A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

In tunnel construction, efficiently predicting the energy usage of tunnel boring machines (TBMs) is critical for optimizing operations and reducing costs. This research proposes a novel method for predicting the specific energy of micro slurry tunnel boring machines (MSTBMs) using an explainable neural network (xNN) that leverages operator-monitored data. The xNN model provides transparency and interpretability by integrating the Shapley additive explanation (SHAP) technique, enabling tunneling engineers and operators to gain valuable insights into the prediction process. Extensive data from MSTBM umbrella pipe support excavation are the foundation for training, testing, and unseen data in the xNN model. The specific energy formula derived from the operational parameters of the MSTBM defines the dependent variable for the xNN model. The test dataset evaluates the model’s performance with an R² of 98.7