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| Intelligent assessment of surrounding rock grade of tunnel face based on multi-scale geological feature enhancement and deep convolutional network |
| ZHONG Hao1, ZHANG Yongping2, CAI Xianqing1*,YUAN Song1,KONG Qingxuan3, SUN Hao1,GUO Sheng4
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| (1. Sichuan Communication Surveying & Design Institute Co., Ltd., Chengdu 610031, Sichuan, China; 2.Sichuan Provincial Railway Construction Co., Ltd., Chengdu 610031, Sichuan, China;3.Chengdu Branch of Sichuan Chengmiancangba Expressway Construction & Development Group Co., Ltd., Chengdu 610213, Sichuan, China;4.Sichuan Provincial Transportation Key Project Work Center, Chengdu 610041, Sichuan，China） |
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Abstract To address the issues of image quality degradation and single feature expression in tunnel face images, an intelligent rock mass evaluation method integrating multi-scale progressive enhancement and deep semantic modeling was proposed. A multi-scale image enhancement framework combining spatial domain filtering and transform domain denoising was employed, by which the signal-to-noise ratio was increased by 12.7 dB with significantly enhanced visibility of key geological structures including fractures and joints. A four-dimensional comprehensive evaluation index system was constructed based on the gray-level co-occurrence matrix, improved local binary pattern, and RGB color moments for quantitative rock mass characterization.The ResNet architecture was improved through integration of multi-scale feature extraction and dual attention mechanisms (channel attention + spatial attention), while a weighted cross-entropy-label smoothing composite loss function was adopted to address class imbalance. Based on a constructed database of 5,000 tunnel face images, the model accuracy of 94.27% was achieved on the test set (4.93% higher than the baseline model) with a computational complexity of 4.8 G FLOPs. Practical case verification indicated that the proposed method could provide real-time，objective geological decision support for tunnel construction，significantly improving the intelligence level of rock mass classification
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Received: 30 June 2025
Published: 18 August 2025
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