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Volumn 53, Issue 4, 2013, Pages 607-615

Pipe pile setup: Database and prediction model using artificial neural network

Author keywords

Artificial neural networks; Pile foundation; Pile setup

Indexed keywords

DEEP NEURAL NETWORKS; FORECASTING; GEOTECHNICAL ENGINEERING; NEURAL NETWORKS; PILE FOUNDATIONS;

EID: 84889588129     PISSN: 00380806     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sandf.2013.06.011     Document Type: Article
Times cited : (56)

References (44)
  • 4
    • 0028977232 scopus 로고
    • Neural network: An alternative to pile driving formulas
    • Chan, W., Chow, Y. K., 1995. Neural network: an alternative to pile driving formulas. Journal of Computer and Geotechnics 17(2), 135-156.
    • (1995) Journal of Computer and Geotechnics , vol.17 , Issue.2 , pp. 135-156
    • Chan, W.1    Chow, Y.K.2
  • 7
    • 0036168112 scopus 로고    scopus 로고
    • High capacity pipe piles at sanfrancisco international airport
    • Geotechnical Special Publication, ASCE
    • Dover, A., Howard, R. 2002. High Capacity Pipe Piles at Sanfrancisco International Airport. Deep Foundations Congress, Geotechnical Special Publication, ASCE: 655-667.
    • (2002) Deep Foundations Congress , pp. 655-667
    • Dover, A.1    Howard, R.2
  • 9
    • 0028416331 scopus 로고
    • Neural networks in civil engineering: Principles and understanding
    • Flood, I., Kartam, N, 1994. Neural networks in civil engineering: principles and understanding. Journal of Computing in Civil Engineering - ASCE 8(2), 131-148.
    • (1994) Journal of Computing in Civil Engineering - ASCE , vol.8 , Issue.2 , pp. 131-148
    • Flood, I.1    Kartam, N.2
  • 10
    • 0001867238 scopus 로고
    • Interpreting neural-network connection weights
    • Garson, G, 1991. Interpreting neural-network connection weights. AI Expert 6(7), 47-51.
    • (1991) AI Expert , vol.6 , Issue.7 , pp. 47-51
    • Garson, G.1
  • 12
    • 0028549824 scopus 로고
    • Nonlinear modeling in geotechnical engineering using neural networks
    • Goh, A. T. C., 1994a. Nonlinear modeling in geotechnical engineering using neural networks. Australian Civil Engineering Transactions 36(4), 293-297.
    • (1994) Australian Civil Engineering Transactions , vol.36 , Issue.4 , pp. 293-297
    • Goh, A.T.C.1
  • 13
    • 0029415865 scopus 로고
    • Empirical design in geotechnics using neural networks
    • Goh, A. T. C., 1995b. Empirical design in geotechnics using neural networks. Geotechnique 45(4), 709-714.
    • (1995) Geotechnique , vol.45 , Issue.4 , pp. 709-714
    • Goh, A.T.C.1
  • 16
    • 0024880831 scopus 로고
    • Multilayer feed forward networks are universal approximators
    • Hornik, K., Stinchcombe, M., White, H, 1989. Multilayer feed forward networks are universal approximators. Neural Network 2, 359-366.
    • (1989) Neural Network , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 19
    • 37249078004 scopus 로고    scopus 로고
    • Incorporating set-up and support cost distributions into driven pile design
    • Geotechnical Special Publications, ASCE/Geo-Institute
    • Komurka, V. E., 2004. Incorporating set-up and support cost distributions into driven pile design. Current Practices and Future Trends in Deep Foundations, 125. Geotechnical Special Publications, ASCE/Geo-Institute 16-49.
    • (2004) Current Practices and Future Trends in Deep Foundations , vol.125 , pp. 16-49
    • Komurka, V.E.1
  • 20
    • 0030472893 scopus 로고    scopus 로고
    • Prediction of pile bearing capacity using artificial neural networks
    • Lee, I. M., Lee, J. H., 1996. Prediction of pile bearing capacity using artificial neural networks. Journal of Computer and Geotechnics 18(3), 189-200.
    • (1996) Journal of Computer and Geotechnics , vol.18 , Issue.3 , pp. 189-200
    • Lee, I.M.1    Lee, J.H.2
  • 22
    • 0001728122 scopus 로고    scopus 로고
    • Applications of artificial neural net-works to forecasting of surface water quality variables: Issues, applications and challenges
    • Govindaraju, R. S., Rao, A. R. Eds., Kluwer, Dordrecht, The Netherlands
    • Maier, H. R., Dandy, G. C., 2000. Applications of artificial neural net-works to forecasting of surface water quality variables: issues, applications and challenges. In: Govindaraju, R. S., Rao, A. R. (Eds.), Artificial Neural Networks in Hydrology. Kluwer, Dordrecht, The Netherlands, pp. 287-309.
    • (2000) Artificial Neural Networks in Hydrology , pp. 287-309
    • Maier, H.R.1    Dandy, G.C.2
  • 26
    • 0029723312 scopus 로고    scopus 로고
    • A neural network approach for site characterization and uncertainty prediction
    • Najjar, Y., Basheer, I. 1996. A neural network approach for site characterization and uncertainty prediction. ASCE Geotechnical Special Publication, vol. 58(1), pp. 134-148.
    • (1996) ASCE Geotechnical Special Publication , vol.58 , Issue.1 , pp. 134-148
    • Najjar, Y.1    Basheer, I.2
  • 27
    • 67651006180 scopus 로고    scopus 로고
    • Prediction of pile settlement using artificial neural networks based on standard penetration test data
    • Nejad, F. P., Jaksa, M. B., 2011. Prediction of pile settlement using artificial neural networks based on standard penetration test data. Journal of Computer and Geotechnics 36(7), 1125-1133.
    • (2011) Journal of Computer and Geotechnics , vol.36 , Issue.7 , pp. 1125-1133
    • Nejad, F.P.1    Jaksa, M.B.2
  • 28
    • 84864853226 scopus 로고    scopus 로고
    • Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil
    • Ornek, M., Laman, M., Demir, A., Yildiz, A., 2012. Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils and Foundations 52(1), 69-80.
    • (2012) Soils and Foundations , vol.52 , Issue.1 , pp. 69-80
    • Ornek, M.1    Laman, M.2    Demir, A.3    Yildiz, A.4
  • 30
    • 0000646059 scopus 로고
    • Learning internal representation by error propagation
    • Rumelhart, D. E., McClelland, J. L. Eds., MIT Press, Cambridge, MA Chapter 8
    • Rumelhart, D. E, Hinton, GE, Williams, R. J., 1986. Learning internal representation by error propagation. In: Rumelhart, D. E., McClelland, J. L. (Eds.), Parallel Distributed Processing, vol. 1. MIT Press, Cambridge, MA (Chapter 8).
    • (1986) Parallel Distributed Processing , vol.1
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 36
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • Stone, M., 1974. Cross-validatory choice and assessment of statistical predictions. Journal of Royal Statistical Society B 36, 111-147.
    • (1974) Journal of Royal Statistical Society B , vol.36 , pp. 111-147
    • Stone, M.1
  • 39
    • 84879576060 scopus 로고    scopus 로고
    • Test pile program to determine axial capacity and pile setup for the Biloxi Bay Bridge
    • Thompson, W. R., Held, L., Say, S., 2009. Test pile program to determine axial capacity and pile setup for the Biloxi Bay Bridge. Deep Foundation Institute 3(1), 13-22.
    • (2009) Deep Foundation Institute , vol.3 , Issue.1 , pp. 13-22
    • Thompson, W.R.1    Held, L.2    Say, S.3
  • 41


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.