1. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China 2. Chair of Computational Science and Simulation Technology, Institute of Photonics, Leibniz University Hannover, Hannover 30167, Germany 3. Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China 4. Guizhou Xingyi Huancheng Expressway Co., Ltd., Xingyi 562400, China
This paper proposes an accurate, efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network (CNN). The state-of-the-art robust CNN model (EfficientNet) is applied to tunnel wall image recognition. Gaussian filtering, data augmentation and other data pre-processing techniques are used to improve the data quality and quantity. Combined with transfer learning, the generality, accuracy and efficiency of the deep learning (DL) model are further improved, and finally we achieve 89.96% accuracy. Compared with other state-of-the-art CNN architectures, such as ResNet and Inception-ResNet-V2 (IRV2), the presented deep transfer learning model is more stable, accurate and efficient. To reveal the rock classification mechanism of the proposed model, Gradient-weight Class Activation Map (Grad-CAM) visualizations are integrated into the model to enable its explainability and accountability. The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou, China, with great results.
. [J]. Frontiers of Structural and Civil Engineering, 2024, 18(9): 1311-1320.
Xiaoying ZHUANG, Wenjie FAN, Hongwei GUO, Xuefeng CHEN, Qimin WANG. Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet. Front. Struct. Civ. Eng., 2024, 18(9): 1311-1320.
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