Improved Mechanistic Modeling of TBM Disc Cutter Wear and Comparison with Data-Driven Prediction Models

To improve the accuracy of cutter wear and service life prediction for disc cutters, an improved normal force model is established based on the traditional CSM model by considering the supporting force and friction acting on the disc cutter from the side crushing zones. By incorporating the micro-mechanism of abrasive wear, an analytical model for the radial wear of the disc cutter and a service life prediction model are derived. Meanwhile, a regression model for cutter wear is established based on field operational parameters and cutter wear data. The mechanistic model is validated using field data from a tunnel project in Guangdong, China, and the results show that the average prediction errors of wear and service life are 8.13% and 8.85%, respectively, which are significantly lower than those of the traditional CSM model. Further comparative analysis between the two types of models is conducted, and the results indicate that the regression model achieves average prediction errors of 7.57% and 7.86% for wear and service life, respectively, showing higher prediction accuracy than the mechanistic model. The results demonstrate that the mechanistic model is suitable for revealing the wear mechanism of the disc cutter, while the regression model is more applicable for engineering prediction, and the two approaches can be used in a complementary manner.