Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm
作者机构：Key Laboratory of Urban Underground Engineering of Ministry of EducationBeijing Jiaotong UniversityBeijing 100044China School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China
出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)
年 卷 期：2023年第17卷第12期
页 面：1777-1795页
核心收录：
学科分类：081406[工学-桥梁与隧道工程] 08[工学] 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]
基 金：the National Program on Key Basic Research Project of China(No.2015CB058100) China Railway Engineering Equipment Group Corporation and the Survey and Design Institute of Water Conservancy of Jilin Province supported by the Natural Key R&D Program ofChina(No.2022YFE0200400)
主 题：Tunnel Boring Machine fractured and weak rock mass machine learning model real-time early warming tunnel facerockcondition
摘 要：Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine(TBM)construction presents a critical challenge that warrants increased *** achieve this goal,this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the *** models are optimized in terms of selecting metric,selecting input features,and processing imbalanced *** results demonstrate the following points.(1)The Youden s index and area under the ROC curve(AUC)are the most appropriate performance metrics,and the XGBoost Random Forest(XGBRF)algorithm exhibits superior prediction and generalization performance.(2)The duration of the TBM loading phase is short,usually within a few minutes after the disc cutter contacts the tunnel face.A model based on the features during the loading phase has a miss rate of 21.8%,indicating that it can meet the early warning needs of TBM construction *** the TBM continues to operate,the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model,ultimately reducing the miss rate to 16.1%.(3)Resampling the imbalanced data set can effectively improve the prediction by the model,while the XGBRF algorithm has certain advantages in dealing with the imbalanced data *** the model gives an alarm,the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel *** real-time predication model can be a useful tool to increase the safety of TBM excavation.
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