Skip to main content

Advertisement

SpringerLink
Log in

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Monjezi M, Bahrami A, Varjani AY (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480

    Google Scholar 

  2. 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 22:432–441

    Google Scholar 

  3. Bakhtavar E, Khoshrou H, Badroddin M (2015) Using dimensional regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arab J Geosci 8:2111–2120

    Google Scholar 

  4. Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808

    Google Scholar 

  5. Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024

    Google Scholar 

  6. 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 735–740

  7. 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, Rotterdam

  8. Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M (2014) An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct Syst Int J 14(5):785–809

    Google Scholar 

  9. Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79(1):291–316

    Google Scholar 

  10. Toghroli A et al (2016) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf. https://doi.org/10.1007/s10845-016-1217-y

    Article  Google Scholar 

  11. Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22(19):3986–3997

    Google Scholar 

  12. Mansouri I et al (2016) Strength prediction of rotary brace damper using MLR and MARS. Struct Eng Mech 60(3):471–488

    Google Scholar 

  13. Zhou J, Shi X, Du K, Qiu X, Li X, Mitri HS (2017) Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. Int J Geomech 17(6):04016129

    Google Scholar 

  14. Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50(12):2177–2191

    Google Scholar 

  15. Toghroli et al (2018) Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method. Comput Concrete 21:525–530

    Google Scholar 

  16. Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659

    Google Scholar 

  17. Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Google Scholar 

  18. Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT Case histories. J Perform Constr Facil 33(3):04019024. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001292

    Article  Google Scholar 

  19. 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 46:1273–1280

    Google Scholar 

  20. 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):809–813

    Google Scholar 

  21. Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36

    Google Scholar 

  22. Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34(2):339–345

    Google Scholar 

  23. Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251

    Google Scholar 

  24. Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222

    Google Scholar 

  25. Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780

    Google Scholar 

  26. Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Autom Remote Control 25:103–109

    Google Scholar 

  27. Vapnik V, Chervonenkis A (1974) Theory of pattern recognition. Nauka, Moscow, (in Russian); German translation: Theorie der Zeichenerkennung, Akademie Verlag, Berlin, 1979

  28. Vapnik V (1982) Estimation of dependences based on empirical data. Springer Verlag, New York

    Google Scholar 

  29. Vapnik V (1995) The nature of statistical learning theory. Springer Verlag, New York

    Google Scholar 

  30. Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Neural information processing systems, vol 9. MIT Press, Cambridge

    Google Scholar 

  31. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Lett Rev 11(10):203–224

    Google Scholar 

  32. Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644

    Google Scholar 

  33. Sari PA et al (2018) An intelligent based-model role to simulate the factor of safe slope by support vector regression. Eng Comput. https://doi.org/10.1007/s00366-018-0677-4

    Article  Google Scholar 

  34. Chang CC, Lin CJ (2001) A library for support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University

  35. Anandhi V, Chezian RM (2013) Support vector regression to forecast demand and supply Pulpwood. Int J Future Commun 2(3):266

    Google Scholar 

  36. 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):1637–1643

    Google Scholar 

  37. Taheri K, Hasanipanah M, Bagheri Golzar S, Majid MZA (2016) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700

    Google Scholar 

  38. 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):705–715

    Google Scholar 

  39. Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks. San Diego, CA, pp 11–14

  40. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Google Scholar 

  41. Toghroli A, Mohammadhassani M, Shariati M, Suhatril M, Ibrahim Z, Sulong NHR (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct J 17(5):623–639

    Google Scholar 

  42. Samui P, Kim D, Viswanathan R (2015) Spatial variability of rock depth using adaptive neuro-fuzzy inference system (ANFIS) and multivariate adaptive regression spline (MARS). Environ Earth Sci. https://doi.org/10.1007/s12665-014-3711-x

    Article  Google Scholar 

  43. Safa M et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21(3):679–688

    Google Scholar 

  44. 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):441–455

    Google Scholar 

  45. Mansouri I et al (2017) Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf. https://doi.org/10.1007/s10845-017-1306-6

    Article  Google Scholar 

  46. Koçaslan A, Yüksek AG, Görgülü K, Arpaz E (2017) Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems. Environ Earth Sci 76:57

    Google Scholar 

  47. Sedghi Y et al (2018) Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct Syst 22(3):335–340

    MathSciNet  Google Scholar 

  48. Jalalifar H et al (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790

    Google Scholar 

  49. Fattahi H, Bayatzadehfard Z (2017) A comparison of performance of several artificial intelligence methods for estimation of required rotational torque to operate horizontal directional drilling. Iran Univ Sci Technol 7:45–70

    Google Scholar 

  50. Yang XS (2008) Firefly algorithm. Nat Inspired Metaheuristic Algorithms 20:79–90

    Google Scholar 

  51. Majumder A, Das A, Das PK (2018) A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel. Neural Comput Appl 29(3):665–677

    Google Scholar 

  52. Kazemivash B, Moghaddam ME (2018) A predictive model-based image watermarking scheme using regression tree and firefly algorithm. Soft Comput 22(12):4083–4098

    Google Scholar 

  53. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civil Eng 30(5):04016003

    Google Scholar 

  54. Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33(4):951–959

    Google Scholar 

  55. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Aaq Qaleh AZ (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179

    Google Scholar 

  56. 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):23–31

    Google Scholar 

  57. 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):349–359

    Google Scholar 

  58. Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307–316

    Google Scholar 

  59. 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):27

    Google Scholar 

  60. Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050

    Google Scholar 

  61. Wang M, Shi X, Zhou J (2018) Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model. Int J Rock Mech Min Sci 110:199–209

    Google Scholar 

  62. Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717

    Google Scholar 

  63. Hasanipanah M et al (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15(3):551–560

    Google Scholar 

  64. Qi C, Fourie A, Chen Q, Tang X, Zhang Q, Gao R (2018) Data-driven modelling of the flocculation process on mineral processing tailings treatment. J Clean Prod 196:505–516

    Google Scholar 

  65. 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 131:35–41

    Google Scholar 

  66. Lu X, Zhou W, Ding X, Shi X, Luan B, Li M (2019) Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill. IEEE Access. https://doi.org/10.1109/access.2019.2918177

    Article  Google Scholar 

  67. 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):1–8

    Google Scholar 

  68. Qi C, Tang X, Dong X, Chen Q, Fourie A, Liu E (2019) Towards intelligent mining for backfill: a genetic programming-based method for strength forecasting of cemented paste backfill. Miner Eng 133:69–79

    Google Scholar 

Download references

Acknowledgements

This research is partially supported by the National Natural Science Foundation Project of China (Grant no. 41807259), the State Key Laboratory of Safety and Health for Metal Mines (Grant no. 2017-JSKSSYS-04), the Natural Science Foundation of Hunan Province (Grant No. 2018JJ3693) and the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou).

Author information

Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou & Chuanqi Li

  2. State Key Laboratory of Safety and Health for Metal Mines, Maanshan, 243000, China

    Jian Zhou

  3. College of Engineering, Civil Engineering Department, Kirkuk University, Kirkuk, Iraq

    Chelang A. Arslan

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mahdi Hasanipanah

  5. School of Mining, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran

    Hassan Bakhshandeh Amnieh

Authors
  1. Jian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuanqi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chelang A. Arslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahdi Hasanipanah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hassan Bakhshandeh Amnieh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdi Hasanipanah.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, J., Li, C., Arslan, C.A. et al. Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Engineering with Computers 37, 265–274 (2021). https://doi.org/10.1007/s00366-019-00822-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00822-0

Keywords

Search

Navigation