Document Type : Original Research Paper

Authors

1 Department of Mining Engineering, Ah.C., Islamic Azad University, Ahar, Iran.

2 Urmia university of Technology

3 Department of Mining Engineering, Ah.C., Islamic Azad University, Ahar, Iran

4 Faculty of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran

10.22044/jme.2025.16642.3261

Abstract

Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research aims to predict flyrock distances (FR) at the Sungun Copper Mine through the application of artificial intelligence (AI) models in conjunction with statistical approaches. Initially, a linear multivariate regression (LMR) model was constructed to establish the correlation between blasting parameters and flyrock range. Subsequently, an artificial neural network based on a multilayer perceptron (ANN-MLP) was developed and further optimized using two advanced hybrid algorithms: the Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). These algorithms were employed to calibrate the neural network’s weights and biases using variables such as number of blast holes, hole spacing, burden, total charge, specific drilling, charge per hole, and specific charge. Results showed that the ANN‑MLP model outperformed the LMR model, with performance metrics of root mean square error (RMSE = 9.31 m), mean absolute error (MAE = 7.10 m), and coefficient of determination (R² = 0.81) during the test phase. However, optimization of the ANN model with ICA and ACO significantly improved prediction accuracy. Among the hybrid models, the ICA-ANN model performed best with RMSE = 5.66 m, MAE = 4.60 m, and R² = 0.89, showing a considerable improvement over the LMR and ANN-MLP models. Sensitivity analysis further highlighted total charge and number of holes as the most influential parameters affecting flyrock dispersion. Overall, the findings underscore the potential of hybrid AI frameworks in advancing predictive modeling for safer and more efficient blasting operations.

Keywords

Main Subjects

[1]. Singh, T. N., & Singh, V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical & Geological Engineering, 23, 249–262.
[2]. Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A., & Noorani, S. A. (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7, 5383–5396.
[3]. Faraji Asl, P., Monjezi, M., Hamidi, J. K., & Armaghani, D. J. (2018). Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Engineering with Computers, 34, 241–251.
[4]. Ladegaard-Pedersen, A., & Holmberg, R. (1973). The dependence of charge geometry on flyrock caused by crater effects in bench blasting. Swedish Detonic Research Foundation Report DS, 1–38.
[5]. Lundborg, N., Persson, A., Ladegaard-Pedersen, A., & Holmberg, R. (1975). Keeping the lid on flyrock in open-pit blasting. Engineering and Mining Journal, 176, 95–100.
[6]. Olofsson, S. O. (1990). Applied explosives technology for construction and mining. Arla, France: Applex Publisher.
[7]. Richards, A., & Moore, A. (2004). Flyrock control—by chance or design. In Proceedings of the Annual Conference on Explosives and Blasting Technique (Vol. 1, pp. 335–348). ISEE.
[8]. Monjezi, M., Rezaei, M., & Varjani, A. Y. (2009). Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. International Journal of Rock Mechanics and Mining Sciences, 46(8), 1273–1280.
[9]. Amini, H., Gholami, R., Monjezi, M., Torabi, S. R., & Zadhesh, J. (2012). Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Computing and Applications, 21, 2077–2085.
[10]. Marto, A., Hajihassani, M., Jahed Armaghani, D., Mohamad, E. T., & Makhtar, A. M. (2014). A novel approach for blast‐induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific World Journal, 2014, 643715.
[11]. Faradonbeh, R. S., Jahed Armaghani, D., & Monjezi, M. (2016). Development of a new model for predicting flyrock distance in quarry blasting: A genetic programming technique. Bulletin of Engineering Geology and the Environment, 75, 993–1006.
[12]. Jahed Armaghani, D., Mohamad, E. T., Hajihassani, M., Alavi Nezhad Khalil Abad, S. V., Marto, A., & Moghaddam, M. R. (2016). Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Engineering with Computers, 32, 109–121.
[13]. Nguyen, H., Bui, X. N., Bui, H. B., & Cuong, D. T. (2019). Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophysica, 67(2), 477–490.
[14]. Nguyen, H., Bui, X. N., Bui, H. B., & Mai, N. L. (2020). A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Computing and Applications, 32(8), 3939–3955.
[15]. Nguyen, H., Bui, X. N., & Moayedi, H. (2019). A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophysica, 67(4), 1025–1037.
[16]. Guo, H., Zhou, J., Koopialipoor, M., Jahed Armaghani, D., & Tahir, M. M. (2021). Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Engineering with Computers, 37, 173–186.
[17]. Yari, M., Armaghani, D. J., Maraveas, C., Ejlali, A. N., Mohamad, E. T., & Asteris, P. G. (2023). Several tree-based solutions for predicting flyrock distance due to mine blasting. Applied Sciences, 13(3), 1345.
[18]. Jiang, H., Zhang, Q., Li, T., Liu, X., & Zhang, Y. (2018). Prediction of flyrock distance using an artificial neural network (ANN) model. Journal of Mining and Environment, 9(10), 2197–2205.
[19]. Koopialipoor, M., Fallah, A., Armaghani, D. J., Azizi, A., & Mohamad, E. T. (2019). Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers, 35, 243–256.
[20]. Nguyen, H., Bui, X. N., Nguyen-Thoi, T., Ragam, P., & Moayedi, H. (2019). Toward a state-of-the-art of fly-rock prediction technology in open-pit mines using EANNs model. Applied Sciences, 9(21), 4554.
[21]. Rad, H. N., Hasanipanah, M., Rezaei, M., & Eghlim, A. L. (2018). Developing a least squares support vector machine for estimating the blast-induced flyrock. Engineering with Computers, 34(4), 709-717.
[22]. Rezaei, M., Monjezi, M., Matinpoor, F., Bolbanabad, S. M., & Habibi, H. (2023). Simulation of induced flyrock due to open-pit blasting using the PCA-CART hybrid modeling. Simulation Modelling Practice and Theory, 129, 102844.
[23]. Nikafshan Rad, H., Bakhshayeshi, I., Wan Jusoh, W. A., Tahir, M. M., & Foong, L. K. (2020). Prediction of flyrock in mine blasting: A new computational intelligence approach. Natural Resources Research, 29, 609–623.
[24]. Rezaei, M., Monjezi, M., & Varjani, A. Y. (2011). Development of a fuzzy model to predict flyrock in surface mining. Safety science, 49(2), 298-305.
[25]. Zangoei, A., Monjezi, M., Armaghani, D. J., Mehrdanesh, A., & Ahmadian, S. (2022). Prediction and optimization of flyrock and oversize boulder induced by mine blasting using artificial intelligence techniques. Environmental Earth Sciences, 81(13), 359.
[26]. Zhou, Y., Zhang, X., Sun, X., Wang, S., & Shi, C. (2024). Advanced machine learning approaches for predicting flyrock using hybrid SVR models. Computational Materials and Engineering, 140(2), 56549.
[27]. Zhang, R., Li, Y., Gui, Y., Armaghani, D. J., & Yari, M. (2024). A stacked multiple kernel support vector machine for blast induced flyrock prediction. Geohazard Mechanics, 2(1), 37–48.
[28]. Kalaivaani, P. T., Akila, T., Tahir, M. M., Ahmed, M., & Surendar, A. (2020). A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO. Engineering with Computers, 36, 435–442.
[29]. Murlidhar, B. R., Nguyen, H., Rostami, J., Bui, X., Armaghani, D. J., Ragam, P., & Mohamad, E. T. (2021). Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1413–1427.
[30]. Hasanipanah, M., Keshtegar, B., Thai, D. K., & Truong, N. T. (2022). An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting. Engineering with Computers. Advance online publication, 1–3.
[31]. Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486.
[32]. Aggarwal, V., Gupta, V., Singh, P., Sharma, K., & Sharma, N. (2019). Detection of spatial outlier by using improved Z-score test. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 788–790). IEEE.
[33]. Ghojoghi, E., Farsangi, M. A. E., Mansouri, H., & Rashedi, E. (2024). Prediction and minimization of blasting flyrock distance, using deep neural networks and gravitational search algorithm, JAYA, and multi-verse optimization algorithms. Heliyon, 10(19). 542.
[34]. Esmaeilzadeh, A., Shaffiee Haghshenas, S., Mikaeil, R., Guido, G., Shirani Faradonbeh, R., Abbasi Azghan, R., Jafarpour, A., & Taghizadeh, S. (2022). Risk assessment in quarries using failure modes and effects analysis method (case study: West-Azerbaijan mines). Journal of Mining and Environment, 13(3), 715–725.
[35]. Majedi-Asl, M., Ghaderi, A., Kouhdaragh, M., & Alavian, T. O. (2024). A performance comparison of the meta model methods for discharge coefficient prediction of labyrinth weirs. Flow Measurement and Instrumentation, 96, 102563.
[36]. Shakeri, J., Shokri, B. J., & Dehghani, H. (2020). Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNs), and linear multivariate regression (LMR). Archives of Mining Sciences, 65(2).
[37]. Monjezi, M., Dehghani, H., Shakeri, J., & Mehrdanesh, A. (2021). Optimization of prediction of flyrock using linear multivariate regression (LMR) and gene expression programming (GEP)—Topal Novin mine, Iran. Arabian Journal of Geosciences, 14, 1–2.
[38]. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115–133.
[39]. Zhang, X., Nguyen, H., Bui, X. N., Tran, Q. H., Nguyen, D. A., Bui, D. T., & Moayedi, H. (2020). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research, 29(2), 711–721.
[40]. Norouzi, R., Daneshfaraz, R., & Ghaderi, A. (2019). Investigation of discharge coefficient of trapezoidal labyrinth weirs using artificial neural networks and support vector machines. Applied Water Science, 9, 1–10.
[41]. Nguyen, H., Bui, X. N., Tran, Q. H., Van Hoa, P., Nguyen, D. A., Hoa, L. T. T., … & Moayedi, H. (2020). A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophysica, 68, 325-336.
[42]. Yilmaz, I., & Yuksek, A. G. (2008). An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mechanics and Rock Engineering, 41(5), 781.
[43]. Hasanipanah, M., Noorian-Bidgoli, M., Jahed Armaghani, D., & Khamesi, H. (2016). Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Engineering with Computers, 32, 705–715.
[45]. Dasineh, M., Ghaderi, A., Bagherzadeh, M., Ahmadi, M., & Kuriqi, A. (2021). Prediction of hydraulic jumps on a triangular bed roughness using numerical modeling and soft computing methods. Mathematics, 9(23), 3135.
[45]. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation (pp. 4661–4667).
[46]. Alzoubi, I., Delavar, M., Mirzaei, F., & Nadjar Arrabi, B. (2017). Integrating artificial neural network and imperialist competitive algorithm (ICA) to predict the energy consumption for land leveling. International Journal of Energy Sector Management, 11(4), 522–540.
[47]. Dorigo, M. (1992). Optimization, learning and natural algorithms (Ph.D. thesis). Politecnico di Milano.
[48]. Hajizadeh, Y., Christie, M., & Demyanov, V. (2011). Ant colony optimization for history matching and uncertainty quantification of reservoir models. Journal of Petroleum Science and Engineering, 77(1), 78–92.
[49]. Oakley, J. E., & O’Hagan, A. (2004). Probabilistic sensitivity analysis of complex models: A Bayesian approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3), 751–769.
[50]. Mohamad, E. T., Armaghani, D. J., Noorani, S. A., Saad, R., Alvi, S. V., & Abad, N. K. (2012). Prediction of flyrock in boulder blasting using artificial neural network. Electronic Journal of Geotechnical Engineering, 17, 2585–2595.
[51]. Yari, M., Bagherpour, R., Jamali, S., & Shamsi, R. (2016). Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety. Neural Computing and Applications, 27, 699-706.