huang Xuehua Li Xu Gao Yukuan Fan |
| author_facet | Qiang Xu Ze Xia Gang Huang Xuehua Li Xu Gao Yukuan Fan |
| author_sort | Qiang Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-be2544977c3c497abd6ddc5e014b49d3 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-be2544977c3c497abd6ddc5e014b49d32025-08-20T01:49:27ZengMDPI AGApplied Sciences2076-34172025-04-01159475610.3390/app15094756Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep LearningQiang Xu0Ze Xia1Gang Huang2Xuehua Li3Xu Gao4Yukuan Fan5Key Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education, Xuzhou 221116, ChinaKey Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education, Xuzhou 221116, ChinaKey Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education, Xuzhou 221116, ChinaKey Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education, Xuzhou 221116, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaKey Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education, Xuzhou 221116, ChinaThe 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.https://www.mdpi.com/2076-3417/15/9/4756structural planes of rock massmachine learningidentification method of structural planesdeep learningprogram application |
| spellingShingle | Qiang Xu Ze Xia Gang Huang Xuehua Li Xu Gao Yukuan Fan Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning Applied Sciences structural planes of rock mass machine learning identification method of structural planes deep learning program application |
| title | Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning |
| title_full | Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning |
| title_fullStr | Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning |
| title_full_unstemmed | Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning |
| title_short | Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning |
| title_sort | research and application of structural plane identification for roadway surrounding based on deep learning |
| topic | structural planes of rock mass machine learning identification method of structural planes deep learning program application |
| url | https://www.mdpi.com/2076-3417/15/9/4756 |
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