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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(floating point operations). 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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Published: 29 December 2025
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