Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

Abstract

During tunnel boring machine (TBM) excavation, lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation. However, site investigation generally lacks ground samples and the information is subjective, heterogeneous, and imbalanced due to mixed ground conditions. In this study, an unsupervised (K-means) and synthetic minority oversampling technique (SMOTE)-guided light-gradient boosting machine (LightGBM) classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data. During the tunnel excavation, an earth pressure balance (EPB) TBM recorded 18 different operational parameters along with the three main tunnel lithologies. The proposed model is applied using Python low-code PyCaret library. Next, four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application. In addition, the Shapley additive explanation (SHAP) was implemented to avoid the model black box problem. The proposed model was evaluated using different metrics such as accuracy, F1 score, precision, recall, and receiver operating characteristics (ROC) curve to obtain a reasonable outcome for the minority class. It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM. The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Keywords

• Earth pressure balance (EPB)
• Tunnel boring machine (TBM)
• Soft ground tunnelling
• Tunnel lithology
• Operational parameters
• Synthetic minority oversampling technique (SMOTE)