Prediction of TBM Performance Under Imbalanced Geological Conditions Using Resampling and Data Synthesis Techniques

Predicting Tunnel Boring Machine (TBM) working parameters, such as cutterhead torque, is crucial for ensuring safe and efficient tunneling. However, complex and changing geological conditions pose a significant challenge to this study, leading to degradation in the model’s performance and poor generalization ability. To overcome these limitations, this study proposes a novel method to predict the TBM working parameters under changing geological conditions. The proposed method first divides the TBM working parameters into short subseries and assigns pseudo labels to each subseries according to their statistical values. The proposed method then creates a balanced dataset for the TBM performance prediction task by generating new samples for minority classes and undersampling majority classes. The mean absolute percentage error (MAPE) of the test dataset decreased from 1.893% to 1.338%, resulting in a 29.3% increase in the model’s performance. Using pseudo labels of the subseries, a more comprehensive assessment of the proposed method was performed. A comparison with several traditional methods also proves the effectiveness of the method. This research is not only significant for studying TBM performance prediction in changing geological conditions but also provides insights for other complex equipment operating under changing working conditions.