Prediction of shield tunneling attitudes: A muti-dimensional feature synthesizing and screening method

Abstract

Shield attitudes, essentially governed by intricate mechanisms, impact the segment assembly quality and tunnel axis deviation. In data-driven prediction, however, existing methods using the original driving parameters fail to present convincing performance due to insufficient consideration of complicated interactions among the parameters. Therefore, a multi-dimensional feature synthesizing and screening method is proposed to explore the optimal features that can better reflect the physical mechanism in predicting shield tunneling attitudes. Features embedded with physical knowledge were synthesized from seven dimensions, which were validated by the clustering quality of Shapley Additive Explanations (SHAP) values. Subsequently, a novel index, Expected Impact Index (EII), has been proposed for screening the optimal features reliably. Finally, a Bayesian-optimized deep learning model was established to validate the proposed method in a case study. Results show that the proposed method effectively identifies the optimal parameters for shield attitude prediction, with an average Mean Squared Error (MSE) deduction of 27.3%. The proposed method realized effective assimilation of shield driving data with physical mechanism, providing a valuable reference for shield deviation control.