Home | About Journal  | Editorial Board  | Instruction | Subscription | Advertisement | Message Board  | Contact Us | 中文
MODERN TUNNELLING TECHNOLOGY 2022, Vol. 59 Issue (2) :38-44    DOI:
Current Issue | Next Issue | Archive | Adv Search << [an error occurred while processing this directive] | [an error occurred while processing this directive] >>
Study of Standardized Pre-processing Method of TBM Tunnelling Data
(1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048; 2. School of Civil Engineering and Transportation, Hohai University, Nanjing 210098; 3. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044)
Download: PDF (2609KB)   HTML (1KB)   Export: BibTeX or EndNote (RIS)      Supporting Info
Abstract During TBM construction process, massive data are collected through information technology, and the stan? dardized pre-processing of TBM data is a precondition for multiple studies. Thus, a standardized pre-processing method of TBM tunnelling data is put forward. Based on the big data generated in TBM tunnelling and the TBM rock-breaking characteristics, the basic tunnelling parameters (e.g. cutterhead rotation speed, advancing speed, cutterhead thrust, and cutterhead torque) are selected to analyze the data characteristics during TBM tunnelling. The judgement methods of starting points of idle stage, ascent stage, stable stage and descent stage during the process of cyclic tunnelling are proposed, and furthermore the standard deviation judgement method, mean value judgement method and histogram judgement method are put forward for the starting point of the stable stage, so as to meet the requirements for segmentation of real-time and non-real-time data. Finally, the standardized pre-processing of TBM data is conducted for two TBM tunnel projects to realize the standardization of big data during construction.The result shows that the proposed standardized pre-processing method can realize the effective segmentation of cyclic tunnelling data. The research results can be applied to the standardized data processing of many TBM tunnel projects to effectively create the database for machine learning.
Service
Email this article
Add to my bookshelf
Add to citation manager
Email Alert
RSS
Articles by authors
Wang Shuangjing1
2 Wang Yujie1 Li Xu3 Liu Lipeng1 Yin Tao1
2
KeywordsTBM   Standardized pre-processing   Division of cyclic tunnelling stages   TBM Data Segmentation (TDS)     
Abstract: During TBM construction process, massive data are collected through information technology, and the stan? dardized pre-processing of TBM data is a precondition for multiple studies. Thus, a standardized pre-processing method of TBM tunnelling data is put forward. Based on the big data generated in TBM tunnelling and the TBM rock-breaking characteristics, the basic tunnelling parameters (e.g. cutterhead rotation speed, advancing speed, cutterhead thrust, and cutterhead torque) are selected to analyze the data characteristics during TBM tunnelling. The judgement methods of starting points of idle stage, ascent stage, stable stage and descent stage during the process of cyclic tunnelling are proposed, and furthermore the standard deviation judgement method, mean value judgement method and histogram judgement method are put forward for the starting point of the stable stage, so as to meet the requirements for segmentation of real-time and non-real-time data. Finally, the standardized pre-processing of TBM data is conducted for two TBM tunnel projects to realize the standardization of big data during construction.The result shows that the proposed standardized pre-processing method can realize the effective segmentation of cyclic tunnelling data. The research results can be applied to the standardized data processing of many TBM tunnel projects to effectively create the database for machine learning.
KeywordsTBM,   Standardized pre-processing,   Division of cyclic tunnelling stages,   TBM Data Segmentation (TDS)     
Cite this article:   
Wang Shuangjing1, 2 Wang Yujie1 Li Xu3 Liu Lipeng1 Yin Tao1, 2 .Study of Standardized Pre-processing Method of TBM Tunnelling Data[J]  MODERN TUNNELLING TECHNOLOGY, 2022,V59(2): 38-44
URL:  
http://www.xdsdjs.com/EN/      或     http://www.xdsdjs.com/EN/Y2022/V59/I2/38
 
No references of article
[1] FENG Huanhuan1,2 HONG Kairong1,2 YANG Yandong2 YANG Luwei1 SI Jingzhao1 YOU Jinhu3.Research and Application of Key Construction Technologies for TBM-driven Tunnels under Extreme Complex Geological Conditions[J]. MODERN TUNNELLING TECHNOLOGY, 2022,59(1): 42-54
[2] WANG Shuaishuai1 MAO Jinbo2 ZHANG Binbin2 Li Yalong2 ZHAO Honggang2.General Construction Technology Scheme of Tianshan Shengli Tunnel on Urumqi-Yuli Expressway[J]. MODERN TUNNELLING TECHNOLOGY, 2022,59(1): 55-68
[3] ZHANG Jinliang1 HUANG Qiuxiang2 WANG Xueying1 HU Chao2 ZHANG Shaoxuan2.Study on Engineering Influence of Defects in Pea Gravel Backfilling and Grouting Layer[J]. MODERN TUNNELLING TECHNOLOGY, 2021,58(6): 163-172
[4] GAO Xin WANG Wenjuan LI Qingfei FENG Shijie WU Qi.Research on the Routing Scheme of a Double Shield TBM Passing through the Metro Station[J]. MODERN TUNNELLING TECHNOLOGY, 2021,58(5): 56-64
[5] YANG Jihua1 GUO Weixin1 YAN Changbin2 MIAO Dong1.Study on Optimization of TBM Driving Parameters Based on the Energy Consumption[J]. MODERN TUNNELLING TECHNOLOGY, 2021,58(1): 54-60
[6] XU Peng1 HUANG Jun1 ZHOU Jianbo1 TANG Jinzhou2.3D Numerical Simulation of the Interaction between Rock Mass and Shield TBM Passing through the Fault Fracture Zone[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(6): 63-69
[7] LI Gang1 LI Xiaojun1 YANG Wenxiang2 HAN Dong1.Research on Prediction of TBM Driving Parameters Based on Deep Learning[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(5): 154-159
[8] WANG Yanqing ZENG Nianchang LIU Bolong.Analysis on Infrared Radiation Temperature Characteristics of Jointed Rock Masses with Different Orientations under Action of Disc Cutters[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(5): 193-199
[9] YANG Tianren HE Fei NING Xiangke ZHANG Xiao TIAN Yanchao.Design and Application of Advanced Geological Prediction System of TBM for Gaoligongshan Tunnel[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(4): 37-42
[10] LU Song1,2 WANG Xu1,2 LI Cangsong1,2 MENG Lu1,2.Study on Geological Prediction Technology of HSP Method for TBM Tunnel[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(3): 30-35
[11] ZHANG Yan1, 2 WANG Wei2 DENG Xueqin2.Prediction Model of TBM Advance Rate Based on Relevance Vector Machine[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(3): 108-114
[12] YAN Changbin JIANG Xiaodi.Prediction Model of TBM Net Advance Rate Based on Parameters of Rock Mass and Tunnelling[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(2): 26-33
[13] ZHU Heqing.Discussion on the Construction Method for the Extra-long Shengli Road Tunnel in Tianshan[J]. MODERN TUNNELLING TECHNOLOGY, 2020,57(1): 175-179
[14] DENG Mingjiang1 TAN Zhongsheng2.Some Issues during TBM Trial Advance of Super-long Tunnel Group and Development Direction of Construction Technology[J]. MODERN TUNNELLING TECHNOLOGY, 2019,56(5): 1-12
[15] HUANG Yinding1,2.Study on the Planning Technology for Metro Built by Double Shield TBM in Old Urban Area[J]. MODERN TUNNELLING TECHNOLOGY, 2019,56(3): 38-44
Copyright 2010 by MODERN TUNNELLING TECHNOLOGY