机器学习方法在盾构隧道工程中的应用研究现状与展望

Review and prospect of machine learning method in shield tunnel construction

随着盾构隧道工程信息化水平的提升,隧道掘进设备作业过程监测技术日益完善,记录的工程数据蕴含了掘进设备内部信息及其与外部地层的相互作用关系。机器学习因其数据分析能力强,无需先验的理论公式和专家知识,相较于传统的建模统计分析方法具有更大的应用空间。通过机器学习方法对收集的信息与数据进行深度挖掘并分析其内在联系,有助于提升盾构隧道工程建设的效率和安全保障水平。简述机器学习方法的基本原理,总结和分析机器学习方法在盾构工程中的应用研究状况,综述基于机器学习的盾构设备状态分析、盾构设备性能预测、围岩参数反演、地表变形预测和隧道病害诊断等5个方面的进展,并分析当前研究的不足。最后,分析盾构隧道工程向智能化方向发展需重点攻克的难题。

With the development of engineering information level and the monitoring technology in the field of shield tunnel,the recorded engineering data contains the internal information of tunneling equipment and its interaction with the external stratum.Machine learning has more application space than traditional modeling statistical analysis methods because of its strong data analysis ability and no requirement on prior theoretical formula and expert knowledge.Improving the efficiency and safety level of shield tunnel construction is helpful to deeply mine the collected information and data and analyze their internal relationship through machine learning method.This paper briefly describes the basic principle of machine learning methods,summarizes and analyzes its application in shield tunnel engineering.In particular,the progress on the equipment status analysis,shield performance prediction,geological parameters analysis,prediction of ground surface deformation and examination of tunnel hazard based on the machine learning method are summarized.Finally,the key problems to be solved so as to realize the intelligent shield tunnel engineering are analyzed and forecasted.