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Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

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Abstract

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock.

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References

  1. Bhandari S (1997) Engineering rock blasting operations. Taylor & Francis, Boca Raton

    Google Scholar 

  2. 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(12):5383–5396

    Article  Google Scholar 

  3. 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. doi:10.1007/s00366-015-0404-3

    Google Scholar 

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

    Article  Google Scholar 

  5. Ghasemi E, Sari M, Ataei M (2012) Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int J Rock Mech Min 2(52):163–170

    Article  Google Scholar 

  6. 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(2):349–356

    Article  Google Scholar 

  7. Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (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 

  8. 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 

  9. Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-015-0720-2

    Google Scholar 

  10. Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Jahed Armaghani D (2015) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. DOI, Eng Comput. doi:10.1007/s00366-015-0415-0

    Google Scholar 

  11. Khandelwal M, Monjezi M (2013) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Min Sci Technol 23(3):313–316

    Article  Google Scholar 

  12. Raina AK, Murthy VMSR, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ. doi:10.1007/s10064-014-0588-6

    Google Scholar 

  13. Institute of Makers of Explosives (IME) (1997) Glossary of commercial explosive industry terms. Safety Publication, Institute of Makers of Explosives, Washington, DC, No 12, p 16

  14. Hemphill GB (1981) Blasting operations. McGraw-Hill, New York

    Google Scholar 

  15. Bajpayee TS, Rehak TR, Mowrey GL, Ingram DK (2004) Blasting injuries in surface mining with emphasis on flyrock and blast area security. J Saf Res 35(1):47–57

    Article  Google Scholar 

  16. Kecojevic V, Radomsky M (2005) Flyrock phenomena and area security in blasting-related accidents. Saf Sci 43:739–750

    Article  Google Scholar 

  17. Roy PP (2005) Rock blasting effects and operations. Taylor & Francis, Boca Raton

    Google Scholar 

  18. Little TN, Blair DP (2009) Mechanistic Monte Carlo models for analysis of flyrock risk. In: Ninth international symposium on rock fragmentation by blasting, Granada, Spain, pp 641–647

  19. Lundborg N, Persson N, Ladegaard-Pedersen A, Holmberg R (1975) Keeping the lid on flyrock in open pit blasting. Eng Min J 176:95–100

    Google Scholar 

  20. Roth JA (1979) A model for the determination of flyrock range as a function of shot condition. US Department of Commerce, NTIS report no. PB81222358

  21. Gupta RN (1980) Surface blasting and its impact on environment. In: Trivedy NJ, Singh BP (eds) Impact of mining on environment. Ashish Publishing House, New Delhi, pp 23–24

    Google Scholar 

  22. Chiapetta RF, Bauer A, Dailey PJ, Burchell SL (1983) The use of high-speed motion picture photography in blast evaluation and design. In: Proceedings of 9th conference on explosives and blasting techniques, Dallas, USA; p 31–40

  23. McKenzie CK (2009) Flyrock range and fragment size prediction. http://docs.isee.org/ISEE/Support/Proceed/General/09GENV2/09v206g.pdf

  24. Trivedi R, Singh TN, Raina AK (2014) Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J Rock Mech Geotech Eng 6(5):447–454

    Article  Google Scholar 

  25. Marto A, Hajihassani M, Jahed Armaghani D, 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. doi:10.1155/2014/643715

  26. Armaghani D, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8(11):9647–9665. doi:10.1007/s12517-015-1908-2

    Article  Google Scholar 

  27. Gibson MFL, St George JD (2001) Implications of flyrock associated with blasting in urban areas. In: AusIMM NZ branch conference, Auckland

  28. St George JD, Gibson MFL (2001) Estimation of flyrock travel distances: a probabilistic approach. In: Proceedings Explo 2001, AusIMM, Melbourne, Australia, pp 245–248

  29. Singh TN, Verma AK (2010) Sensitivity of total charge and maximum charge per delay on ground vibration. Geomat Nat Hazards Risk 1:259–272

    Article  Google Scholar 

  30. Verma AK, Singh TN (2009) A neuro-genetic approach for prediction of compressional wave velocity of rock and its sensitivity analysis. Int J Earth Sci Eng 2:81–94

    Google Scholar 

  31. 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 

  32. Singh R, Vishal V, Singh TN (2012) Soft computing method for assessment of compressional wave velocity. Sci Iran 19(4):1018–1024

    Article  Google Scholar 

  33. 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 

  34. Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl. doi:10.1007/s00521-016-2434-1

    Google Scholar 

  35. Abad SVANK, Yilmaz M, Jahed Armaghani D, Tugrul A (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl. doi:10.1007/s00521-016-2456-8

    Google Scholar 

  36. Armaghani DJ, Mohamad ET, Momeni E, Monjezi M, Narayanasamy MS (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9(1):1–16

    Article  Google Scholar 

  37. Mohamad ET, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl. doi:10.1007/s00521-016-2359-8

    Google Scholar 

  38. Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49(2):298–305

    Article  Google Scholar 

  39. 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 

  40. 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

    Article  Google Scholar 

  41. Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mech Geotech Eng 5(4):325–329

    Article  Google Scholar 

  42. Ahangari K, Moeinossadat SR, Behnia D (2015) Estimation of tunnelling-induced settlement by modern intelligent methods. Soils Found 55(4):737–748

    Article  Google Scholar 

  43. Faradonbeh RS, Armaghani DJ, Majid MA, Tahir MM, Murlidhar BR, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol. doi:10.1007/s13762-016-0979-2

    Google Scholar 

  44. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  45. Keshavarz A, Mehramiri M (2015) New gene expression programming models for normalized shear modulus and damping ratio of sands. Eng Appl Artif Intell 45:464–472

    Article  Google Scholar 

  46. Faradonbeh RS, Armaghani DJ, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75:993–1006. doi:10.1007/s10064-016-0872-8

    Article  Google Scholar 

  47. Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput. doi:10.1007/s00366-016-0448-z

    Google Scholar 

  48. Ferreira C (2006) Gene expression programming: mathematical modeling by an Artificial intelligence, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  49. Steeb WH (2011) The nonlinear workbook: chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, fuzzy logic with C++, Java and symbolic C++ programs. World Scientific, Singapore

    Book  MATH  Google Scholar 

  50. Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141:92–113

    Article  Google Scholar 

  51. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  52. Ferreira C (2002) Gene expression programming in problem solving. In: Roy R, Koppen M, Ovaska S, Furuhashi T, Hoffmann F (eds) Soft computing and industry—recent applications. Springer, Berlin, pp 635–654

    Google Scholar 

  53. Teodorescu L, Sherwood D (2008) High energy physics event selection with gene expression programming. Comput Phys Commun 178:409–419

    Article  Google Scholar 

  54. Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087

    Article  Google Scholar 

  55. Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. Jason Brownlee, Melbourne

    Google Scholar 

  56. Baykasoglu A, Gullu H, Canakci H, Ozbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

    Article  Google Scholar 

  57. Yang Y et al (2013) A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming. J Netw Comput Appl 36(6):1540–1550

    Article  Google Scholar 

  58. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications, SAGA 2009, Lecture Notes in Computer Science, vol 5792, pp 169–178

  59. Yang XS (2010) Firefly algorithm, levy flights and global optimization. In: Bramer M et al (eds) Research and development in intelligent systems XXVI. Springer, London, pp 209–218

    Chapter  Google Scholar 

  60. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-inspir Comput 2:78–84

    Article  Google Scholar 

  61. Kwiecień J, Filipowicz B (2012) Firefly algorithm in optimization of queueing systems. Bull Pol Acad Sci Tech Sci 60(2):363–368

    Google Scholar 

  62. Alweshah M (2014) Firefly algorithm with artificial neural network for time series problems. Res J Appl Sci Eng Technol 7(19):3978–3982

    Google Scholar 

  63. Balachennaiah P, Suryakalavathi M, P. Nagendra (2015) Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm. Eng Sci Technol Int J. doi:10.1016/j.jestch.2015.10.008

  64. Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl 42(21):8221–8231

    Article  Google Scholar 

  65. Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Int J Electr Power Energy Syst 66:9–24

    Article  Google Scholar 

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

  67. Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425

    Article  Google Scholar 

  68. Amini H, Gholami R, Monjezi M, Rahman Torabi S, Zadhesh J (2012) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl 21:2077–2085

    Article  Google Scholar 

  69. 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 32:109–121. doi:10.1007/s00366-015-0402-5

    Article  Google Scholar 

  70. Armaghani D, Mohamad ET, Momeni E, Narayanasamy MS (2015) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on main range granite. Bull Eng Geol Environ 74:1301–1319

    Article  Google Scholar 

  71. Middleton GV (2000) Data analysis in the earth sciences using MATLAB. Prentice Hall

  72. MATLAB (2006) Statistics toolbox for use with MATLAB, user’s guide version 5. The MathWorks, Inc

  73. Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1–2):51–60

    Article  Google Scholar 

  74. Tiryaki B (2009) Estimating rock cuttability using regression trees and artificial neural networks. Rock Mech Rock Eng 42(6):939–946

    Article  Google Scholar 

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

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Authors and Affiliations

  1. Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran, 14115-143, Iran

    Roohollah Shirani Faradonbeh

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

    Danial Jahed Armaghani & Edy Tonnizam Mohamad

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

    Hassan Bakhshandeh Amnieh

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  1. Roohollah Shirani Faradonbeh
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  2. Danial Jahed Armaghani
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  3. Hassan Bakhshandeh Amnieh
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  4. Edy Tonnizam Mohamad
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Correspondence to Danial Jahed Armaghani.

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Faradonbeh, R.S., Armaghani, D.J., Amnieh, H.B. et al. Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput & Applic 29, 269–281 (2018). https://doi.org/10.1007/s00521-016-2537-8

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