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Engineering with Computers

, Volume 35, Issue 1, pp 47–56 | Cite as

Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting

  • S. Farid F. Mojtahedi
  • Isa Ebtehaj
  • Mahdi HasanipanahEmail author
  • Hossein Bonakdari
  • Hassan Bakhshandeh Amnieh
Original Article

Abstract

In the open-pit mines and civil projects, drilling and blasting is the most common method for rock fragmentation aims. This article proposes a new hybrid forecasting model based on firefly algorithm, as an algorithm optimizer, combined with the adaptive neuro-fuzzy inference system for estimating the fragmentation. In this regard, 72 datasets were collected from Shur river dam region, and the required parameters were measured. Using the different input parameters, six hybrid models were constructed. In these models, 58 and 14 data were used for training and testing, respectively. The proposed hybrid models were then evaluated in accordance with statistical criteria such as coefficient of determination and Nash and Sutcliffe. Based on obtained results, the proposed model with five input parameters, including burden, spacing, stemming, powder factor and maximum charge per delay can estimate rock fragmentation better than the linear multiple regression. The values of the coefficient of determination for the proposed hybrid model and linear multiple regression were 0.980 and 0.669, respectively, that demonstrate the hybrid forecasting model proposed in the present study can be introduced as a reliable method for estimating the fragmentation.

Keywords

Blasting Fragmentation ANFIS Firefly algorithm 

References

  1. 1.
    Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A (2016) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput.  https://doi.org/10.1007/s00366-016-0462-1 Google Scholar
  2. 2.
    Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2434-1 Google Scholar
  3. 3.
    Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2016) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput.  https://doi.org/10.1007/s00366-016-0497-3 Google Scholar
  4. 4.
    Hasanipanah M, Jahed Armaghani D,MonjeziM, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci.  https://doi.org/10.1007/s12665-016-5503-y Google Scholar
  5. 5.
    Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2016) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput.  https://doi.org/10.1007/s00366-016-0453-2 Google Scholar
  6. 6.
    Fouladgar N, Hasanipanah M, Bakhshandeh Amnieh H (2016) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput.  https://doi.org/10.1007/s00366-016-0463-0 Google Scholar
  7. 7.
    Hasanipanah M, Shirani Faradonbeh R, Bakhshandeh Amnieh H, Jahed Armaghani D, Monjezi M (2016) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33(2):307–316.  https://doi.org/10.1007/s00366-016-0475-9 CrossRefGoogle Scholar
  8. 8.
    MacKenzie AS (1966) Cost of explosives—do you evaluate it properly? Min Congr J 52:32–41Google Scholar
  9. 9.
    Morin AM, Ficarazzo F (2006) Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. Comput Geosci 32:352–359CrossRefGoogle Scholar
  10. 10.
    Monjezi M, Rezaei M, Yazdian Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46:1273–1280CrossRefGoogle Scholar
  11. 11.
    Shams S, Monjezi M, Johari Majd V, Jahed Armaghani D (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci.  https://doi.org/10.1007/s12517-015-1952-y Google Scholar
  12. 12.
    Trivedi R, Singh TN, Raina AK (2016) Simultaneous prediction of blast-induced flyrock and fragmentation in opencast limestone mines using back propagation neural network. Int J Min Miner Eng 7(3):237–252CrossRefGoogle Scholar
  13. 13.
    Bhandari S (1997) Engineering rock blasting operations. A.A. Balkema, NetherlandsGoogle Scholar
  14. 14.
    Hustrulid WA (1999) Blasting principles for open pit mining: general design concepts. Balkema, NetherlandsGoogle Scholar
  15. 15.
    Mishnaevsky JR, Schmauder S (1996) Analysis of rock fragmentation with the use of the theory of fuzzy sets. In: Barla (ed) Proceedings of the Eurock, ISRM International Symposium, vol 96. International Society for Rock Mechanics and Rock Engineering, pp 735–740Google Scholar
  16. 16.
    Roy PP, Dhar BB (1996) Fragmentation analyzing scale—a new tool for breakage assessment. In: Proceedings 5th international symposium on rock fragmentation by blasting-FRAGBLAST 5. Balkema, RotterdamGoogle Scholar
  17. 17.
    Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27:177–181CrossRefGoogle Scholar
  18. 18.
    Karami A, Afiuni-Zadeh S (2013) Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro fuzzy inference system (ANFIS). Int J Min Sci Technol 23(6):809–813CrossRefGoogle Scholar
  19. 19.
    Mokfi T, Almaeenejad M, Sedighi MM (2011) A data mining based algorithm to enhance maintenance management: a medical equipment case study. In: Informatics and computational intelligence (ICI), first international conference, IEEE. pp 74–80Google Scholar
  20. 20.
    Sedighi MM, Mokfi T, Golrizgashti S (2012) Proposing a customer knowledge management model for customer value augmentation: a home appliances case study. J Database Marketing Customer Strategy Manag 19(4):321–347CrossRefGoogle Scholar
  21. 21.
    Mokfi T, Shahnazar A, Bakhshayeshi I, Mahmodi Derakhsh A, Tabrizi O (2018) Proposing a new soft computing-based model to predict peak particle velocity induced by blasting. Eng Comput.  https://doi.org/10.1007/s00366-018-0578-6 Google Scholar
  22. 22.
    Ahmad M, Ansari MK, Sharma LK, Singh R, Singh TN (2017) Correlation between strength and durability indices of rocks-soft computing approach. Proc Eng 191:458–466CrossRefGoogle Scholar
  23. 23.
    Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507CrossRefGoogle Scholar
  24. 24.
    Hasanipanah M et al (2016) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol.  https://doi.org/10.1007/s13762-017-1395-y Google Scholar
  25. 25.
    Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169CrossRefGoogle Scholar
  26. 26.
    Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715.  https://doi.org/10.1007/s00366-016-0447-0 CrossRefGoogle Scholar
  27. 27.
    Sharma LK, Vishal V, Singh TN (2017) Predicting CO2 permeability of bituminous coal using statistical and adaptive neuro-fuzzy analysis. J Nat Gas Sci Eng.  https://doi.org/10.1016/j.jngse.2017.02.037 Google Scholar
  28. 28.
    Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119CrossRefGoogle Scholar
  29. 29.
    Monjezi M, Bahrami A, Yazdian Varjani A (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480CrossRefGoogle Scholar
  30. 30.
    Shi XZ, Zhou J, Wu B, Huang D, Wei W (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432–441CrossRefGoogle Scholar
  31. 31.
    Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2016) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2746-1 Google Scholar
  32. 32.
    Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  33. 33.
    Vieira JF, Dias M, Mota A (2004) Neuro-fuzzy systems: a survey, 5th WSEAS NNA international conference on neural networks and applications, Udine, ItaliaGoogle Scholar
  34. 34.
    Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, NJGoogle Scholar
  35. 35.
    Azimi H, Bonakdari H, Ebtehaj I, Michelson DG (2016) A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2560-9 Google Scholar
  36. 36.
    Mousavi SJ, Ponnambalam K, Karray F (2007) Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets Syst 158:1064–1082MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Chang BR (2008) Resolving the forecasting problems of overshoot and volatility clustering using ANFIS coupling nonlinear heteroscedasticity with quantum tuning. Fuzzy Sets Syst 159:3183–3200MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Aliyari Shoorehdeli M, Teshnehlab M, Khaki Sedigh A (2009) Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter. Fuzzy Sets Syst 160:922–948MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Nguyena SD, Choi SB (2015) Design of a new adaptive neuro-fuzzy inference system based on a solution for clustering in a data potential field. Fuzzy Sets Syst 279:64–86MathSciNetCrossRefGoogle Scholar
  40. 40.
    Azimi H, Bonakdari H, Ebtehaj I, Talesh SH, Michelson DG, Jamali A (2016) Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets Syst.  https://doi.org/10.1016/j.fss.2016.10.010 Google Scholar
  41. 41.
    Yang XS (2008) Firefly algorithm (chap. 8). In: Nature-inspired metaheuristic algorithms. Luniver Press, UKGoogle Scholar
  42. 42.
    Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, UKGoogle Scholar
  43. 43.
    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Ins Comp 2(2):78–84CrossRefGoogle Scholar
  44. 44.
    Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghani D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297CrossRefGoogle Scholar
  45. 45.
    Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455.  https://doi.org/10.1007/s00366-015-0425-y CrossRefGoogle Scholar
  46. 46.
    Hasanipanah M, Shirani Faradonbeh R, Jahed Armaghani D, Bakhshandeh Amnieh H, 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
  47. 47.
    Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Tonnizam Mohamad E (2016) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2577-0 Google Scholar
  48. 48.
    Shirani Faradonbeh R, Monjezi M, Jahed Armaghani D (2015) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput.  https://doi.org/10.1007/s00366-015-0404-3 Google Scholar
  49. 49.
    Enayatollahi I, Bazzazi AA, Asadi A (2014) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47:799–807CrossRefGoogle Scholar
  50. 50.
    Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211.  https://doi.org/10.1016/j.cageo.2011.10.031 CrossRefGoogle Scholar
  51. 51.
    Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168.  https://doi.org/10.1016/j.asoc.2014.11.01 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • S. Farid F. Mojtahedi
    • 1
  • Isa Ebtehaj
    • 2
    • 3
  • Mahdi Hasanipanah
    • 4
    Email author
  • Hossein Bonakdari
    • 2
    • 3
  • Hassan Bakhshandeh Amnieh
    • 5
  1. 1.Civil Engineering DepartmentSharif University of TechnologyTehranIran
  2. 2.Department of Civil EngineeringRazi UniversityKermanshahIran
  3. 3.Water and Wastewater Research CenterRazi UniversityKermanshahIran
  4. 4.Young Researchers and Elite Club, Qom BranchIslamic Azad UniversityQomIran
  5. 5.School of Mining, College of EngineeringUniversity of TehranTehranIran

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