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

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

作者机构：Department of Geotechnical EngineeringCollege of Civil EngineeringTongji UniversityShanghai 200092China Chair of Computational Science and Simulation TechnologyInstitute of PhotonicsLeibniz University HannoverHannover 30167Germany Key Laboratory of Geotechnical and Underground Engineering of Ministry of EducationTongji UniversityShanghai 200092China Guizhou Xingyi Huancheng Expressway Co.Ltd.Xingyi 562400China 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2024年第18卷第9期

页      面：1311-1320页

核心收录：

学科分类：081406[工学-桥梁与隧道工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：surrounding rock classification convolutional neural network EfficientNet Gradient-weight Class Activation Map 

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 *** filtering,data augmentation and other data pre-processing techniques are used to improve the data quality and *** with transfer learning,the generality,accuracy and efficiency of the deep learning(DL)model are further improved,and finally we achieve 89.96%*** 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 *** 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 *** 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.