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An optimized ANN model based on genetic algorithm for predicting ripping production

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Abstract

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

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References

  1. Tsiambaos G, Saroglou H (2010) Excavatability assessment of rock masses using the geological strength index (GSI). Bull Eng Geol Environ 69(1):13–27

    Article  Google Scholar 

  2. Hadjigeorgiou J, Poulin R (1998) Assessment of ease of excavation of surface mines. J Terramech Pergamon 35:137–153

    Article  Google Scholar 

  3. Tonnizam Mohamad E, Abad SVANK, Saad R (2011) Challenges of excavation by ripping works in weathered sedimentary zone. Electron J Geotech Eng 16:1337–1350

    Google Scholar 

  4. Basarir H, Karpuz C (2004) A rippability classification system for marls in lignite mines. Eng Geol 74(3):303–318

    Article  Google Scholar 

  5. Basarir H, Karpuz C, Tutluoglu L (2007) A fuzzy logic based rippability classification system. J S Afr Inst Min Metall 107(12):817

    Google Scholar 

  6. Thuro K, Plinninger RJ (2003) Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. In: ISRM technology roadmap for rock mechanics: South African Institute of Mining and Metallurgy, pp 1–7

  7. Fowell RJ, Johnson ST (1991) Cuttability assessment applied to drag tool tunnelling machines. In: Wittke W, Balkema AA (eds) Proceeding 7th international congress rock mechanics. ISRM, Achen, pp 985–990

  8. Bradybrooke JC (1988) The state of art of rock cuttability and rippability prediction. In: Proceedings of fifth Australia-New Zealand conference on geomechanics. August Sydney, pp 13–42

  9. Singh RN, Denby B, Egretli I (1987) Development of a new rippability index for Coal Measures excavations. In: Proceedings of the 28th US symposium on rock mechanics, Tucson, AZ, Balkema, Boston, pp 935–943

  10. Singh RN, Elmherig AM, Sunu MZ (1986) Application of rock mass characterization to the stability assessment and blast design in hard rock surface mining excavations. In: Proceedings of the 27th US symposium on rock mechanics. Alabama, pp 471–478

  11. Thuro K, Plinninger RJ, Spaun G (2002) Drilling, blasting and cutting—Is it possible to quantify geological parameters relating to excavatability? In: Proceedings of the 9th congress of the international association for engineering geology and the environment, Durban, South Africa. Engineering geology for developing countries, pp 2853–2862

  12. Komoo I (1995) Geologi Kejuruteraan-Perspektif Rantau Tropika Lembap: Kuala Lumpur. Universiti Kebangsaan Malaysia, Malaysia

    Google Scholar 

  13. Hudson JA (1999) Technical auditing of rock mechanics modeling and rock engineering design. In: 37th US symposium on rock mechanics. vol 1, Taylor & Francis, US, pp 183–197

  14. Caterpillar TC (1985) Caterpillar performance handbook, vol 2, 16th edn. Caterpillar Inc., Peoria, Illinois

  15. Hardy MP, Goodrich RR (1992) Solution mining cavity stability: a site investigation and analytical assessment. In: ISRM symposium Eurock’92. Rock characterization, British Geotechnical Society, London, pp 293–297

  16. Tripathy A, Singh TN, Kundu J (2015) Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement 68:302–309

    Article  Google Scholar 

  17. Franklin JA, Broch E, Walton G (1971) Logging the mechanical character of rock. Trans Inst Min Metall 80:A1–A9

    Google Scholar 

  18. Atkinson T (1971) Selection of open pit excavating and loading equipment. Trans Inst Min Metall 80:A101–A129

    Google Scholar 

  19. Scoble MJ, Muftuoglu YV (1984) Derivation of a diggability index for surface mine equipment selection. Min Sci Technol 1:305–322

    Article  Google Scholar 

  20. Pettifer GS, Fookes PG (1994) A revision of the graphical method for assessing the excavability of rock. Q J Eng Geol 27:145–164

    Article  Google Scholar 

  21. McLean AC, Gribble CD (1985) Geology for civil engineers, 2nd edn. George Allen and Unwin, Crows Nest, p 314

    Google Scholar 

  22. Karpuz C (1990) A classification system for excavation of surface Coal Measures. Min Sci Technol 11:157–163

    Article  Google Scholar 

  23. Church HK (1981) Excavation handbook. McGraw-Hill Inc, New York

    Google Scholar 

  24. Caterpillar TC (2001) Caterpillar performance handbook. Caterpillar Inc, Preoria

    Google Scholar 

  25. Kainthola A, Singh PK, Verma D, Singh R, Sarkar K, Singh TN (2015) Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach. Geotech Geol Eng 33(5):1255–1278

    Article  Google Scholar 

  26. Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45

    Article  Google Scholar 

  27. Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506

    Article  Google Scholar 

  28. Singh TN, Gupta AR, Sain R (2006) A comparative analysis of cognitive systems for the prediction of drillability of rocks and wear factor. Geotech Geol Eng 24(2):299–312

    Article  Google Scholar 

  29. Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116–125

    Article  Google Scholar 

  30. Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233

    Article  Google Scholar 

  31. Monjezi M, Ahmadi Z, Varjani AY, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput Appl 23(3–4):1101–1107

    Article  Google Scholar 

  32. 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):755–770

    Article  Google Scholar 

  33. Ghasemi E, Amini H, Ataei M, Khalokakaei R (2014) Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7(1):193–202

    Article  Google Scholar 

  34. 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(2):771–783

    Article  Google Scholar 

  35. 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. doi:10.1007/s00366-016-0438-1

    Google Scholar 

  36. 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

    Article  Google Scholar 

  37. Ghasemi E (2016) Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Comput Appl. doi:10.1007/s00521-016-2182-2

    Google Scholar 

  38. Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods 36:1636–1650

    Article  Google Scholar 

  39. Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22(4):808–814

    Article  Google Scholar 

  40. Khandelwal M, Singh TN (2009) Correlating static properties of coal measures rocks with P-wave velocity. Int J Coal Geol 79:55–60

    Article  Google Scholar 

  41. Khandelwal M, Jahed Armaghani D (2015) Prediction of drillability of rocks with strength properties using a hybrid GA–ANN technique. Geotech Geol Eng. doi:10.1007/s10706-015-9970-9

    Google Scholar 

  42. Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22(7–8):1685–1693

    Article  Google Scholar 

  43. Lee Y, Oh SH, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Proceedings of the international joint conference on neural networks, pp 765–770

  44. Singh TN, Kanchan R, Verma AK, Saigal K (2005) A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass. J Earth Syst Sci 114(1):75–86

    Article  Google Scholar 

  45. McCulloch Warren S, Walter Pitts (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  46. Ferreira PM, Ruano AE, Silva SM, Conceicao EZE (2012) Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy Build 55:238–251

    Article  Google Scholar 

  47. Morel N, Bauer M, El-Khoury M, Krauss J (2001) Neurobat, a predictive and adaptive heating control system using artificial neural network. Int J Solar Energy 21:161–201

    Article  Google Scholar 

  48. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall Inc, Upper Saddle River

    MATH  Google Scholar 

  49. Hajihassani M et al (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  50. Zhang W (2010) Computational ecology: artificial neural networks and their applications. World Scientific, Singapore

    Book  MATH  Google Scholar 

  51. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor (second edition: MIT Press, 1992)

  52. Goldberg DE (1989) Genetic algorithms in search. Optimization and Machine Learning. Addison-Wesley, New York

    MATH  Google Scholar 

  53. Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken

    Book  Google Scholar 

  54. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence, vol 21. Springer, Berlin

    MATH  Google Scholar 

  55. Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12:265–319

    Article  MATH  Google Scholar 

  56. Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Petroleum Sci Eng 59:97–105

    Article  Google Scholar 

  57. Singh TN, Verma AK, Sharma PK (2007) A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis. Geotech Geol Eng 25(4):395–407

    Article  Google Scholar 

  58. 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):174

    Article  Google Scholar 

  59. Priest SD, Hudson JA (1976) Discontinuity spacings in rock. Int J Rock Mech Mineral Sci Geomech Abstr 13:135–148

    Article  Google Scholar 

  60. ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Hudson JA, Ulusay R (eds) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics, Ankara

    Google Scholar 

  61. Khamesi H, Torabi S, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: Case Study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng 29(6):05014010

    Article  Google Scholar 

  62. Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3–4):141–158

    Article  Google Scholar 

  63. 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. doi:10.1007/s00366-015-0402-5

    Google Scholar 

  64. Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  65. Looney CG. Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226

  66. Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading MA

    MATH  Google Scholar 

  67. Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, OH. pp 277–280

  68. Singh TN, Singh VK, Sinha S (2006) Prediction of cadmium removal using an artificial neural network and a neuro-fuzzy technique. Mine Water Environ 25(4):214–219

    Article  Google Scholar 

  69. 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. doi:10.1007/s00366-015-0400-7

    Google Scholar 

  70. 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

  71. Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235

    Article  Google Scholar 

  72. Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123

    Chapter  Google Scholar 

  73. Paola JD (1994) Neural network classification of multispectral imagery. M.Sc. thesis, The University of Arizona, USA

  74. Wang C (1994) A theory of generalization in learning machines with neural application. Ph.D. thesis, The University of Pennsylvania, USA

  75. Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston

    MATH  Google Scholar 

  76. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  77. Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  78. Chambers LD (2010) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Washington

    Google Scholar 

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Acknowledgments

The authors want to extend their gratefulness to the Government of Malaysia and Universiti Teknologi Malaysia for the FRGS Grant Number of 4F406 and for giving the needed facilities that made this study possible.

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Authors and Affiliations

  1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

    Edy Tonnizam Mohamad & Danial Jahed Armaghani

  2. Department of Mining, Tarbiat Modares University, Tehran, 14115-143, Iran

    Roohollah Shirani Faradonbeh & Masoud Monjezi

  3. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Muhd Zaimi Abd Majid

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  1. Edy Tonnizam Mohamad
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  3. Danial Jahed Armaghani
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Correspondence to Danial Jahed Armaghani.

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Mohamad, E.T., Faradonbeh, R.S., Armaghani, D.J. et al. An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput & Applic 28 (Suppl 1), 393–406 (2017). https://doi.org/10.1007/s00521-016-2359-8

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