An interpretable probabilistic prediction algorithm for shield movement performance

Abstract

Total thrust and torque are two key indicators of shield movement performance. Most existing data-driven machine learning studies focus on developing more accurate models for predicting total thrust and torque but overlook the interpretability of the models. To address this black-box issue, this study proposes an interpretable probabilistic prediction algorithm for the shield movement performance. The algorithm uses the natural gradient boosting (NGBoost) model to iteratively update the parametric probability distributions (e.g., mean and variance) and achieve probabilistic predictions of the total thrust and torque. The impact of each feature on the prediction values and uncertainty is quantified by extending the importance analysis of a single deterministic predictive value to both the mean and variance. The feature interactions are analyzed and their predictive contributions are quantified by the shapley additive explanations (SHAP) method. The transparency of the NGBoost model is improved through the visualization of the decision-making process. A shield tunneling project in Hangzhou is used to validate the effectiveness of the proposed algorithm. The results indicate that the NGboost model outperforms other five models in terms of accuracy. The prediction results are interpretable, and the interpretable probabilistic model provides decision-makers with a more intuitive and reliable reference.