Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction

Abstract

To date, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge owing to the complex interactions between the TBM and ground. Using evolutionary polynomial regression (EPR) and random forest (RF), this study develops two novel prediction models for TBM performance. Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters: the uniaxial compressive strength, intact rock brittleness index, distance between planes of weakness, and angle between the tunnel axis and planes of weakness (α). First, the performances of both EPR- and RF-based models are examined by comparison with the conventional numerical regression method (i.e., multivariate linear regression). Subsequently, the performances of the RF- and EPR-based models are further investigated and compared, including the model robustness for unknown datasets, interior relationships between input and output parameters, and variable importance. The results indicate that the RF-based model has greater prediction accuracy, particularly in identifying outliers, whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression. Both EPR- and RF-based models can accurately identify the relationships between the input and output parameters. This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.

Keywords

• Tunnel boring machine
• Evolutionary polynomial regression
• Random forest
• Optimization
• Regularization