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 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.