An intelligent approach for predicting overbreak in underground blasting operation based on an optimized XGBoost model

The occurrence of overbreak in tunnels excavated with the drill-and-blast technique is a common phenomenon that has significant impacts on structure safety and construction costs. Accurate prediction of overbreak is crucial for optimizing the construction schedule and diminishing damages. This study proposed a data-driven method that integrated extreme gradient boosting (XGBoost) and Bayesian optimization (BO) algorithms to predict overbreak extent. Firstly, 250 overbreak samples were collected from three underground mines, and eight independent factors that may affect overbreak were identified. Subsequently, the BO–XGBoost prediction model was established, and Spearman correlation analysis and sensitivity analysis were conducted to analyze the relation between overbreak and influencing factors. Finally, the proposed BO–XGBoost model was employed to forecast the overbreak in another two underground mines. The experimental results indicated that the proposed BO–XGBoost model outperformed other models, including Random Forests (RF), Support Vector Machine (SVM), BO–RF, BO–SVM, and XGBoost models, with root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of 0.888, 0.619 and 0.935, respectively. Additionally, statistical analysis using Friedman Test (FT) and Wilcoxon Signed-Rank Test (WSRT) demonstrated the efficacy of the proposed model. The results suggested that tunnel diameter (D) was the most significant factor affecting overbreak, followed by RMR, periphery hole burden (SP) and uniaxial compressive strength (UCS). The proposed method accurately forecasted overbreak extents at different mines, with errors between the predicted and observed overbreaks of less than 6%. In summary, the proposed BO–XGBoost model can provide valuable guidance for predicting blast-induced overbreak in mining and tunneling operations.