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Novel approach to predicting blast-induced ground vibration using Gaussian process regression

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Abstract

An attempt has been made to propose a novel prediction model based on the Gaussian process regression (GPR) approach. The proposed GPR was used to predict blast-induced ground vibration using 210 blasting events from an open pit mine in Ghana. Out of the 210 blasting data, 130 were used in the model development (training), whereas the remaining 80 were used to independently assess the performance of the GPR model. The formulated GPR model was compared with the other standard predictive techniques such as the generalised regression neural network, radial basis function neural network, back-propagation neural network, and four conventional ground vibration predictors (United State Bureau of Mines model, Langefors and Kihlstrom model, Ambraseys–Hendron model, and Indian Standard model). Comparatively, the statistical results revealed that the proposed GPR approach can predict ground vibration more accurately than the standard techniques presented in this study. The GPR had the highest correlation coefficient (R), variance accounted for, and the lowest values of the statistical error indicators (mean absolute error and root-mean-square error) applied. The superiority of GPR to the other methods is explained by the ability of the GPR to quantitatively model the noise patterns in the blasting data events adequately. The study will serve as a foundation for future research works in the mining industry where artificial intelligence technology is yet to be fully explored.

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References

  1. Gokhale BV (2011) Rotary drilling and blasting in large surface mines. CRC Press/Balkema, Leiden

    Google Scholar 

  2. Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

    Google Scholar 

  3. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643

    Google Scholar 

  4. 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 

  5. 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 

  6. Hasanipanah M, Amnieh HB, 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

    Article  Google Scholar 

  7. Dogan O, Anil Ö, Akbas SO, Kantar E, Tuğrul Erdem R (2013) Evaluation of blast-induced ground vibration effects in a new residential zone. Soil Dyn Earthq Eng 50:168–181

    Google Scholar 

  8. Faramarzi F, Ebrahimi Farsangi MA, Mansouri H (2014) Simultaneous investigation of blast induced ground vibration and airblast effects on safety level of structures and human in surface blasting. Int J Min Sci Technol 24:663–669

    Google Scholar 

  9. Richards AB, Moore AJ (2012) Blast vibration course: measurement, assessment, control. Terrock Consulting Engineers (Terrock Pty Ltd), Australia

    Google Scholar 

  10. Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. USBM Rep Investig 548:21

    Google Scholar 

  11. Langefors U, Kilstrom B (1963) The modern technique of rock blasting. Wiley, New York

    Google Scholar 

  12. Davies B, Farmer IW, Attewell PB (1964) Ground vibration from shallow sub-surface blasts. Engineer 217:553–559

    Google Scholar 

  13. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses: rock mechanics in engineering practices. In: Stagg K, Wiley J (eds) Rock mechanics in engineering practices. Wiley, London, pp 203–207

    Google Scholar 

  14. Bureau of Indian Standards (1973) Criteria for safety and design of structures subject to underground blasts, ISI Bulletin, IS-6922

  15. Ghosh A, Daemen JK (1983) A simple new blast vibration predictor based on wave propagation laws. In: The 24th US symposium on rock mechanics (USRMS)

  16. Gupta RN, Roy PP, Singh B (1987) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference on safety in Mines Research Institute, Beijing, China, pp 1015–1021

  17. Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165

    Google Scholar 

  18. Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11:178–184

    Google Scholar 

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

    Google Scholar 

  20. Ghasemi E, Ataei M, Hashemolhosseini H (2012) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19:755–770

    Google Scholar 

  21. Hasanipanah M, Amnieh HB, Khamesi H, Armaghani DJ, Golzar SB, Shahnazar A (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

    Article  Google Scholar 

  22. Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32:631–644

    Google Scholar 

  23. Fouladgar N, Hasanipanah M, Amnieh HB (2017) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput 33(2):181–189

    Google Scholar 

  24. 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

    Google Scholar 

  25. Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015

    Google Scholar 

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

    Google Scholar 

  27. Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machines. Eng Comput 27:193–200

    Google Scholar 

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

    Google Scholar 

  29. 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 22:1637–1643

    Google Scholar 

  30. Ghoraba S, Monjezi M, Talebi N, Jahed Armaghani D, Moghaddam MR (2016) Estimation of ground vibration produced by blasting operations through intelligent and empirical models. Environ Earth Sci 75:1137

    Google Scholar 

  31. Taheri K, Hasanipanah M, Golzar SB, 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 

  32. Hasanipanah M, Noorian–Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO–ANN model for predicting surface settlement caused by tunnelling. Eng Comput 32(4):705–715

    Google Scholar 

  33. Mojtahedi S, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2017) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 33(2):307–316

    Google Scholar 

  34. Mokfi T, Shahnazar A, Bakhshayeshi I, Derakhsh AM, Tabrizi O (2018) Proposing of 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

    Article  Google Scholar 

  35. 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 

  36. Jiang W, Arslan CA, Tehrani MS, Khorami M, Hasanipanah M (2018) Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system. Eng Comput. https://doi.org/10.1007/s00366-018-0659-6

    Article  Google Scholar 

  37. Zadeh LZ (1993) Fuzzy logic, neural networks and soft computing”. Microprocess Microprogramming 38:13

    Google Scholar 

  38. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725

    Google Scholar 

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

    Google Scholar 

  40. Amnieh BH, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in sarcheshmeh copper mine by neural network. Saf Sci 38:319–325

    Google Scholar 

  41. 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 30:1233–1236

    Google Scholar 

  42. Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50

    Google Scholar 

  43. Xue X, Yang X (2013) Predicting blast-induced ground vibration using general regression neural network. J Vib Control 20:1512–1519

    Google Scholar 

  44. Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar Iron Ore Mine, Iran. J Rock Mech Geotech Eng 6:67–76

    Google Scholar 

  45. Álvarez-Vigil AE, González-Nicieza C, López Gayarre F, Álvarez-Fernández MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural networks. Int J Rock Mech Min Sci 55:108–116

    Google Scholar 

  46. Görgülü K, Arpaz E, Demirci A, Koçaslan A, Dilmaç MK, Yüksek AG (2013) Investigation of blast-induced ground vibrations in the Tülü Boron Open Pit Mine. Bull Eng Geol Environ 72:555–564

    Google Scholar 

  47. Lapčević R, Kostić S, Pantović R, Vasović N (2014) Prediction of blast-induced ground motion in a copper mine. Int J Rock Mech Min Sci 69:19–25

    Google Scholar 

  48. Görgülü K, Arpaz E, Uysa Ö, Durutürk AG, Yüksek AG, Koçaslan A, Dilmaç MK (2015) Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arab J Geosci 8:4269–4278

    Google Scholar 

  49. Shahri AA, Asheghi A (2018) Optimized developed artificial neural network-based models to predict the blast-induced ground vibration. Innov Infrastruct Solut 3:34

    Google Scholar 

  50. Iramina WS, Sansone EC, Wichers M, Wahyudi S, Eston SM, Shimada H, Sasaoka T (2018) Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships. REM Int Eng J 71:89–95

    Google Scholar 

  51. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  52. Huang Y (2009) Advances in artificial neural networks—methodological development and application. Algorithms 2:973–1007

    MathSciNet  MATH  Google Scholar 

  53. Adeel A, Larijani H, Javed A, Ahmadinia A (2015) Impact of learning algorithms on random neural network based optimization for LTE-UL systems. Netw Protoc Algorithms 7:157–178

    Google Scholar 

  54. Samui P (2014) Utilization of Gaussian process regression for determination of soil electrical resistivity. Geotech Geol Eng 32:191–195

    Google Scholar 

  55. Liu J, Yan K, Zhao X, Hu Y (2016) Prediction of autogenous shrinkage of concretes by support vector machine. Int J Pavement Res Technol 9:169–177

    Google Scholar 

  56. Ažman K, Kocijan J (2007) Application of Gaussian processes for black-box modelling of biosystems. ISA Trans 46:443–457

    Google Scholar 

  57. Samui P, Jagan J (2013) Determination of effective stress parameter of unsaturated soils: a Gaussian process regression approach. Front Struct Civ Eng 7:133–136

    Google Scholar 

  58. Bin S, Wenlai Y (2013) Application of Gaussian process regression to prediction of thermal comfort index. In: 11th IEEE international conference on electronic measurement & instruments, Harbin, China, pp 958–961

  59. Dong D (2012) Mine gas emission prediction based on Gaussian process model. Procedia Eng 45:334–338

    Google Scholar 

  60. Kong D, Chen Y, Li N (2018) Gaussian process regression for tool wear prediction. Mech Syst Signal Process 104:556–574

    Google Scholar 

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

    Google Scholar 

  62. Appianing EJA (2013) A review of waste dump reclamation practices at GMC Nsuta. Dissertation, University of Mines and Technology, Tarkwa

  63. Apalangya PA (2014) An assessment of the blast practices at Pit C of Ghana Manganese Company Limited. Dissertation, University of Mines and Technology, Tarkwa

  64. Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design. Pws Pub, Boston

    Google Scholar 

  65. Yegnanarayana B (2005) Artificial neural networks. Prentice-Hall of India Private Limited, New Delhi

    Google Scholar 

  66. Ghosh J, Nag A (2001) An overview of radial basis function networks. In: Howlett RJ, Jain LC (eds) Radial basis function network 2, 1st edn. Springer, Berlin, pp 1–36

    Google Scholar 

  67. Specht DF (1991) A General regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Google Scholar 

  68. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press, Cambridge

    MATH  Google Scholar 

  69. Li JJ, Jutzeler A, Faltings B (2014) Estimating urban ultrafine particle distributions with Gaussian process models. In: Winter S, Rizos C (eds) Proceedings of Research@Locate’14, Canberra, Australia, 7th–9th April, 2014, pp 145–153

  70. Kang F, Han S, Salgado R, Li J (2015) System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin Hypercube sampling. Comput Geotech 63:13–25

    Google Scholar 

  71. Snelson EL (2007) Flexible and efficient Gaussian process models for machine learning. Dissertation, University of London

  72. Moore CJ, Chua AJK, Berry CPL, Gair JR (2016) Fast methods for training Gaussian process on large datasets. R Soc Open Sci 3:1–10

    Google Scholar 

  73. Mueller AV, Hemond HF (2013) Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels. Talanta 117:112–118

    Google Scholar 

  74. Savaux V, Bader F (2015) Mean square error analysis and linear minimum mean square error application for preamble-based channel estimation in orthogonal frequency division multiplexing/offset quadrature amplitude modulation systems. IET Commun 9(14):1763–1773

    Google Scholar 

  75. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250

    Google Scholar 

  76. Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36:41–59

    Google Scholar 

  77. 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 

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

    Google Scholar 

  79. 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 28:1043–1050. https://doi.org/10.1007/s00521-016-2434-1

    Article  Google Scholar 

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

    MATH  Google Scholar 

  81. Moré JJ (1978) The Levenberg–Marquardt algorithm: implementation and theory. In: Watson GA (ed) Numerical analysis, vol 630. Lecture notes in mathematics. Springer, Berlin, Heidelberg, pp 105–116

    Google Scholar 

  82. Ruder S (2016) An overview of gradient descent optimisation algorithms. arXiv preprint http://arxiv.org/abs/1609.04747

  83. Mohammadnejad M, Gholami R, Ramezanzadeh A, Jalali ME (2011) Prediction of blast-induced vibrations in limestone quarries using support vector machine. J Vib Control 18(9):1322–1329

    Google Scholar 

  84. Ragam P, Nimaje DS (2018) Assessment of blast-induced ground vibration using different predictor approaches—a comparison. Chem Eng Trans 66:487–492

    Google Scholar 

  85. Armaghani DJ, 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

    Google Scholar 

  86. Tiile RN (2016) Artificial neural network approach to predict blastinduced ground vibration, airblast and rock fragmentation. Dissertation, Missouri University of Science and Technology

  87. Delis I, Panzeri S, Pozzo T, Berret B (2013) A unifying model of concurrent spatial and temporal modularity in muscle activity. J Neurophysiol 111:675–693

    Google Scholar 

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Acknowledgements

The authors would like to thank the Ghana National Petroleum Corporation (GNPC) for providing funding to support this work through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology (UMaT), Ghana.

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  1. Department of Mining Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Western Region, Ghana

    Clement Kweku Arthur & Victor Amoako Temeng

  2. Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Western Region, Ghana

    Yao Yevenyo Ziggah

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  1. Clement Kweku Arthur
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Correspondence to Victor Amoako Temeng.

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Arthur, C.K., Temeng, V.A. & Ziggah, Y.Y. Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers 36, 29–42 (2020). https://doi.org/10.1007/s00366-018-0686-3

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