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

Abstract

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%, an MSE of 2.40, and an MAE of 0.003, demonstrating its accuracy and reliability. Ten percent of the dataset was reserved as unseen data to assess the model’s generalization capabilities. Upon evaluation, the model achieved an R2 value of 89%, an MAE of 0.01, and a root mean squared error (RMSE) of 0.01. The xNN empowers operators to optimize operational parameters and promote more efficient and sustainable tunneling practices by identifying influential factors affecting energy consumption through its interpretable nature. This research has significant implications for the future of underground construction, paving the way for improved resource management.