A comparative study on the application of Regression-PSO and ANN methods to predict backbreak in open pit mines

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Mining Engineering, Sahand University of Technology, East Azarbaijan, Iran

چکیده

• One of the most challenging safety problems in open pit mines is backbreak during blasting operation, and its prediction is very important for a technically and economically successful mining operation. To avoid backbreak, different parameters such as physicomechanical properties of rock mass, explosives properties and geometrical features of the blasting pattern should be considered. This paper presents a new solution of multiple linear regression (MLR), particle swarm optimization algorithm (PSO) and artificial neural networks (ANNs) to estimate the backbreak induced by bench blasting, based on major controllable blasting parameters. To this aim, Angouran mine in Iran was considered and blasting pattern parameters for 73 operations were collected. In addition, back-break was measured in each operation. Considering the previous investigations and also collected data from the mine, burden, spacing, hole length, stemming, charge per delay, RQD, number of row and powder factor were selected as input parameters. In order to find the better solutions, the constructed models were implemented in PSO algorithms. Also, the prediction of backbreak was investigated using ANNs. According to the obtained results, the PSO algorithm is a suitable tool for optimizing models and obtaining more accurate prediction of backbreak. Among the presented empirical models, the optimized exponential model with PSO algorithm with a RMSE (0.31) and R2 (0.87) shows the better results in prediction of backbreak and it is suitable for practical use in Angouran mine.

Considering the sensitivity analysis, among the input parameters, length of stemming and charge per delay have shown the most and the least effect on the backbreak, respectively. The results of ANNs show that multilayer networks are more powerful and efficient than single-layer in prediction of backbreak.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A comparative study on the application of Regression-PSO and ANN methods to predict backbreak in open pit mines

نویسندگان [English]

  • Masoud Shamsoddin Saeed 1
  • Hossein Jalalifar 1
  • Hamed Shamsoddini 1
  • Mohammad Darbor 2
1 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Mining Engineering, Sahand University of Technology, East Azarbaijan, Iran
چکیده [English]

• One of the most challenging safety problems in open pit mines is backbreak during blasting operation, and its prediction is very important for a technically and economically successful mining operation. To avoid backbreak, different parameters such as physicomechanical properties of rock mass, explosives properties and geometrical features of the blasting pattern should be considered. This paper presents a new solution of multiple linear regression (MLR), particle swarm optimization algorithm (PSO) and artificial neural networks (ANNs) to estimate the backbreak induced by bench blasting, based on major controllable blasting parameters. To this aim, Angouran mine in Iran was considered and blasting pattern parameters for 73 operations were collected. In addition, back-break was measured in each operation. Considering the previous investigations and also collected data from the mine, burden, spacing, hole length, stemming, charge per delay, RQD, number of row and powder factor were selected as input parameters. In order to find the better solutions, the constructed models were implemented in PSO algorithms. Also, the prediction of backbreak was investigated using ANNs. According to the obtained results, the PSO algorithm is a suitable tool for optimizing models and obtaining more accurate prediction of backbreak. Among the presented empirical models, the optimized exponential model with PSO algorithm with a RMSE (0.31) and R2 (0.87) shows the better results in prediction of backbreak and it is suitable for practical use in Angouran mine.

Considering the sensitivity analysis, among the input parameters, length of stemming and charge per delay have shown the most and the least effect on the backbreak, respectively. The results of ANNs show that multilayer networks are more powerful and efficient than single-layer in prediction of backbreak.

کلیدواژه‌ها [English]

  • Backbreak
  • Multiple regression analysis
  • Artificial neural networks
  • Particle swarm optimization algorithm
  • Angouran mine
[1] Abdi MJ, Giveki D (2013) Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules. Eng Appl Artif Intel 26:603–608
[2] Assareh E, Behrang MA, Assari MR, Ghanbarzadeh A (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35:5223–5229
[3] Babanouri N, Karimi Nasab S, Sarafrazi S (2013) A hybrid particle swarm optimization and multi-layer perceptron algorithm for bivariate fractal analysis of rock fractures roughness. Int J Rock Mech Min Sci 60:66–74
[4] Bauer A (1982) Wall control blasting in open pits. CIM Special 30, Canadian Institute of Mining and Metallurgy, In: 14th Can, rock mechanics symposium, pp 3–10
[5] Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum Associates, Mahwah
[6] Dreyfus G (2005) Neural Networks: methodology and application. Springer, Berlin
[7] Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp 81–86
[8] Gate WC, Ortiz BLT, Florez RM (2005) Analysis of rock fall and blasting backbreak problems. In: Proceedings of the fortieth US Rock Mechanics Symposium, paper ARMA 05-0671.
[9] Ghasemi E (2016) Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Comput Appl 28:1855-1862
[10] Ghasemi E, Kalhori H, Bagherpour R (2016) A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Eng Comput 32:607-614
[11] Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32:85-97
[12] Jenkins SS (1981) Adjusting blast design for best results. Pit and Quarry, Rotterdam, Balkema
[13] Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Rotterdam, Balkema
[14] Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Rock Mech Rock Eng 41:533-538
[15] Kahraman S, Altun H, Tezekici BS, Fener M (2006) Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks. Int J Rock Mech Min Sci 43:157–64.
[16] Kalatehjari R, Ali N, Kholghifard M, Hajihassani M (2014) The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization. Arab J Geosci 7(4):1529–1539
[17] Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, pp 1942–1948
[18] Khandelwal M, Singh TN (2006) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthquake Eng 27:116–125
[19] Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dynam Earthq Eng, 27:116-125
[20] Konya CJ (2003) Rock blasting and overbreak control. Washington, DC: National Highway Institute, FHWA-HI-92-001
[21] Konya CJ, Walter EJ (1991) Rock blasting and overbreak control. FHWA Report, FHWA-HI-92-001.
[22] Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi
[23] Maulenkamp F, Grima MA (1999) Application of neural networks for prediction of the unconfined compressive strength (UCS) from Equotip Hardness. Int J Rock Mech Min Sci 36:29–39
[24] Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63
[25] Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN, Measurement 57:122–131
[26] Monjezi M, Ahmadi Z, Varjani AY, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput Appl 23:1101–1107
[27] Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on backbreak using neural networks. International Journal of Rock Mechanics and Mining Sciences 45:1446–1453
[28] Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neurogenetic approach. Arab J Geosci 5(3):441–448
[29] Monjezi M, Mehrdanesh A, Malek A, Khandelwal M (2013) Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput Appl 23:349–356
[30] Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Systems with Applications 37:2637–2643
[31] Rafig MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79:541–1552
[32] Scoble MJ, Lizotte YC, Paventi M, Mohanty BB (1997) Measurement of blast damage. Mining Engineering 49:103–108
[33] Simpson PK (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York.
[34] Singh VK, Singh D, Singh TN (2001) Prediction of strength algorithm and artificial neural network. Sci World J, Article ID 643715.
[35] Sumathi S, Paneerselvam S (2010) Computational intelligence paradigms: theory and applications using MATLAB. CRC Press, New York
[36]  Tawadrous AS (2006) Evaluation of artificial neural networks as a reliable tool in blast design. In: Proceedings of the thirty-second Annual Conference on Explosives and Blasting Techniques, Dallas, International Society of Explosives Engineering
[37] Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Model Simul 5(6):2501–2506
[38] Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427–433
[39] Yang Y, Zhang Q (1997a) Analysis for the results of point load testing with artificial neural network. In: Proceedings of the 9th International Conference on Computer Methods and Advances in Geomechanics, Rotterdam, A.A. Balkema, pp 607–612
[40] Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026–1037.