准确的掌子面地层信息对于土压平衡与TBM双模式盾构掘进控制以及模式转换至关重要,刀盘振动是双模盾构在掘进中与地层相互作用的直接响应,在不同地层条件下呈现出显著特征差异。本研究依托深圳地铁12号线怀福区间双模盾构施工段,通过在盾构上搭载振动监测系统对该区间掘进过程中的刀盘振动数据进行采集,创建振动与地层类型数据库。采用信号处理技术,对振动信号的关键特征进行提取与分析,并作为输入建立双模盾构掘进掌子面地层类型识别模型。该模型使用了Stacking集成技术,取决策树、K近邻、支持向量分类和AdaBoost作为基学习器,逻辑回归为元学习器。通过对怀福区间数据集进行测试,模型取得95.7%的准确率,显著优于DT、KNN、SVC、AdaBoost等传统机器学习模型,并得到了对地层类型最敏感的刀盘振动关键特征,包括均方根值、方根幅值和平均频域等。研究可为盾构施工地层实时识别提供新途径。
Accurate ground information of tunnel face is essential for earth pressure balance and TBM dual-mode shield tunneling control and mode changeover, and the cutterhead vibration is a direct response to the interaction between the dual-mode shield and the ground during tunneling, which presents significant characteristic differences under different ground conditions. The study relies on the construction section of the double-mode shield of Shenzhen Metro Line 12, Huai Fu, and the vibration data of the cutterhead during the tunneling process is collected by carrying a vibration monitoring system on the shield to create a database of vibration and ground types. The key features of the vibration signals were extracted and analyzed using signal processing techniques, and used as input to establish a model for recognition the ground types at the tunnel surface of the double-mode shield tunneling. The model uses Stacking integration technique, taking decision tree, K-nearest neighbor, support vector classification and AdaBoost as base learners and logistic regression as meta-learners. By testing the Huai Fu section dataset, the model achieved an accuracy of 95.7%, which was significantly better than traditional machine learning models such as DT, KNN, SVC, and AdaBoost, and obtained the key cutterhead vibration features that were most sensitive to ground types, including root mean square value, root mean square amplitude, and mean frequency domain. This study provides a new approach for real-time identification of geological types in shield tunnel construction in the future.
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