Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance

Abstract

Recently, AI-based models have been applied to accurately estimate tunnel boring machine (TBM) energy consumption. Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. Consequently, this study develops an XGB_MOFS model that cooperates extreme gradient boosting (XGBoost) and multi-objective feature selection (MOFS) to improve the accuracy and explainability of energy consumption prediction. The XGB_MOFS model includes: (1) a causal inference framework to identify the causal relationships among influential factors, and (2) a MOFS approach to balance predictive performance and explainability. Two case studies are carried out to verify the proposed method. Results show that XGB_MOFS achieves a high degree of accuracy and robustness in energy consumption prediction. The XGB_MOFS model, balancing accuracy with explainability, serves as an effective and feasible tool for regulating TBM energy consumption.

Keywords

Machine learning / Multi-objective feature selection / Explainability / Energy consumption / Shield tunneling

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Wenli Liu: Writing - review & editing, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis. Yafei Qi: Writing - review & editing, Writing - original draft, Visualization, Validation, Methodology, Investigation. Fenghua Liu: Writing - review & editing, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors gratefully acknowledge the support provided by the National Key Research and Development Program (Grant No. 2023YFC3805800) and the National Natural Science Foundation of China (Grant Nos. U21A20151, 72171094 and 52192664).

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