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Neural computing & applications, v.24 no.3/4, 2014년, pp.891 - 900
Erzin, Yusuf , Gul, T. Oktay
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Geotech Geoenviron Eng MA Shahin 128 785 2002 10.1061/(ASCE)1090-0241(2002)128:9(785) Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. Geotech Geoenviron Eng 128:785-793
Geotech Geoenviron Eng M Maugeri 124 595 1998 10.1061/(ASCE)1090-0241(1998)124:7(595) Maugeri M, Castelli F, Massimino MR, Verona G (1998) Observed and computed settlements of two shallow foundations on sand. Geotech Geoenviron Eng 124:595-605
DP Coduto 1994 Foundation design principles and practices Coduto DP (1994) Foundation design principles and practices. Prentice-Hall, Englewood Cliffs
GF Sowers 1970 Introductory soil mechanics and foundations: geo-technical engineering Sowers GF (1970) Introductory soil mechanics and foundations: geo-technical engineering. Macmillan, New York
K Terzaghi 1996 Soil mechanics in engineering practice 3 Terzaghi K, Peck RD, Mesri G (1996) Soil mechanics in engineering practice, 3rd edn. Wiley, New York
J Soil Mech Found Div ASCE JH Schmertmann 96 1032 1970 Schmertmann JH (1970) Static cone to compute static settlement over sand. J Soil Mech Found Div ASCE 96:1032-1043
Rock Mech Rock Eng I Yilmaz 41 5 781 2008 10.1007/s00603-007-0138-7 Yilmaz I, Yüksek AG (2008) An example of artificial neural network application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781-795
Int J Rock Mech Min I Yilmaz 46 4 803 2009 10.1016/j.ijrmms.2008.09.002 Yilmaz I, Yüksek AG (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison. Int J Rock Mech Min 46(4):803-810
Energy Educ Sci Tech Part A O Kaynar 26 221 2011 Kaynar O, Yilmaz I, Demirkoparan F (2011) Forecasting of natural gas consumption with neural network and neuro fuzzy system. Energy Educ Sci Tech Part A 26:221-238
Expert Syst Appl I Yilmaz 38 5 5958 2011 10.1016/j.eswa.2010.11.027 Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958-5966
Neural Comput Appl I Yilmaz 21 5 957 2012 10.1007/s00521-011-0535-4 Yilmaz I, Marschalko M, Bednarik M, Kaynar O, Fojtova L (2012) Neural computing models for prediction of permeability coefficient of coarse grained soils. Neural Comput Appl 21(5):957-968
Sci Iran Y Erzin 19 2 188 2012 10.1016/j.scient.2012.02.008 Erzin Y, Cetin T (2012) The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Sci Iran 19(2):188-194
10.1016/j.cageo.2012.09.003 Erzin Y, Cetin T (2012) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci. doi: 10.1016/j.cageo.2012.09.003
Arab J Sci Eng AJ Choobbasti 2 311 2009 Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (a case study: Noabad, Mazandaran, Iran). Arab J Sci Eng 2:311-319
Aust Civil Eng Trans N Sivakugan CE40 49 1998 Sivakugan N, Eckersley JD, Li H (1998) Settlement predictions using neural networks. Aust Civil Eng Trans CE40:49-52
DE Rumelhart 318 1986 10.7551/mitpress/5236.001.0001 Parallel distributed processing: explorations in the microstructure of cognition Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318-362
Gul TO (2011) The use of neural networks for the prediction of the settlement of pad and one- way strip footings on cohesionless soils based on standard penetration test. MSc thesis, Celal Bayar University, Manisa (in Turkish)
J Soil Mech Found Eng Div ASCE GG Meyerhof 91 21 1965 10.1061/JSFEAQ.0000719 Meyerhof GG (1965) Shallow foundations. J Soil Mech Found Eng Div ASCE 91:21-31
K Terzaghi 1967 Soil mechanics in foundation engineering practice Terzaghi K, Peck RD (1967) Soil mechanics in foundation engineering practice. Wiley, New York
Parry RHG (1971) A direct method of estimating settlements in sands from standard penetration tests. In: Proceedings of symposium on interaction of structure and foundations, Midland Soil Mechanics and Foundation Engineering Society, Birmingham, pp 29-37
RB Peck 1974 Foundation engineering Peck RB, Hanson WE, Thornburn TH (1974) Foundation engineering. Wiley, NY
Proc Inst Civil Eng JB Burland 78 1325 1985 10.1680/iicep.1985.1058 Burland JB, Burbidge MC (1985) Settlement of foundations on sand and gravel. Proc Inst Civil Eng 78:1325-1381
Burbidge MC (1982) A case study review of settlements on granular soil. MSc thesis, Imperial College of Science and Technology, University of London, London
Aust Geomech MA Shahin 36 49 2001 Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36:49-62
J Comput Civil Eng I Flood 8 131 1994 10.1061/(ASCE)0887-3801(1994)8:2(131) Flood I, Kartam N (1994) Neural network in civil engineering. I: principles and understanding. J Comput Civil Eng 8:131-148
M Twomey 44 1997 Artificial neural networks for civil engineers: fundamentals and applications Twomey M, Smith AE (1997) Validation and verification. In: Kartam N, Flood I, Garrett JH (eds) Artificial neural networks for civil engineers: fundamentals and applications. ASCE, New York, pp 44-64
J R Stat Soc B Methodol M Stone 36 111 1974 10.1111/j.2517-6161.1974.tb00994.x Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B Methodol 36:111-147
M Smith 1993 Neural networks for modeling Smith M (1993) Neural networks for modeling. Van Nostrand Reinhold, New York
J Comput Civil Eng MA Shahin 18 105 2004 10.1061/(ASCE)0887-3801(2004)18:2(105) Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng 18:105-114
H Demuth 2006 Neural network toolbox user’s guide Demuth H, Beale M, Hagan M (2006) Neural network toolbox user’s guide. The Math Works, Inc., Natick
Neural Netw K Hornik 2 359 1989 10.1016/0893-6080(89)90020-8 Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359-366
Can Geotech J Y Erzin 44 1215 2007 10.1139/T07-052 Erzin Y (2007) Artificial neural networks approach for swell pressure versus soil suction behavior. Can Geotech J 44:1215-1223
Int J Therm Sci Y Erzin 47 1347 2008 10.1016/j.ijthermalsci.2007.11.001 Erzin Y, Rao BH, Singh DN (2008) Artificial neural networks for predicting soil thermal resistivity. Int J Therm Sci 47:1347-1358
Can Geotech J Y Erzin 46 955 2009 10.1139/T09-035 Erzin Y, Gumaste SD, Gupta AK, Singh DN (2009) ANN models for determining hydraulic conductivity of compacted fine grained soils. Can Geotech J 46:955-968
Int J Therm Sci Y Erzin 49 118 2010 10.1016/j.ijthermalsci.2009.06.008 Erzin Y, Rao BH, Patel A, Gumaste SD, Gupta AK, Singh DN (2010) Artificial neural network models for predicting of electrical resistivity of soils from their thermal resistivity. Int J Therm Sci 49:118-130
Math Comput Appl Y Erzin 16 425 2011 Erzin Y, Gunes N (2011) The prediction of swell percent and swell pressure by using neural networks. Math Comput Appl 16:425-436
Kanibir A, Ulusay R, Aydan Ö (2006) Liquefaction-induced ground deformations on a lake shore (Turkey) and empirical equations for their prediction. IAEG 2006, paper 362
Bull Eng Geol Environ Y Erzin 71 529 2012 10.1007/s10064-012-0424-9 Erzin Y, Patel A, Singh DN, Tiga MG, Yılmaz I, Srinivas K (2012) Investigations on factors influencing the crushing strength of some Aegean sands. Bull Eng Geol Environ 71:529-536
AI Expert GD Garson 6 47 1991 Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6:47-51
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