Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3

Abstract

Abstract Accurately predicting the performance of Earth Pressure Balance Tunnel Boring Machines (EPB-TBMs) in soft ground conditions is crucial yet challenging due to the complex interaction of geological and operational factors. This study investigates Mashhad Metro Line 3, where a TBM was employed to excavate a 1831-m section through variable soil compositions, including significant cobble and boulder content, presenting unique challenges to performance prediction. To address these complexities, several machine learning models—Multiple Linear Regression (MLR), Decision Trees (DT), and Multi-Layer Perceptron (MLP) neural networks—were applied to predict TBM penetration rates and assess model efficacy. Beginning with a dataset of 438,960 rows, rigorous feature selection and data processing yielded a final dataset of 1833 rows. Among the models, MLR achieved an R2 score of 0.991, closely matching the more complex MLP model, which reached an R2 score of 0.988. In contrast, the Decision Tree model demonstrated a lower R2 score of 0.923, suggesting a tendency to overfit. While MLR provided an effective, straightforward approach, MLP proved valuable for capturing non-linear patterns that could improve predictive accuracy in more variable tunneling conditions. These findings underscore the practical applications of both simple and complex machine learning models in enhancing TBM performance prediction.

Keywords

• Tunnel boring machine (TBM)
• Machine learning models
• Data preprocessing
• Penetration rate prediction
• Feature selection
• Decision trees