Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction

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 devel-ops 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(a).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 param-eters.This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.