TBM penetration rate prediction based on the long short-term memory neural network

作     者：Boyang Gao RuiRui Wang Chunjin Lin Xu Guo Bin Liu Wengang Zhang 

作者机构：Geotechnical and Structural Engineering Research CenterShandong UniversityJinanChina School of Qilu TransportationShandong UniversityJinanChina School of Civil EngineeringShandong UniversityJinanChina Data Science InstituteShandong UniversityJinanChina Key Laboratory of New Technology for Construction of Cities in Mountain AreaChongqing UniversityChongqingChina National Joint Engineering Research Center of Geohazards Prevention in the Reservoir AreasChongqing UniversityChongqingChina School of Civil EngineeringChongqing UniversityChongqingChina 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2021年第6卷第6期

页      面：718-731页

核心收录：

学科分类：12[管理学] 081406[工学-桥梁与隧道工程] 08[工学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0813[工学-建筑学] 0835[工学-软件工程] 0814[工学-土木工程] 0811[工学-控制科学与工程] 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）] 

基　　金：supported by National Natural Science Foundation of China(No.51739007) the National Science Fund for Excellent Young Scholars(No.51922067) Joint Funds of the National Natural Science Foundation of China(No.U1806226) Taishan Scholars Program of Shandong Province(tsqn20190900,tsqn201909044) the Key Research and Development Program of Shandong Province(No.Z135050009107) the Interdisciplinary Development Program of Shandong University(No.2017JC002) 

主　　题：TBM performance prediction Penetration rate Long short-term memory Water conveyance tunnel 

摘      要：Tunnel boring machines(TBMs)are widely used in tunnel engineering because of their safety and *** TBM penetration rate(PR)is crucial,as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating *** this study,deep learning technology is applied to TBM performance prediction,and a PR prediction model based on a long short-term memory(LSTM)neuron network is *** verify the performance of the proposed model,the machine parameters,rock mass parameters,and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a ***,2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model,and 257 excavation cycles were used as a testing dataset to test the *** root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204,*** with Recurrent neuron network(RNN)based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables(ARIMAX),the overall performance on proposed model is ***,in the rapidly increasing period of the PR,the error of the LSTM-based model prediction curve is significantly smaller than those of the other two *** prediction results indicate that the LSTM-based model proposed herein is relatively accurate,thereby providing guidance for the excavation process of TBMs and offering practical application value.