Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques.

Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts. Highlights: An efficient hybrid ensemble model based on HBA is proposed for predicting tunnel overbreak caused by blasting. An explicit high-accuracy prediction equation for tunnel overbreak is constructed using GEP technology. The model performance is comprehensively evaluated through various metrics and non-parametric statistical tests. The constructed model demonstrates excellent generalization capabilities and interpretability. [ABSTRACT FROM AUTHOR]