Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak

  • Mahdi Hasanipanah
  • Hassan Bakhshandeh AmniehEmail author
Original Article


This study proposes a new uncertain rule-based fuzzy approach for the evaluation of blast-induced backbreak. The proposed approach is based on rock engineering systems (RES) updated by the fuzzy system. Additionally, a genetic algorithm (GA) and imperialist competitive algorithm (ICA) were employed for the prediction aim. The most key step in modeling of fuzzy RES is the coding of the interaction matrix. This matrix is responsible for analyzing the interrelationships among the parameters influencing the rock engineering activities. The codes of the interaction matrix are not unique; thus, probabilistic coding can be done non-deterministically, which allows the uncertainties to be considered in the RES analysis. To achieve the objective of this research, 62 blasts in Shur River dam region, located in south of Iran, were investigated and the required datasets were measured. The performance of the proposed models was then evaluated in accordance with the statistical criteria such as coefficient of determination (R2). The results signify the effectiveness of the proposed GA- and ICA-based models in the simulating process. R2 of 0.963 and 0.934 obtained from ICA- and GA-based models, respectively, revealed that both models were capable of predicting the backbreak. Further, the fuzzy RES was introduced as a powerful uncertain approach to evaluate and predict the backbreak.


Blasting Backbreak RES Fuzzy system Uncertainty 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22(7–8):1637–1643CrossRefGoogle Scholar
  2. 2.
    Sari M, Ghasemi E, Ataei M (2014) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mech Rock Eng 47:771–783CrossRefGoogle Scholar
  3. 3.
    Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. CrossRefGoogle Scholar
  4. 4.
    Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817CrossRefGoogle Scholar
  5. 5.
    Khandelwal M, Monjezi M (2013) Prediction of backbreak in open pit blasting operations using the machine learning method. Rock Mech Rock Eng 46:389–396CrossRefGoogle Scholar
  6. 6.
    Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455CrossRefGoogle Scholar
  7. 7.
    Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396CrossRefGoogle Scholar
  8. 8.
    Jahed Armaghani D, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2015) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32(1):109–121CrossRefGoogle Scholar
  9. 9.
    Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model. Eng Comput 33(1):23–31CrossRefGoogle Scholar
  10. 10.
    Marto A, Hajihassani M, Armaghani DJ, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 5:643715Google Scholar
  11. 11.
    Hasanipanah M, Shahnazar A, Arab H, Golzar SB, Amiri M (2017) Developing a new hybrid-AI model to predict blast induced backbreak. Eng Comput 33(3):349–359CrossRefGoogle Scholar
  12. 12.
    Hasanipanah M, Faradonbeh RS, Armaghani DJ, Amnieh HB, Khandelwal M (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci 76(1):27CrossRefGoogle Scholar
  13. 13.
    Nguyen H, Bui XN, Tran QH, Mai NL (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms. Appl Soft Comput. CrossRefGoogle Scholar
  14. 14.
    Lu X, Hasanipanah M, Brindhadevi K, Amnieh HB, Khalafi S (2019) ORELM: A novel machine learning approach for prediction of flyrock in mine blasting. Nat Resour Res. CrossRefGoogle Scholar
  15. 15.
    Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement 131:35–41CrossRefGoogle Scholar
  16. 16.
    Gao W, Alqahtani AS, Mubarakali A, Mavaluru D, Khalafi S (2019) Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Eng Comput 35(131):1–8Google Scholar
  17. 17.
    Yang H, Hasanipanah M, Tahir MM, Bui DT (2019) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res. CrossRefGoogle Scholar
  18. 18.
    Monjezi M, Amini Khoshalan H, Yazdian Varjani A (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5:441–448CrossRefGoogle Scholar
  19. 19.
    Mohammadnejad M, Gholami R, Sereshki F, Jamshidi A (2013) A new methodology to predict backbreak in blasting operation. Int J Rock Mech Min Sci 60:75–81CrossRefGoogle Scholar
  20. 20.
    Ebrahimi E, Monjezi M, Khalesi MR, Jahed A (2015) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. CrossRefGoogle Scholar
  21. 21.
    Ghasemi E, Bakhshandeh Amnieh H, Bagherpour R (2016) Assessment of backbreak due to blasting operation in open pit mines: a case study. Environ Earth Sci 75:552CrossRefGoogle Scholar
  22. 22.
    Hasanipanah M, Shahnazar A, Arab H, Golzar SB, Amiri M (2017) Developing a new hybrid-AI model to predict blast-induced backbreak. Eng Comput 33(3):349–359CrossRefGoogle Scholar
  23. 23.
    Konya CJ, Walter EJ (2003) Rock blasting and overbreak control. National Highway Institute, ArlingtonGoogle Scholar
  24. 24.
    Blair DP, Armstrong LW (2001) The influence of burden on blast vibration. Fragblast 5:108–129CrossRefGoogle Scholar
  25. 25.
    Gate WC, Ortiz BLT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems. In: Proceedings of the 40th U.S. symposium on rock mechanics (USRMS), Anchorage, Alaska, June 2005, vol 5, pp 671–680Google Scholar
  26. 26.
    Hustrulid WA, Lu WB (2002) Some general design concepts regarding the control of blast-induced damage during rock slope excavation. In: Proceedings of the 7th international symposium on rock fragmentation by blasting, Beijing, China, August 2002, pp 595–604Google Scholar
  27. 27.
    Jhanwar JC, Jethwa JL (2000) The use of air decks in production blasting in an open pit coal mine. Geotech Geol Eng 18:269–287CrossRefGoogle Scholar
  28. 28.
    Aghajani Bazzazi A, Mansouri H, Ebrahimi Farsangi MA, Atashpanjeh A (2006) Application of controlled blasting (presplitting) using large diameter holes in Sarcheshmeh copper mine. In: Proceedings of the 8th international symposium on rock fragmentation by blasting, Santiago, Chile, May 2006, pp 388–392Google Scholar
  29. 29.
    Bhandari S (1997) Engineering rock blasting operations. Balkema, RotterdamGoogle Scholar
  30. 30.
    Firouzadj A, Ebrahimi Farsangi MA, Mansouri H, Esfahani SK (2006) Application of controlled blasting (pre-splitting) in Sarcheshmeh copper mine. In: Proceedings of the 8th international symposium on rock fragmentation by blasting, Santiago, Chile, May 2006, pp 383–387Google Scholar
  31. 31.
    Enayatollahi I, Aghajani-Bazzazi A (2010) 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, September 2009, pp 127–133Google Scholar
  32. 32.
    Iverson SR, Hustrulid WA, Johnson JC, Tesarik D, Akbarzadeh Y (2010) The extent of blast damage from a fully coupled explosive charge. In: Proceedings of the 9th international symposium on rock fragmentation by blasting, Granada, Spain, September 2009, pp 459–468Google Scholar
  33. 33.
    Jia Z, Chen G, Huang S (1998) Computer simulation of open pit bench blasting in jointed rock mass. Int J Rock Mech Min Sci 35:476–486CrossRefGoogle Scholar
  34. 34.
    Esmaeili M, Osanloo M, Rashidinejad F, Aghajani Bazzazi A, Taji M (2012) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput. CrossRefGoogle Scholar
  35. 35.
    Hudson JA (1992) Rock engineering systems, theory and practice. Ellis Horwood, ChichesterGoogle Scholar
  36. 36.
    Rafiee R, Ataei M, Khalokakaie R, Esmaeil Jalali SM, Sereshki F (2015) Determination and assessment of parameters influencing rock mass cavability in block caving mines using the probabilistic rock engineering system. Rock Mech Rock Eng 48:1207–1220CrossRefGoogle Scholar
  37. 37.
    Rafiee R, Ataei M, Khalokakaie R, Esmaeil Jalali SM, Sereshki F (2015) A fuzzy rock engineering system to assess rock mass cavability in block caving mines. Neural Comput Appl. CrossRefGoogle Scholar
  38. 38.
    Zare Naghadehi M, Jimenez R, KhaloKakaie R, Jalali S-ME (2013) A new open-pit mine slope instability index defined using the improved rock engineering systems approach. Int J Rock Mech Min Sci 61:1–14CrossRefGoogle Scholar
  39. 39.
    Rafiee R, Ataei M, KhalooKakaie R (2015) A new cavability index in block caving mines using fuzzy rock engineering system. Int J Rock Mech Min Sci 77:68–76CrossRefGoogle Scholar
  40. 40.
    Benardos AG, Kaliampakos DC (2004) A methodology for assessing geotechnical hazards for TBM tunnelling—illustrated by the Athens Metro, Greece. Int J Rock Mech Min Sci 41:987–999CrossRefGoogle Scholar
  41. 41.
    Yang YJ, Zhang Q (1998) The application of neural networks to rock engineering systems (RES). Int J Rock Mech Min Sci 35(6):727–745CrossRefGoogle Scholar
  42. 42.
    Zare Naghadehi M, Jimenez R, KhaloKakaie R, Jalali S-ME (2011) A probabilistic systems methodology to analyze the importance of factors affecting the stability of rock slopes. Eng Geol 118(3):82–92CrossRefGoogle Scholar
  43. 43.
    Zimmermann HJ (1999) Practical applications of fuzzy technologies, operations research, RWTH. Kluwer Academic Publishers, AachenCrossRefGoogle Scholar
  44. 44.
    Jin Y, Von Seelen W, Sendhoff B (1999) On generating FC fuzzy rule systems from data using evolution strategies. IEEE Trans Syst Man Cybern B Cybern 29(6):829–845CrossRefGoogle Scholar
  45. 45.
    Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37(3):2265–2272CrossRefGoogle Scholar
  46. 46.
    Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643CrossRefGoogle Scholar
  47. 47.
    Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851CrossRefGoogle Scholar
  48. 48.
    Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770CrossRefGoogle Scholar
  49. 49.
    Hasanipanah M, Bakhshandeh Amnieh H, Khamesi H, Jahed Armaghani D, Bagheri Golzar S, Shahnazar A (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15(3):551–560CrossRefGoogle Scholar
  50. 50.
    Faramarzi F, Ebrahimi Farsangi MA, Mansouri H (2013) An RES-based model for risk assessment and prediction of backbreak in bench blasting. Rock Mech Rock Eng 46:877–887CrossRefGoogle Scholar
  51. 51.
    Nikafshan Rad H, Bakhshayeshi I, Wan Jusoh WA, Tahir MM, Kok Foong L (2019) Prediction of flyrock in mine blasting: a new computational intelligence approach. Nat Resour Res. CrossRefGoogle Scholar
  52. 52.
    Soltani S, Hezarkhani A, Tercan AE, Karimi B (2011) Use of genetic algorithm in optimally locating additional drill holes. J Min Sci 47(1):62–72CrossRefGoogle Scholar
  53. 53.
    Moghaddasi MR, Noorian-Bidgoli M (2018) ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling. Tunn Undergr Space Technol 79:197–209CrossRefGoogle Scholar
  54. 54.
    Tonnizam Mohamad E, Jahed Armaghani D, Hasanipanah M, Murlidhar BR, Alel MNA (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75(2):1–15CrossRefGoogle Scholar
  55. 55.
    Alameer Z, Abd Elaziz M, Ewees AA, Ye H, Jianhua Z (2019) Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Nat Resour Res. CrossRefGoogle Scholar
  56. 56.
    Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29(9):457–465CrossRefGoogle Scholar
  57. 57.
    Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. CrossRefGoogle Scholar
  58. 58.
    Moosazadeh S et al (2019) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 35(2):579–591CrossRefGoogle Scholar
  59. 59.
    Biswas R, Samui P, Rai B (2019) Determination of compressive strength using relevance vector machine and emotional neural network. Asian J Civ Eng 20(8):1109–1118CrossRefGoogle Scholar
  60. 60.
    Kumar M, Samui P (2019) Reliability analysis of pile foundation using ELM and MARS. Geotech Geol Eng 37(4):3447–3457CrossRefGoogle Scholar
  61. 61.
    Soltani-Mohammadi S, Safa M, Mokhtari H (2016) Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization. Comput Geosci 95:146–155CrossRefGoogle Scholar
  62. 62.
    Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715CrossRefGoogle Scholar
  63. 63.
    Samui P, Kumar R, Yadav U, Kumari S, Bui DT (2019) Reliability analysis of slope safety factor by using GPR and GP. Geotech Geol Eng 37(3):2245–2254CrossRefGoogle Scholar
  64. 64.
    Gholami A, Bonakdari H, Samui P, Mohammadian M, Gharabaghi B (2019) Predicting stable alluvial channel profiles using emotional artificial neural networks. Appl Soft Comput 78:420–437CrossRefGoogle Scholar
  65. 65.
    Abbaszadeh M, Hezarkhani A, Soltani-Mohammadi S (2013) An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Chemie der Erde-Geochemistry 73(4):545–554CrossRefGoogle Scholar
  66. 66.
    Hajihassani M, Kalatehjari R, Marto A, Mohamad H, Khosrotash M (2019) 3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods. Eng Comput. CrossRefGoogle Scholar
  67. 67.
    Samui P, Hoang ND, Nhu VH, Nguyen ML, Ngo PTT, Bui DT (2019) A new approach of hybrid bee colony optimized neural computing to estimate the soil compression coefficient for a housing construction project. Appl Sci 9(22):4912CrossRefGoogle Scholar
  68. 68.
    Aalianvari A, Soltani-Mohammadi S, Rahemi Z (2018) Estimation of geomechanical parameters of tunnel route using geostatistical methods. Geomech Eng 14(5):453–458Google Scholar
  69. 69.
    Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146–1153CrossRefGoogle Scholar
  70. 70.
    Nhu VH, Samui P, Kumar D, Singh A, Hoang ND, Bui DT (2019) Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study. Eng Comput 1–12Google Scholar
  71. 71.
    Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344CrossRefGoogle Scholar
  72. 72.
    Dutta S, Samui P, Kim D (2018) Comparison of machine learning techniques to predict compressive strength of concrete. Comput Concr 21(4):463–470Google Scholar
  73. 73.
    Zhou J, Li C, Arslan CA, Hasanipanah M, Amnieh HB (2019) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput. CrossRefGoogle Scholar
  74. 74.
    Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5:318–324CrossRefGoogle Scholar
  75. 75.
    Asteris PG, Armaghani DJ, Hatzigeorgiou Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24(5):469–488Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Mining EngineeringUniversity of KashanKashanIran
  2. 2.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  3. 3.School of Mining, College of EngineeringUniversity of TehranTehranIran

Personalised recommendations