What question did this study set out to answer?

The aim is to accurately predict peak particle velocity during blasting in open-pit mines to enhance operational safety.

February 11, 2026Open Access

CCO–XGBoost Hybrid Model for Prediction of Blasting-Induced Peak Particle Velocity in Open-Pit Mines: A SHAP-Driven Sensitivity Analysis

Puntos clave

The aim is to accurately predict peak particle velocity during blasting in open-pit mines to enhance operational safety.
Hybrid modeling using CCO and XGBoost algorithms.
Data collection of 193 blasting records from Panzhihua Iron Mine.
Evaluation with metrics like R2, RMSE, MAE, and VAF.
SHAP-based sensitivity analysis for model transparency.
CCO–XGBoost model achieved R2 of 0.967, RMSE of 0.110, MAE of 0.067, and VAF of 96.35%.
Blast-to-monitor distance was found to negatively impact PPV, especially when exceeding 54m.
Charge per hole and total charge per delay are significant positive predictors of PPV.

Resumen

Accurate prediction of peak particle velocity (PPV) in open-pit mine blasting is critical for ensuring operational safety and effective vibration control. This study proposes a hybrid modeling approach that integrates the Centered Collision Optimization (CCO) algorithm with Extreme Gradient Boosting (XGBoost), enhanced by SHAP-based sensitivity analysis to improve model transparency and mechanistic interpretability. A comprehensive dataset was constructed based on 193 field-measured blasting records collected from the Panzhihua Iron Mine in China, incorporating nine key input parameters. Model performance was rigorously evaluated using four widely recognized metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). The results demonstrate that the CCO–XGBoost model achieves superior predictive performance, with R2 = 0.967, RMSE = 0.110, MAE = 0.067, and VAF = 96.35%, outperforming conventional approaches. SHAP-based sensitivity analysis reveals that blast-to-monitor distance (R) is the dominant negative predictor of PPV, contributing 43% to the total influence, with its vibration attenuation effect intensifying significantly when R exceeds 54 m. Charge per hole (q) and total charge per delay (Q) are identified as the primary positive influencing factors, accounting for 24% and 20% of the total contribution, respectively: the positive promoting effect of q on PPV strengthens markedly when q exceeds 17 kg, while Q exerts a continuous positive increasing influence on PPV when it exceeds 253 kg. Compared to existing hybrid models, the CCO–XGBoost uniquely avoids local optima and ensures higher global stability. This study fills the gap by providing quantifiable engineering thresholds for practical vibration control, making the model directly applicable to on-site blasting optimization.

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