Wenjun Zhang, Wuqi Zhang, Gaole Zhang, Jun Huang, Minggeng Li, Xiaohui Wang, Fei Ye, Xiaoming Guan
{"title":"Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images","authors":"Wenjun Zhang, Wuqi Zhang, Gaole Zhang, Jun Huang, Minggeng Li, Xiaohui Wang, Fei Ye, Xiaoming Guan","doi":"10.1007/s11709-023-0002-1","DOIUrl":null,"url":null,"abstract":"<p>For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for <i>in situ</i> rock lithology classification utilizing geological images of the tunnel face.</p>","PeriodicalId":12476,"journal":{"name":"Frontiers of Structural and Civil Engineering","volume":"86 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Structural and Civil Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11709-023-0002-1","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.
期刊介绍:
Frontiers of Structural and Civil Engineering is an international journal that publishes original research papers, review articles and case studies related to civil and structural engineering. Topics include but are not limited to the latest developments in building and bridge structures, geotechnical engineering, hydraulic engineering, coastal engineering, and transport engineering. Case studies that demonstrate the successful applications of cutting-edge research technologies are welcome. The journal also promotes and publishes interdisciplinary research and applications connecting civil engineering and other disciplines, such as bio-, info-, nano- and social sciences and technology. Manuscripts submitted for publication will be subject to a stringent peer review.