[1]丁小彬,吴志远,任续锋,等.基于机器学习的盾构正面滚刀掘进效率预测模型[J].郑州大学学报(工学版),2026,47(3):38-46.[doi:10.13705/j.issn.1671-6833.2026.03.012]
　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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基于机器学习的盾构正面滚刀掘进效率预测模型()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
38-46
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
Prediction Model of Shield Frontal Hob Tunneling Efficiency Based on Machine Learning
文章编号:
1671-6833(2026)03-0038-09
作者:
丁小彬1,2, 吴志远1, 任续锋1, 袁霖轩1
1.华南理工大学 土木与交通学院,广东 广州 510640;2.华南理工大学 华南岩土工程研究院,广东 广州 510640
Author(s):
DING Xiaobin1,2, 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
关键词:
盾构施工;  掘进效率预测;  机器学习;  开仓换刀
Keywords:
shield construction;  tunnelling efficiency prediction;  machine learning;  tool change of the shield machine
分类号:
P642
DOI:
10.13705/j.issn.1671-6833.2026.03.012
文献标志码:
A
摘要:
以往工程中对滚刀开仓换刀时机的判断主要依赖传感器数据和人为经验,导致开仓换刀时滚刀已经严重磨损从而影响掘进速度,或者滚刀磨损值未达到预期造成开仓成本损耗。为了能准确判断滚刀开仓换刀时机,总结出掘进效率计算方法对滚刀的使用价值进行表征,并用机器学习方法对其进行预测。研究依托深圳市CFL隧道工程,调研以往文献分析盾构机掘进效率的影响因素,优选15种特征作为输入参数,将滚刀掘进效率作为输出参数,经过数据处理一共得到37 849条数据序列作为总样本集。采用机器学习的方法利用数据集进行训练,选用的算法模型包括Random Forest、Extra Tress、GBDT和XGBOOST。结果表明:机器学习模型能够很好地预测滚刀的掘进效率,其中XGBOOST模型的预测效果最好,决定系数为0.955,平均绝对误差为7.053,均方根误差为13.249,最适作为盾构正面滚刀掘进效率的预测模型,研究结果可以为盾构机开仓换刀时机提供参考。
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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更新日期/Last Update: 2026-05-27