Significance and methodology:Preprocessing the big data for machine learning on TBM performance  

作　　者：Hao-Han Xiao Wen-Kun Yang Jing Hu Yun-Pei Zhang Liu-Jie Jing Zu-Yu Chen 

机构地区：[1]Department of Geotechnical Engineering,China Institute of Water Resources and Hydropower Research,Beijing 100038,China [2]Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing 100038,China [3]Institute of Underground Space,School of Civil Engineering,Southeast University,Nanjing 211189,China [4]Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China [5]China Railway Engineering Equipment Group Co.,Ltd.,Zhengzhou,Henan 450016,China

出　　处：《Underground Space》2022年第4期680-701,共22页地下空间（英文）

基　　金：support from the National Program on Key Basic Research Project(973 Program,No.2015CB058100)of China and China Railway Engineering Equipment Group Corporation;supported by the Key Research Project of China Institute of Water Resources and Hydropower Research Limited(Grant Nos.HTGE0203A03201900000,HTGE0203A20202000000);Natural Science Foundation of Shaanxi Province(Grant Nos.2019JLZ-13,2019JLP-23).

摘　　要：This paper addresses the significance of preprocessing big data collected during a tunnel boring machine(TBM)excavation before it is used for machine learning on various TBM performance predictions.The research work is based on two water diversion tunneling projects that cover 29.52 km and 17051 boring cycles.It has been found that the penetration rate calculated from the raw measured penetration distances exhibits more random behavior owing to their percussive and vibratory behavior of the cutterhead.A moving average method to process the negative instantaneous velocities and a noise reduction filter to deal with signals with abnormal frequencies have been recommended.An index called the drilling efficiency index is introduced to assess the relationships between the mechanical parameters in a boring cycle,whose linear regression coefficient R^(2)is taken for a preliminary investigation of possible problems requiring preprocessing.The research work defines the irrelevant data whose errors are caused by human or mechanical mistakes,and therefore should be cleaned or amended.These irrelevant data can be divided into five categories:(1)premature cycles,(2)sensor defects,(3)mechanical defects,(4)human interruption,and(5)missing files.A program TBM-Processing has been coded for the recognition and classification of these categories.PDF books generated by the program have been uploaded at GitHub to encourage discussions,collaboration,and upgrading of the data processing work with our peers.

关 键 词：TBM Big data Data processing Anomaly classification Machine learning 

分 类 号：U45[建筑科学—桥梁与隧道工程] TP181[交通运输工程—道路与铁道工程]