Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet  

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作  者:Xiaoying ZHUANG Wenjie FAN Hongwei GUO Xuefeng CHEN Qimin WANG 

机构地区:[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

出  处:《Frontiers of Structural and Civil Engineering》2024年第9期1311-1320,共10页结构与土木工程前沿(英文版)

摘  要: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.

关 键 词:surrounding rock classification convolutional neural network EfficientNet Gradient-weight Class Activation Map 

分 类 号:U455[建筑科学—桥梁与隧道工程] TP391.04[交通运输工程—道路与铁道工程]

 

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