Identification of failure behaviors of underground structures under dynamic loading using machine learning  

作　　者：Chun Zhu Yingze Xu Manchao He Yujing Jiang Murat Karakus Lihua Hu Yalong Jiang Fuqiang Ren 

机构地区：[1]School of Earth Sciences and Engineering,Hohai University,Nanjing,210098,China [2]State Key Laboratory of Coal Mine Disaster Dynamics and Control,Chongqing University,Chongqing,400044,China [3]State Key Laboratory for Geomechanics&Deep Underground Engineering,Xuzhou,221116,China [4]Graduate School of Engineering,Nagasaki University,Bunkyo,Nagasaki,852-8521,Japan [5]School of Civil,Environmental and Mining Engineering,The University of Adelaide,Adelaide,5005,SA,Australia [6]State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,East China Jiaotong University,Nanchang,330013,China [7]School of Civil Engineering,University of Science and Technology Liaoning,Anshan,114051,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2025年第1期414-431,共18页岩石力学与岩土工程学报(英文)

基　　金：funding support from the National Natural Science Foundation of China(Grant No.52374119);the opening fund of State Key Laboratory of Coal Mine Disaster Dynamics and Control(Grant No.2011DA105827-FW202209);the opening fund of State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,East China Jiaotong University(Grant No.HJGZ2023103).

摘　　要：Understanding the dynamic responses of hard rocks is crucial during deep mining and tunneling activities and when constructing nuclear waste repositories. However, the response of deep massive rocks with openings of different shapes and orientations to dynamic loading is not well understood. Therefore, this study investigates the dynamic responses of hard rocks of deep underground excavation activities. Split Hopkins Pressure Bar (SHPB) tests on granite with holes of different shapes (rectangle, circle, vertical ellipse (elliptical short (ES) axis parallel to the impact load direction), and horizontal ellipse (elliptical long (EL) axis parallel to the impact load direction)) were carried out. The influence of hole shape and location on the dynamic responses was analyzed to reveal the rocks' dynamic strengths and cracking characteristics. We used the ResNet18 (convolutional neural network-based) network to recognize crack types using high-speed photographs. Moreover, a prediction model for the stress-strain response of rocks with different openings was established using Deep Neural Network (DNN). The results show that the dynamic strengths of the granite with EL and ES holes are the highest and lowest, respectively. The strength-weakening coefficient decreases first and then increases with an increase of thickness-span ratio (h/L). The weakening of the granite with ES holes is the most obvious. The ResNet18 network can improve the analyzing efficiency of the cracking mechanism, and the trained model's recognition accuracy reaches 99%. Finally, the dynamic stress-strain prediction model can predict the complete stress-strain curve well, with an accuracy above 85%.

关 键 词：Dynamic mechanical response Cracking mode Hole shape/location effect Deep Neural Network(DNN) Stress-strain prediction 

分 类 号：TD324[矿业工程—矿井建设] TP181[自动化与计算机技术—控制理论与控制工程]