Akademik Veri Yönetim Sistemi
Neural Computing and Applications, 2025 (SCI-Expanded)
The improvement in blast fragmentation material size aids downstream operation efficiency by supplying a high percentage of 80% passing size of rocks (D80) material. In this study, blast fragmentation percentage prediction models were developed using five soft computing approaches, i.e., multilayer perceptron (MLP), multivariate adaptive regression spline (MARS), support vector regression SVR), decision tree (DT), and random forest (RF), combined with reptile search algorithm. Blast characteristics were obtained from 407 production blasts at the Anguran lead and zinc mine in Mahenshan, Iran. The particle size distribution resulting from blasting was determined using a digital image processing approach in the current paper. The novelty of this research lies in the integration of the reptile search algorithm (RSA) with machine-learning models for enhanced rock fragmentation prediction. In this study, the RSA was used to optimize RF model and accurate prediction of rock fragmentation due to mine blasting. The proposed models’ prediction accuracy was compared using nine performance indices such as root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE), Variance Accounted For (VAF), Nash–Sutcliffe efficiency (NS), Willmott’s index of agreement (WI) and bias index (Bias). Based on the obtained results, the RF-RSA model outperformed other developed models with the lowest error and highest accuracy. Hence, the results indicated that the RF-RSA model outperforms other models in predicting blast fragmentation, achieving the lowest R2 of 0.9734 and RMSE of 0.8386 in the training phase, and 0.9715 and 0.7464 in the testing phase. Furthermore, the model attained the lowest MAPE values of 2.3067 (training) and 2.0959 (testing), demonstrating its robustness and predictive accuracy. These findings highlight the effectiveness of the proposed AI-based approach in optimizing blast fragmentation prediction, contributing to improved operational efficiency in mining.