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Ahmadi, Amin, Etesami, Reza. (1405). A Nonlinear Hybrid PSO–Neural Network Framework for Predicting Blast-Induced Ground Vibrations in Open-Pit Mining. , 58(1), 91-100. doi: 10.22059/jac.2026.411970.1254
Amin Ahmadi; Reza Etesami. "A Nonlinear Hybrid PSO–Neural Network Framework for Predicting Blast-Induced Ground Vibrations in Open-Pit Mining". , 58, 1, 1405, 91-100. doi: 10.22059/jac.2026.411970.1254
Ahmadi, Amin, Etesami, Reza. (1405). 'A Nonlinear Hybrid PSO–Neural Network Framework for Predicting Blast-Induced Ground Vibrations in Open-Pit Mining', , 58(1), pp. 91-100. doi: 10.22059/jac.2026.411970.1254
Ahmadi, Amin, Etesami, Reza. A Nonlinear Hybrid PSO–Neural Network Framework for Predicting Blast-Induced Ground Vibrations in Open-Pit Mining. , 1405; 58(1): 91-100. doi: 10.22059/jac.2026.411970.1254

A Nonlinear Hybrid PSO–Neural Network Framework for Predicting Blast-Induced Ground Vibrations in Open-Pit Mining

Journal of Algorithms and Computation
دوره 58، شماره 1، مهر 2026، صفحه 91-100    اصل مقاله (446.86 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2026.411970.1254
نویسندگان
Amin Ahmadi1؛ Reza Etesami* 2
1Islamic Azad University Kerman Branch
2Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
Blast-induced ground vibrations threaten structures and operations in mining environments, making accurate prediction essential for safety. This study develops a hybrid Particle Swarm Optimization (PSO)-Artificial Neural Network (ANN) model to predict ground vibrations at the Sarcheshmeh Copper Mine. Using 29 field records, the model relates four inputs—charge weight per delay, distance, stemming height, and number of hole-rows—to the Peak Particle Velocity (PPV). A compact single-hidden-layer network (3 neurons, 19 parameters) was adopted to limit overfitting, with PSO optimizing the weights and biases. The model achieved a training R² = 0.983 and, on representative test samples, R² ≈ 0.984 with an average absolute relative error of about 4.5%. While limited by dataset size, the results indicate that the hybrid PSO-ANN approach is a promising tool for vibration control in open-pit mining.
کلیدواژه‌ها
Artificial Neural Networks؛ Particle Swarm Optimization؛ Blast-induced Vibrations؛ Prediction Modeling؛ Hybrid Optimization؛ Open-pit Mining
آمار
تعداد مشاهده مقاله: 57
تعداد دریافت فایل اصل مقاله: 33





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