Prediction Model of Shield Frontal Hob Tunneling Efficiency Based on Machine Learning

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Prediction Model of Shield Frontal Hob Tunneling Efficiency Based on Machine Learning

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[1]DING Xiaobin,WU Zhiyuan,REN Xufeng,et al.Prediction Model of Shield Frontal Hob Tunneling Efficiency Based on Machine Learning[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):38-46.[doi:10.13705/j.issn.1671-6833.2026.03.012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 38-46 Column: Public date: 2026-05-27

Title:: Prediction Model of Shield Frontal Hob Tunneling Efficiency Based on Machine Learning

Author(s):: DING Xiaobin^1,2, WU Zhiyuan¹, REN Xufeng¹, YUAN Linxuan¹; 1.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China; 2.South China Institute of Geotechnical Engineering, South China University of Technology, Guangzhou 510640, China

Keywords:: shield construction; tunnelling efficiency prediction; machine learning; tool change of the shield machine

CLC:: P642

DOI:: 10.13705/j.issn.1671-6833.2026.03.012

Abstract:: In the past, the judgment of the timing of the opening and changing of the disc cutter mainly relied on sensor data and human experience, which led to the serious wear of the disc cutter, or affected the tunneling speed. In order to accurately judge the timing of disc cutter opening and tool replacement, in this study the excavation efficiency calculation method was summarized to characterize the use value of disc cutter, and machine learning method was used to predict it. Based on the Shenzhen CFL tunnel project, the influencing factors of the shield tunneling efficiency were analyzed in the previous literature, 15 characteristics were selected as the input parameters, and the hob tunneling efficiency was used as the output parameters, and a total of 37 849 data series were obtained as the total sample set after data processing. The machine learning method was used to train on datasets, and the algorithmic models used include Random Forest, Extra Tress, GBDT and XGBOOST. The results showed that the machine learning model could predict the tunneling efficiency of the hob cutter well, and the XGBOOST model had the best prediction effect, with a determination coefficient of 0.955, an average absolute error of 7.053, and a root mean square error of 13.249.

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Last Update: 2026-05-27