Document Type : Original Research Paper
Authors
1 Department of Mining Engineering, Facullty of Engineering, Tarbiat Modares University, Tehran, Iran
2 Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
Abstract
Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such as rock mass properties, blast geometry, and explosive properties. Therefore, during the blasting process, back-break must be accurately predicted, and other production activities must be done to prevent and reduce its adverse effects. In this regard, a hybrid model of extreme gradient boosting (XGB) is proposed for predicting back-break using gray wolf optimization (GWO) and particle swarm optimization (PSO). Additionally, validation of the hybrid model is conducted using XGBoost, gene expression programming (GEP), random forest (RF), linear multiple regression (LMR), and non-linear multiple regression (NLMR) methods. For this purpose, the data obtained from 90 blasting operations in the Chadormalu iron ore mine are collected by considering the parameters of the blast pattern design. According to the results obtained, the performance and accuracy level of hybrid models including GWO-XGB (R2 = 99, RMSE = 0.01, MAE = 0.001, VAF = 0.99, a-20 = 0.98), and PSO-XGB (99, 0.01, 0.001, 0.99, 0.98) are better than the XGBoost (97, 0.185, 0.132, 0.98, 95), GEP (96, 0.233, 0.186, 0.967, 0.935), RF (97, 0.210, 0.156, 0.97, 0.94), LMR (96, 0.235, 0.181, 0.964, 0.92), and NLMR (96, 0.229, 0.177, 0.968, 0.93) models. Notably, the GWO-XGB hybrid model has superior overall performance as compared to the PSO-XGB model. Based on the sensitivity analysis results, hole depth and stemming are the essential effective parameters for back-break.
Keywords
[1]. Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., Alavi Nezhad Khalil Abad, S.V., Marto, A., and Moghaddam, M.R. (2016). Evaluation and prediction of fly-rock resulting from blasting operations using empirical and computational methods. Engineering with Computers, 32 (1): 109-121.
[2]. Agrawal, H. and Mishra, A.K. (2018). Evaluation of initiating system by measurement of seismic energy dissipation in surface blasting. Arabian Journal of Geosciences, 11 (13): 1-12.
[3]. Dai, Y., Khandelwal, M., Qiu, Y., Zhou, J., Monjezi, M., and Yang, P. (2022). A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting. Neural Computing and Applications, 34 (8): 6273-6288.
[4]. Ramesh Murlidhar, B., Yazdani Bejarbaneh, B., Jahed Armaghani, D., Mohammed, A.S., and Tonnizam Mohamad, E. (2021). Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Natural Resources Research, 30 (2): 1865-1887.
[5]. Sirjani, A.K., Sereshki, F., Ataei, M., and Hosseini, M.A. (2022). Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1). Archives of Mining Sciences, 107-121.
[6]. Sharma, M., Choudhary, B.S., and Agrawal, H. (2021). Prediction and assessment of back break by multivariate regression analysis, and random forest algorithm in hot strata/fiery seam of open-pit coal mine.
[7]. Gates, W.C., Ortiz, L.T., and Florez, R. M. (2005, June). Analysis of rockfall and blasting backbreak problems, US 550, Molas Pass, CO. In Alaska Rocks 2005, The 40th US Symposium on Rock Mechanics (USRMS). OnePetro.
[8]. Mohammadnejad, M., Gholami, R., Sereshki, F., and Jamshidi, A. (2013). A new methodology to predict backbreak in blasting operation. International journal of rock mechanics and mining sciences, 60, 75-81.
[9]. Sari, M., Ghasemi, E., and Ataei, M. (2014). Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock mechanics and rock engineering, 47 (2): 771-783.
[10]. Esmaeili, M., Osanloo, M., Rashidinejad, F., Aghajani Bazzazi, A., and Taji, M. (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with computers, 30 (4): 549-558.
[11]. Konya, C.J. and Walter, E. J. (1991). Rock blasting and overbreak control (No. FHWA-HI-92-001; NHI-13211). United States. Federal Highway Administration.
[12]. Monjezi, M., Amini Khoshalan, H., and Yazdian Varjani, A. (2012). Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arabian Journal of Geosciences, 5 (3): 441-448.
[13]. Roy, M.P., Mishra, A.K., Agrawal, H., and Singh, P.K. (2020). Blast vibration dependence on total explosives weight in open-pit blasting. Arabian Journal of Geosciences, 13 (13): 1-8.
[14]. Singh, C.P., Agrawal, H.E.M.A.N.T., Mishra, A.K., and Singh, P.K. (2019). Reducing environmental hazards of blasting using electronic detonators in a large opencast coal project-a case study. J Mines Met Fuels, 67(7): 345-350.
[15]. Enayatollahi, I. and Aghajani-Bazzazi, A. (2009, September). Evaluation of salt-ANFO mixture in back break reduction by data envelopment analysis. In Proceedings of the 9th international symposium on rock fragmentation by blasting, Granada, Spain (pp. 127-133).
[16]. Enayatollahi, I. and Aghajani-Bazzazi, A. (2009). Evaluation of salt-ANFO mixture in back break reduction by data envelopment analysis. In Proceedings of the 9th international symposium on rock fragmentation by blasting, Granada, Spain (pp. 127-133).
[17]. Faradonbeh, R.S., Armaghani, D.J., Monjezi, M., and Mohamad, E.T. (2016). Genetic programming and gene expression programming for fly-rock assessment due to mine blasting. International Journal of Rock Mechanics and Mining Sciences, 88, 254-264.
[18]. Lawal, A.I., Kwon, S., Hammed, O.S., and Idris, M.A. (2021). Blast-induced ground vibration prediction in granite quarries: An application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN. International Journal of Mining Science and Technology, 31 (2): 265-277.
[19]. Mahdiyar, A., Jahed Armaghani, D., Koopialipoor, M., Hedayat, A., Abdullah, A., and Yahya, K. (2020). Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and Monte Carlo simulation techniques. Applied Sciences, 10 (2): 472.
[20]. Nguyen, H., Bui, X.N., Bui, H.B., and 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.
[21]. Zhang, X., Nguyen, H., Bui, X.N., Tran, Q.H., Nguyen, D. A., Bui, D.T., and 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.
[22]. Chandrahas, N., Choudhary, B.S., Teja, M.V., Venkataramayya, M.S., and Prasad, N.S.R. (2022). XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration using Unique Blast Data. Applied Sciences, 12 (10): 5269.
[23]. Monjezi, M., Rezaei, M., and Yazdian Varjani, A. (2010). Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Systems with Applications, 37 (3): 2637-2643.
[24]. Monjezi, M., Ahmadi, Z., Varjani, A. Y., and Khandelwal, M. (2013). Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Computing and Applications, 23 (3): 1101-1107.
[25]. Ghasemi, E. (2017). Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Computing and Applications, 28 (7): 1855-1862.
[26]. Saghatforoush, A., Monjezi, M., Shirani Faradonbeh, R., and Jahed Armaghani, D. (2016). Combination of neural network and ant colony optimization algorithms for prediction and optimization of fly-rock and back-break induced by blasting. Engineering with Computers, 32 (2): 255-266.
[27]. Ghasemi, E., Amnieh, H. B., and Bagherpour, R. (2016). Assessment of backbreak due to blasting operation in open pit mines: a case study. Environmental Earth Sciences, 75 (7): 1-11.
[28]. Shirani Faradonbeh, R., Monjezi, M., and Jahed Armaghani, D. (2016). Genetic programing and nonlinear multiple regression techniques to predict backbreak in blasting operation. Engineering with computers, 32(1): 123-133.
[29]. Hasanipanah, M., Shahnazar, A., Arab, H., Golzar, S.B., and Amiri, M. (2017). Developing a new hybrid-AI model to predict blast-induced backbreak. Engineering with Computers, 33 (3): 349-359.
[30]. Yu, Q., Monjezi, M., Mohammed, A.S., Dehghani, H., Armaghani, D.J., and Ulrikh, D.V. (2021). Optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation. Sustainability, 13 (22): 12797.
[31]. Kumar, S., Mishra, A.K., and Choudhary, B.S. (2022). Prediction of back break in blasting using random decision trees. Engineering with Computers, 38 (2): 1185-1191.
[32]. Li, C., Zhou, J., Khandelwal, M., Zhang, X., Monjezi, M., and Qiu, Y. (2022). Six Novel Hybrid Extreme Learning Machine–Swarm Intelligence Optimization (ELM–SIO) Models for Predicting Backbreak in Open-Pit Blasting. Natural Resources Research, 1-23.
[33]. Fan, J., Wu, L., Ma, X., Zhou, H., and Zhang, F. (2020). Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions. Renewable Energy, 145, 2034-2045.
[34]. Ghorbani, M.A., Deo, R.C., Yaseen, Z.M., H Kashani, M., and Mohammadi, B. (2018). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoretical and applied climatology, 133 (3): 1119-1131.
[35]. Friedman, J.H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
[36]. Friedman, J.H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4): 367-378.
[37]. Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28 (2): 337-407.
[38]. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of statistical learning. Springer series in statistics. New York, NY, USA.
[39]. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., and Chen, K. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4): 1-4.
[40]. Chen, T. and Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
[41]. Zhou, J., Li, E., Wang, M., Chen, X., Shi, X., and Jiang, L. (2019). Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. Journal of Performance of Constructed Facilities, 33 (3): 04019024.
[42]. Gao, W., Wang, W., Dimitrov, D., and Wang, Y. (2018a). Nano-properties analysis via fourth multiplicative ABC indicator calculating. Arabian journal of chemistry, 11 (6): 793-801.
[43]. Kennedy, J. and Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
[44]. Cao, Y., Zhang, H., Li, W., Zhou, M., Zhang, Y., and Chaovalitwongse, W.A. (2018). Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Transactions on Evolutionary Computation, 23(4): 718-731.
[45]. Mirjalili, S., Mirjalili, S.M., and Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
[46]. Emary, E., Yamany, W., Hassanien, A. E., and Snasel, V. (2015). Multi-objective gray-wolf optimization for attribute reduction. Procedia Computer Science, 65, 623-632.
[47]. Song, X., Tang, L., Zhao, S., Zhang, X., Li, L., Huang, J., and Cai, W. (2015). Grey wolf optimizer for parameter estimation in surface waves. Soil Dynamics and Earthquake Engineering, 75, 147-157.
[48]. El-Kenawy, E.S.M., Eid, M.M., Saber, M., and Ibrahim, A. (2020). MbGWO-SFS: Modified binary grey wolf optimizer based on stochastic fractal search for feature selection. IEEE Access, 8, 107635-107649.
[49]. Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
[50]. Faradonbeh, R.S., Armaghani, D.J., Amnieh, H.B., and Mohamad, E.T. (2018). Prediction and minimization of blast-induced fly-rock using gene expression programming and firefly algorithm. Neural Computing and Applications, 29 (6): 269-281.
[51]. Zhou, J., Li, C., Koopialipoor, M., Jahed Armaghani, D., and Thai Pham, B. (2021). Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC). International Journal of Mining, Reclamation and Environment, 35 (1): 48-68.
[52]. Faradonbeh, R. S., Hasanipanah, M., Amnieh, H. B., Armaghani, D. J., and Monjezi, M. (2018b). Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environmental monitoring and assessment, 190 (6): 1-15.
[53]. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
[54]. Vigneau, E., Courcoux, P., Symoneaux, R., Guérin, L., and Villière, A. (2018). Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Quality and Preference, 68, 135-145.
[55]. Gao, W., Guirao, J. L., Basavanagoud, B., and Wu, J. (2018). Partial multi-dividing ontology learning algorithm. Information Sciences, 467, 35-58.
[56]. Dou, J., Yunus, A.P., Bui, D.T., Merghadi, A., Sahana, M., Zhu, Z., and Pham, B.T. (2019). Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the total environment, 662, 332-346.
[57]. Himanshu, V.K., Roy, M.P., Mishra, A.K., Paswan, R.K., Panda, D., and Singh, P.K. (2018). Multivariate statistical analysis approach for prediction of blast-induced ground vibration. Arabian Journal of Geosciences, 11 (16): 1-11.
[58]. Shakeri, J., Shokri, B.J., and 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).
[59]. Shokri, B.J., Dehghani, H., and Shamsi, R. (2020). Predicting silver price by applying a coupled multiple linear regression (MLR) and imperialist competitive algorithm (ICA). Metaheuristic Computing and Applications, 1 (1): 1.
[60]. Bhatawdekar, R.M., Kumar, R., Sabri Sabri, M.M., Roy, B., Mohamad, E.T., Kumar, D., and Kwon, S. (2023). Estimating Fly-rock Distance Induced due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer. Sustainability, 15 (4): 3265.
[61]. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.
[62]. Chakraborty, S. and Bhattacharya, S. (2021). Application of XGBoost algorithm as a predictive tool in a CNC turning process. Reports in Mechanical Engineering, 2 (1): 190-201.
[63]. Sharma, M., Agrawal, H., and Choudhary, B. S. (2022). Multivariate regression and genetic programming for prediction of backbreak in open-pit blasting. Neural Computing and Applications, 34 (3): 2103-2114.
[64]. Taylor, K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7): 7183-7192.
[65]. Nikafshan Rad, H., Bakhshayeshi, I., Wan Jusoh, W.A., Tahir, M.M., and Foong, L.K. (2020). Prediction of flyrock in mine blasting: a new computational intelligence approach. Natural Resources Research, 29 (2): 609-623
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