Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis

Abstract Overbreak prediction in drilling and blasting tunnel construction remains a significant challenge due to the complexity and variability of influencing factors. Existing models, including empirical, statistical, and machine learning approaches, often fall short in terms of generalizability and accuracy. Empirical methods lack universal applicability due to their reliance on specific project conditions, while statistical models struggle with inconsistent patterns across different datasets. Furthermore, traditional AI models, including single machine learning algorithms, often overlook the multifaceted nature of overbreak, and hybrid models lack comprehensive evaluation standards. To address these limitations, this research proposes three innovative hybrid models that integrate metaheuristic optimization algorithms with support vector machine (SVM): multi-verse optimizer-SVM (MVO-SVM), salp swarm algorithm-SVM (SSA-SVM), and Harris’s Hawk optimization-SVM (HHO-SVM). These models optimize SVM hyperparameters, enhancing its ability to handle high-dimensional, non-linear data with robustness to outliers and improving the prediction of overbreak. The study’s motivation stems from the need for more accurate and universally applicable overbreak prediction models that can also explain the relationship between input parameters and overbreak outcomes. By incorporating SHapley Additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), the research introduces interpretability to enhance model transparency. The results show that rock mass rating and hole depth are the most crucial factors influencing overbreak predictions. Compared to previous models, the proposed hybrid models demonstrate significant improvements, with the HHO-SVM model showing superior predictive performance across various metrics. This study lays the groundwork for more reliable overbreak predictions and offers a powerful tool for geotechnical engineers.