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Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques

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Abstract

Ground vibration (GV) is a blasting consequence and is an important parameter to control in mining and civil projects. Previous GV predictor models have mainly been developed considering two factors; charge per delay and distance from the blast-face. However, mostly the presence of the water as an influential factor has been neglected. In this paper, an attempt has been made to modify United State Bureau of Mines model (USBM) by incorporating the effect of water. For this purpose, 35 blasting operations were investigated in Chadormalu iron mine, Iran and required blasting parameters were recorded in each blasting operation. Eventually, a coefficient was calculated and added in USBM model for effect of water. To demonstrate the capability of the suggested equation, several empirical models were also used to predict measured values of PPV. Results showed that the modified USBM model can perform better compared to previous models. By establishing new parameter in the USBM model, a new predictive model based on gene expression programming (GEP) was utilized and developed to predict GV. To show capability of GEP model in estimating GV, linear multiple regression (LMR) and non-linear multiple regression (NLMR) techniques were also performed and developed using the same datasets. The results demonstrated that the newly proposed model is able to predict blast-induced GV more accurately than other developed techniques.

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References

  1. Singh SP, Xavier P (2005) Causes, impact and control of overbreak in underground excavations. Tunn Undergr Space Technol 20(1):63–71

    Article  Google Scholar 

  2. Zhu Z, Mohanty B, Xie H (2007) Numerical investigation of blasting-induced crack initiation and propagation in rocks. Int J Rock Mech Min Sci 44(3):412–424

    Article  Google Scholar 

  3. 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 

  4. Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  5. Jahed Armaghani D, Hajihassani M, Tonnizam Mohamad E, 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 7:5383–5396

    Article  Google Scholar 

  6. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637–2643

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4:427–433

    Article  Google Scholar 

  9. 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 Sci 52:163–170

    Article  Google Scholar 

  10. 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 

  11. 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 

  12. 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 

  13. Jahed Armaghani D, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187(11):1–13

    Article  Google Scholar 

  14. Duvall WI, Fogelson DE (1962). Review of criteria for estimating damage to residences from blasting vibration. US Bureau of Mines R.I. 5968

  15. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Article  Google Scholar 

  16. Kuzu C (2008) The importance of site-specific characters in prediction models for blast-induced ground vibrations. Soil Dyn Earthq Eng 28(5):405–414

    Article  Google Scholar 

  17. Monjezi M, Ahmadi M, Sheikhan M, Bahrami A, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dyn Earthq Eng 30(11):1233–1236

    Article  Google Scholar 

  18. Dehghani H, Ataee-pour M (2011) Development of a model to predict peak particle velocity in a blasting operation. Int J Rock Mech Min Sci 48:51–58

    Article  Google Scholar 

  19. Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23:249–262

    Article  Google Scholar 

  20. Jahed Armaghani D, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860

    Article  Google Scholar 

  21. Bureau of Indian Standard Criteria for safety and design of structures subjected to underground blast. ISI Bull IS-6922, 1973

  22. New BM (1986) Ground vibration caused by civil engineering works. Transport and road research laboratory research report, 53, 19

  23. Basu D, Sen M (2005) Blast induced ground vibration norms—a critical review. National seminar on policies. Statutes and Legislation in Mines, Kharagpur, pp 112–113

    Google Scholar 

  24. Crandell FJ (1949) Ground vibrations due to blasting and its effects upon structures. J. Boston Soc Civ Eng 30:222–245

    Google Scholar 

  25. Blair BE, Duvall WI (1954) Evaluation of gauges for measuring displacement, velocity and acceleration of seismic pulses. USBM RT 5073:21

    Google Scholar 

  26. Ambraseys NR, Hendron AJ (1968) Dynamic behaviour of rock mass. John Wiley and Sons, London, pp 203–207

    Google Scholar 

  27. Langefors U, Kihlstrom B (1973) The modern technique of rock blasting, seconded. Wiley Publisher, New York

    Google Scholar 

  28. Ghosh A, Daeman JK (1983) A simple new blast vibration predictor based on wave propagation laws, The 24th U.S. Symposium on Rock Mechanics (USRMS)

  29. Singh PK, Vogt W, Singh RB (1996) Blasting side effects Investigations in an open cast coal mine in India. Int J Mini Reclamat Environ 10:155–159

    Article  Google Scholar 

  30. Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. USBM Rep Investig 5483:21

    Google Scholar 

  31. Hajihassani M (2014) Jahed Armaghani D, Marto A, Tonnizam Mohamad E. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-014-0657-x

    Google Scholar 

  32. 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 75:289–297

    Article  Google Scholar 

  33. Hagan TN, Kennedy BJ (1977) A practical approach to the reduction of blasting nuisance from surface operation. Aust Min 69:36–46

    Google Scholar 

  34. Mather W (1984) Factors affecting magnitude and frequency of blast-induced ground and air vibrations. Inst Min Metall Trans 93:173–180

    Google Scholar 

  35. Blair DP, Jiang JJ (1995) Surface vibrations due to a vertical column of explosive. Int J Rock Mech Min Sci 32:149–154

    Article  Google Scholar 

  36. Roy PP (1998) Characteristics of ground vibrations and structural response to surface and underground blasting. Geotech Geol Eng 16:151–166

    Article  Google Scholar 

  37. Blair DP (1999) Statistical models for ground vibration and airblast. Fragblast 3:335–364

    Article  Google Scholar 

  38. Hakan AK, Adnan K (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dyn Earthq Eng 28(9):686–694

    Article  Google Scholar 

  39. McDowell PW (2002) Geophysics in Engineering Investigations (CIRIA), Construction Industry Research

  40. Singh TN, Verma AK (2010) Sensitivity of total charge and maximum charge per delay on ground vibration. Geomat Nat Hazards Risk 1(3):259–272

    Article  Google Scholar 

  41. 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(2):81–94

    MathSciNet  Google Scholar 

  42. 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 

  43. Tonnizam Mohamad E, Noorani SA, Jahed Armaghani D, Saad R (2012) Simulation of blasting induced ground vibration by using artificial neural network. Electron J Geotech Eng 17:2571–2584

    Google Scholar 

  44. Momeni E, Nazir R, Jahed Armaghani D, Sohaie H (2015) Bearing capacity of precast thin-walled foundation in sand. Geotech Eng. doi:10.1680/jgeen.14.00177

    Google Scholar 

  45. Khandelwal M, Singh TN (2010) Artificial neural networks as a valuable tool for well log interpretation. Pet Sci Technol 28(14):1381–1393

    Article  Google Scholar 

  46. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

    Article  Google Scholar 

  47. Sakellariou M, Ferentinou M (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 24(3):419–445

    Article  Google Scholar 

  48. Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neurofuzzy inference system. Environ Geol 56:97–107

    Article  Google Scholar 

  49. Fisne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174:461–470

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Alkroosh I, Nikraz H (2011) Correlation of pile axial capacity and CPT data using gene expression programming. Geotech Geol Eng 29(5):725–748

    Article  Google Scholar 

  52. Mollahasani A, Alavi AH, Gandomi AH (2011) Empirical modeling of plate load test moduli of soil via gene expression programming. Comput Geotech 38(2):281–286

    Article  Google Scholar 

  53. 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 

  54. Wyllie DC, Mah CW (2004) Rock Slope Engineering Civil and Mining, 4th edn. Taylor & Francis, Abingdon

    Google Scholar 

  55. Zhang GJ (1996) A study of free toe-space explosive loading and its application in open pit blasts, Fragblast5, Montreal, Canada

  56. Mandal SK (2007) An investigation on the effect of geo–technical and blast design parameters on smooth blasting, PhD thesis. Department of Mining Engineering Bengal Engineering and Science University, Shibpur

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

    MathSciNet  MATH  Google Scholar 

  58. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence, 2nd edn. Springer-Verlag, Germany, p 478

    MATH  Google Scholar 

  59. 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 

  60. Steeb W-H (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 

  61. 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 

  62. Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston

    MATH  Google Scholar 

  63. 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 

  64. Yassin MA, Alazba A, Mattar MA (2016) Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agric Water Manag 163:110–124

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  67. 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 

  68. GEPSOFT. GeneXproTools. Version 4.0 (2006) http://www.gepsoft.com/

  69. Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

    Article  Google Scholar 

  70. 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 Intel 22(4):808–814

    Article  Google Scholar 

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Acknowledgments

The cooperation of Mr. Nikravan, director of the Asphalttous Company, in providing the required information of blasting operation of Chadormalu iron mine is highly appreciated.

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Authors and Affiliations

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

    M. Monjezi, M. Baghestani & R. Shirani Faradonbeh

  2. Mining and Metallurgical Department, Yazd University, Yazd, Iran

    M. Pourghasemi Saghand

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

    D. Jahed Armaghani

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  1. M. Monjezi
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  4. M. Pourghasemi Saghand
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Correspondence to M. Monjezi.

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Monjezi, M., Baghestani, M., Shirani Faradonbeh, R. et al. Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Engineering with Computers 32, 717–728 (2016). https://doi.org/10.1007/s00366-016-0448-z

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