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

  • Clement Kweku Arthur
  • Victor Amoako TemengEmail author
  • Yao Yevenyo Ziggah
Original Article
  • 45 Downloads

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.

Keywords

Gaussian process regression Artificial neural network Ground vibration empirical predictors Blasting 

Notes

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.

References

  1. 1.
    Gokhale BV (2011) Rotary drilling and blasting in large surface mines. CRC Press/Balkema, LeidenGoogle Scholar
  2. 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 75:289–297CrossRefGoogle Scholar
  3. 3.
    Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643CrossRefGoogle Scholar
  4. 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):27CrossRefGoogle Scholar
  5. 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):23–31CrossRefGoogle Scholar
  6. 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 CrossRefGoogle Scholar
  7. 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 50:168–181CrossRefGoogle Scholar
  8. 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 24:663–669CrossRefGoogle Scholar
  9. 9.
    Richards AB, Moore AJ (2012) Blast vibration course: measurement, assessment, control. Terrock Consulting Engineers (Terrock Pty Ltd), AustraliaGoogle Scholar
  10. 10.
    Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. USBM Rep Investig 548:21Google Scholar
  11. 11.
    Langefors U, Kilstrom B (1963) The modern technique of rock blasting. Wiley, New YorkGoogle Scholar
  12. 12.
    Davies B, Farmer IW, Attewell PB (1964) Ground vibration from shallow sub-surface blasts. Engineer 217:553–559Google Scholar
  13. 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 203–207Google Scholar
  14. 14.
    Bureau of Indian Standards (1973) Criteria for safety and design of structures subject to underground blasts, ISI Bulletin, IS-6922Google Scholar
  15. 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)Google Scholar
  16. 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 1015–1021Google Scholar
  17. 17.
    Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165CrossRefGoogle Scholar
  18. 18.
    Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11:178–184Google Scholar
  19. 19.
    Khandewal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222CrossRefGoogle Scholar
  20. 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 19:755–770CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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 32:631–644CrossRefGoogle Scholar
  23. 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):181–189CrossRefGoogle Scholar
  24. 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 48:51–58CrossRefGoogle Scholar
  25. 25.
    Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015CrossRefGoogle Scholar
  26. 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 56:97–107CrossRefGoogle Scholar
  27. 27.
    Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machines. Eng Comput 27:193–200CrossRefGoogle Scholar
  28. 28.
    Fişne A, Kuzu C, Hüdaverdi T (2011) prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174:461–470CrossRefGoogle Scholar
  29. 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 22:1637–1643CrossRefGoogle Scholar
  30. 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 75:1137CrossRefGoogle Scholar
  31. 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):689–700CrossRefGoogle Scholar
  32. 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):705–715CrossRefGoogle Scholar
  33. 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):307–316CrossRefGoogle Scholar
  34. 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 CrossRefGoogle Scholar
  35. 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):307–316CrossRefGoogle Scholar
  36. 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 CrossRefGoogle Scholar
  37. 37.
    Zadeh LZ (1993) Fuzzy logic, neural networks and soft computing”. Microprocess Microprogramming 38:13CrossRefGoogle Scholar
  38. 38.
    Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725CrossRefGoogle Scholar
  39. 39.
    Khandewal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125CrossRefGoogle Scholar
  40. 40.
    Amnieh BH, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in sarcheshmeh copper mine by neural network. Saf Sci 38:319–325CrossRefGoogle Scholar
  41. 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 30:1233–1236CrossRefGoogle Scholar
  42. 42.
    Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50CrossRefGoogle Scholar
  43. 43.
    Xue X, Yang X (2013) Predicting blast-induced ground vibration using general regression neural network. J Vib Control 20:1512–1519CrossRefGoogle Scholar
  44. 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 6:67–76CrossRefGoogle Scholar
  45. 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 55:108–116CrossRefGoogle Scholar
  46. 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 72:555–564CrossRefGoogle Scholar
  47. 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 69:19–25CrossRefGoogle Scholar
  48. 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 8:4269–4278CrossRefGoogle Scholar
  49. 49.
    Shahri AA, Asheghi A (2018) Optimized developed artificial neural network-based models to predict the blast-induced ground vibration. Innov Infrastruct Solut 3:34CrossRefGoogle Scholar
  50. 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 71:89–95CrossRefGoogle Scholar
  51. 51.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  52. 52.
    Huang Y (2009) Advances in artificial neural networks—methodological development and application. Algorithms 2:973–1007MathSciNetCrossRefGoogle Scholar
  53. 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 7:157–178CrossRefGoogle Scholar
  54. 54.
    Samui P (2014) Utilization of Gaussian process regression for determination of soil electrical resistivity. Geotech Geol Eng 32:191–195CrossRefGoogle Scholar
  55. 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 9:169–177CrossRefGoogle Scholar
  56. 56.
    Ažman K, Kocijan J (2007) Application of Gaussian processes for black-box modelling of biosystems. ISA Trans 46:443–457CrossRefGoogle Scholar
  57. 57.
    Samui P, Jagan J (2013) Determination of effective stress parameter of unsaturated soils: a Gaussian process regression approach. Front Struct Civ Eng 7:133–136CrossRefGoogle Scholar
  58. 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 958–961Google Scholar
  59. 59.
    Dong D (2012) Mine gas emission prediction based on Gaussian process model. Procedia Eng 45:334–338CrossRefGoogle Scholar
  60. 60.
    Kong D, Chen Y, Li N (2018) Gaussian process regression for tool wear prediction. Mech Syst Signal Process 104:556–574CrossRefGoogle Scholar
  61. 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 34:339–345CrossRefGoogle Scholar
  62. 62.
    Appianing EJA (2013) A review of waste dump reclamation practices at GMC Nsuta. Dissertation, University of Mines and Technology, TarkwaGoogle Scholar
  63. 63.
    Apalangya PA (2014) An assessment of the blast practices at Pit C of Ghana Manganese Company Limited. Dissertation, University of Mines and Technology, TarkwaGoogle Scholar
  64. 64.
    Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design. Pws Pub, BostonGoogle Scholar
  65. 65.
    Yegnanarayana B (2005) Artificial neural networks. Prentice-Hall of India Private Limited, New DelhiGoogle Scholar
  66. 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 1–36Google Scholar
  67. 67.
    Specht DF (1991) A General regression neural network. IEEE Trans Neural Netw 2(6):568–576CrossRefGoogle Scholar
  68. 68.
    Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press, CambridgezbMATHGoogle Scholar
  69. 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 145–153Google Scholar
  70. 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 63:13–25CrossRefGoogle Scholar
  71. 71.
    Snelson EL (2007) Flexible and efficient Gaussian process models for machine learning. Dissertation, University of LondonGoogle Scholar
  72. 72.
    Moore CJ, Chua AJK, Berry CPL, Gair JR (2016) Fast methods for training Gaussian process on large datasets. R Soc Open Sci 3:1–10CrossRefGoogle Scholar
  73. 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 117:112–118CrossRefGoogle Scholar
  74. 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):1763–1773CrossRefGoogle Scholar
  75. 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 7:1247–1250CrossRefGoogle Scholar
  76. 76.
    Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36:41–59CrossRefGoogle Scholar
  77. 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):441–455CrossRefGoogle Scholar
  78. 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):173–179CrossRefGoogle Scholar
  79. 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 CrossRefGoogle Scholar
  80. 80.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366CrossRefGoogle Scholar
  81. 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 105–116CrossRefGoogle Scholar
  82. 82.
    Ruder S (2016) An overview of gradient descent optimisation algorithms. arXiv preprint http://arxiv.org/abs/1609.04747
  83. 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):1322–1329CrossRefGoogle Scholar
  84. 84.
    Ragam P, Nimaje DS (2018) Assessment of blast-induced ground vibration using different predictor approaches—a comparison. Chem Eng Trans 66:487–492Google Scholar
  85. 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):5383–5396CrossRefGoogle Scholar
  86. 86.
    Tiile RN (2016) Artificial neural network approach to predict blastinduced ground vibration, airblast and rock fragmentation. Dissertation, Missouri University of Science and TechnologyGoogle Scholar
  87. 87.
    Delis I, Panzeri S, Pozzo T, Berret B (2013) A unifying model of concurrent spatial and temporal modularity in muscle activity. J Neurophysiol 111:675–693CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mining Engineering, Faculty of Mineral Resources TechnologyUniversity of Mines and TechnologyTarkwaGhana
  2. 2.Department of Geomatic Engineering, Faculty of Mineral Resources TechnologyUniversity of Mines and TechnologyTarkwaGhana

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