Machine learning forecasting models of disc cutters life of tunnel boring machine

This study aims to propose four Machine Learning methods of Gaussian process regression (GPR), support vector regression (SVR), decision trees (DT), and K-nearest neighbors (KNN) to predict disc cutter's life of TBM. 200 datasets monitored during the Alborz service tunnel construction in Iran, including TBM operational parameters, geometry, and geological conditions, were applied in the models. The 5-fold cross-validation method was considered to investigate the prediction performance of the models. Finally, the GPR model with R-2 = 0.8866/RMSE = 107.3554, was the most accurate model to predict TBM disc cutter's life. KNN model with R-2 = 0.1753/RMSE = 288.9277, produced the minimum accuracy. To assess each parameter's contribution in the prediction problem, the backward selection method was used. The results showed that TF, RPM, PR, and Qc parameters significantly contribute to TBM disc cutter's life. However, RPM and PR parameters were more and less significant compared to the others.

Automated summary

This study proposed four Machine Learning methods to predict TBM disc cutter's life, with Gaussian process regression model being the most accurate and K-nearest neighbors model having the lowest accuracy. Backward selection method revealed the significant contributions of different parameters to TBM disc cutter's life.