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Combination of artificial neural networks and numerical modeling for predicting deformation modulus of rock masses

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The deformation modulus of the rock mass as a very important parameter in rock mechanic projects generally is determined by the plate load in-situ tests. While this test is very expensive and time-consuming, so in this study a new method is developed to combin artificial neural networks and numerical modeling for predicting deformation modulus of rock masses. For this aim, firstly, the plate load test was simulated using a Finite Difference numerical model that was verified with actual results of the plate load test in Pirtaghi dam galleries in Iran. Secondly, an artificial neural network is trained with a set of data resulted from numerical simulations to estimate the deformation modulus of the rock mass. The results showed that an ANN with five neurons in the input layer, three hidden layers with 4, 3 and 2 neurons, and one neuron in the output layer had the best accuracy for predicting the deformation modulus of the rock mass.
Rocznik
Strony
337--346
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
  • Toossab Consulting Engineers Company, Mashhad, Iran
autor
  • Isfahan University of Technology (IUT), Department of Mining Engineering, Isfahan- 84156-83111, Iran
  • Toossab Consulting Engineers Company, Mashhad, Iran
Bibliografia
  • [1] Alshkane Y.M.A., 2015. Numerical modelling investigation of rock mass behaviour under gravity dams. University of Nottingham.
  • [2] ASTM, D., “4394-08. 2008. Standard Test Method for Determining In Situ Modulus of Deformation of Rock Mass Using Rigid Plate Loading Method.”
  • [3] Bieniawski Z., 1978. Determining rock mass deformability: experience from case histories. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 15 (5), 237-247, DOI: 10.1016/0148-9062(78)90956-7.
  • [4] Bieniawski Z.T., 1989. Engineering rock mass classifications: a complete manual for engineers and geologists in mining, civil, and petroleum engineering, John Wiley & Sons.
  • [5] Cai J., Zhao J., 1997. Use of neural networks in rock tunneling. Proc Int Conf Compu Methods & Advances in Geome-chanics.
  • [6] Finley R., George J., Riggins M., 1999. Determination of Rock Mass Modulus Using the Plate Loading Method at Yucca Mountain, Nevada, Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US).
  • [7] Gholamnejad J., Bahaaddini H., Astegar M. R., 2013. Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods. J. Min. Env. 4 (1), 35-43, DOI: 10.22044/jme.2013.144.
  • [8] Gokceoglu C., Yesilnacar E., Sonmez H., Kayabasi A., 2004. A neuro-fuzzy model for modulus of deformation of jointed rock masses. Comput. Geotech. 31 (5), 375-383, DOI: 10.1016/j.compgeo.2004.05.001.
  • [9] Hoek E., Brown E.T., 1997. Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34(8), 1165-1186, DOI: 10.1016/S1365-1609(97)80069-X.
  • [10] Hoek E., Carranza-Torres C., Corkum B., 2002. Hoek-Brown failure criterion-2002 edition. Proc NARMS-Tac 1, 267-273.
  • [11] Hoek E., Diederichs M.S., 2006. Empirical estimation of rock mass modulus. Int. J. Rock Mech. Min. Sci. 43 (2), 203-215, DOI: 10.1016/j.ijrmms.2005.06.005.
  • [12] Javad G., Narges T., 2010. Application of artificial neural networks to the prediction of tunnel boring machine penetra-tion rate. Min. Sci. Tech. (China) 20 (5), 727-733, DOI: 10.1016/S1674-5264(09)60271-4.
  • [13] Kishan M., Chilukuri K.M., Sanjay R., 1997. Elements of artificial neural networks. MIT Press, USA.
  • [14] Meulenkamp F., Grima M.A., 1999. Application of neural networks for the prediction of the unconfined compres-sive strength (UCS) from Equotip hardness. Int. J. Rock Mech. Min. Sci. 36 (1), 29-39, DOI: 10.1016/S0148-9062(98)00173-9.
  • [15] Monjezi M., Ghafurikalajahi M., Bahrami A., 2011. Prediction of blast-induced ground vibration using artificial neural networks. Tunn. UnderGr. Sp. Tech. 26 (1), 46-50, DOI: 10.1016/j.tust.2010.05.002.
  • [16] Nicholson G., Bieniawski Z., 1990. A nonlinear deformation modulus based on rock mass classification. Int. J. Min. Geo. Eng. 8 (3), 181-202, DOI: 10.1007/BF01554041.
  • [17] Palmström A., Singh R., 2001. The deformation modulus of rock masses–comparisons between in situ tests and indirect estimates. Tunn. UnderGr. Sp. Tech. 16 (2), 115-131, DOI: 10.1016/S0886-7798(01)00038-4.
  • [18] Ravandi E.G., Rahmannejad R., Monfared A.E.F., Ravandi E.G., 2013. Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation. Int. J. Min. Sci. Tech. 23 (5), 733-737, DOI: 10.1016/j.ijmst.2013.08.018.
  • [19] Roy M., Singh P., 2004. Application of artificial neural network in mining industry. Ind. Min. Eng. J. 43 (7), 19-23.
  • [20] Serafim J.L., 1983. Consideration of the geomechanical classification of Bieniawski. Proc. Int. Symp. on Engineering Geology and Underground Construction.
  • [21] Shahin M.A., Jaksa M.B., Maier H.R., 2001. Artificial neural network applications in geotechnical engineering. Austra-lian geomechanics 36 (1), 49-62.
  • [22] Sonmez H., Gokceoglu C., Nefeslioglu H., 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 (2), 224-235, DOI: 10.1016/j.ijrmms.2005.06.007.
  • [23] Tajduś K., 2009. New method for determining the elastic parameters of rock mass layers in the region of underground mining influence. Int. J. Rock Mech. Min. Sci. 46 (8),: 1296-1305, DOI: 10.1016/j.ijrmms.2009.04.006.
  • [24] Tajduś K., 2010. Determination of approximate value of a GSI Index for the disturbed rock mass layers in the area of Polish coal mines. Arch. Min. Sci. 55 (4), 879-890.
  • [25] Yang Y., Zhang Q., 1997. Analysis for the results of point load testing with artificial neural network. Proc. Int. Conf. Compu. Methods & Advances in Geomechanics.
  • [26] Zhang L., H. Einstein, 2004. Using RQD to estimate the deformation modulus of rock masses. Int. J. Rock Mech. Min. Sci.41 (2), 337-341, DOI: 10.1016/S1365-1609(03)00100-X.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2c56064f-5e1f-48c9-949f-61760190b932
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