A Novel AI-Driven Framework for Predicting Fragmentation and Cost-Oriented Design Optimization Using a Hybrid XGBoost–NSGA-II Approach
Keywords:
XGBoost, Blast Fragmentation, Multi-objective Optimization, NSGA-II algorithm, Data-driven ModelingAbstract
Modeling and optimizing the blasting process in mining has always been one of the fundamental challenges in mining engineering due to its multidimensional and nonlinear nature. This study aimed to develop a hybrid framework based on artificial intelligence for predicting rock fragmentation and simultaneously optimizing blasting cost and quality. In the first stage, the XGBoost machine learning algorithm was employed to model and predict the mean fragmentation size (P50) based on blasting design parameters. The results indicated that the proposed model could accurately capture complex relationships among variables, achieving R² values of 0.97 for the training dataset and 0.92 for the testing dataset, thus demonstrating remarkable predictive performance. Variable importance analysis revealed that the specific charge (q) and the burden distance (B were the most influential factors controlling fragmentation, while the rock mass quality (T) also played a decisive role in altering the fracture mechanism. In the next step, the NSGA-II evolutionary algorithm was applied for multi-objective optimization between blasting costs and the P50 index. The optimization outcomes generated a set of Pareto solutions, allowing engineers to flexibly select blasting patterns in accordance with either economic or operational priorities. The findings demonstrate that the proposed hybrid framework not only enhances prediction accuracy compared to empirical models but also provides a powerful data-driven decision-making tool for blast design. By introducing an integrated approach based on XGBoost and NSGA-II, this study makes a significant contribution to the blasting engineering literature and paves the way for developing sustainable and efficient blast designs in various mining operations.
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Copyright (c) 2025 Hassan Hosseinzadeh; Gholamreza Nowrouzi, Elham Rafieenia (Author)
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