Machine learning-based prediction model for disc cutter life in TBM excavation through hard rock formations

Replacing damaged or worn disc cutters is costly and time-consuming, which can significantly reduce the utilization and advance rate of tunnel boring machines (TBMs). Accurate prediction of disc cutter life is crucial for optimizing TBM operation in tunneling projects. This study introduces a machine-learning-based prediction model designed to forecast disc cutter wear, incorporating the analysis of each cutter's travel length and the intervals between cutterhead interventions (CHI). The principle underlying the developed machine learning approach involves usage of multiple learning algorithms, ensemble learning methods, and a sophisticated analysis of these factors to identify patterns and relationships essential for effectively predicting wear rates. Employing CHI report data from the Daegok–Sosa tunneling project's hard rock excavation, the model evaluates 15 wear-influencing factors, providing precise wear predictions. This study proposes the ensemble machine learning (ML) methods, namely Random Forest (RF) and Extreme Gradient Boosting (XGB), for accurate wear rate predictions and discern influential factors. The proposed model demonstrated exceptional prediction accuracy, as evidenced by a root mean square error of 0.049 mm for a single-ring excavation length. Notably, this model innovatively accounts for variable wear rates of cutters based on the individual cutter travel lengths as well as other geological and operational parameters. Furthermore, the predicted cutter consumption rate showed reasonable correspondence when compared to the actual CHI records. The proposed model is expected to improve existing disc cutter life prediction methods and reduce the cost and time for replacing damaged or worn disc cutters.