Intelligent computing for modeling axial capacity of pile foundations

Canadian Geotechnical Journal

Volumn 47, Issue 2, 2010, Pages 230-243

  Shahin, Mohamed A ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Artificial neural networks; Axial capacity; Drilled shafts; Driven piles; Modeling

Indexed keywords

ARTIFICIAL INTELLIGENCE TECHNIQUES; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; ARTIFICIAL NEURAL NETWORKS; AXIAL CAPACITY; CONE PENETRATION TESTS; DESIGN EQUATION; DRILLED SHAFT; DRIVEN PILE; LOAD TEST; PILE CAPACITY; SOIL PROPERTY; STATISTICAL ANALYSIS;

CHARGE TRAPPING; FORECASTING; INTELLIGENT COMPUTING; MINING; PILE FOUNDATIONS; PILES; SHAFT SINKING; SOIL TESTING;

NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COLLAPSIBLE SOIL; CONE PENETRATION TEST; DRILLING; ENVIRONMENTAL MODELING; PILE; PREDICTION; SHAFT;

EID: 76749110197     PISSN: 00083674     EISSN: None     Source Type: Journal    
DOI: 10.1139/T09-094     Document Type: Article

References (29)

1. Abu-Farsakh, M.Y., and Titi, H.H. 2004. Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 130(9): 935-944. doi:10.1061/(ASCE)1090-0241(2004)130:9(935).
2. Abu-Kiefa, M.A. 1998. General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 124(12): 1177-1185.doi:10.1061/(ASCE)1090-0241(1998)124:12(1177).
3. Alsamman, O.M. 1995. The use of CPT for calculating axial capacity of drilled shafts. Ph.D. thesis, University of Illinois-Champaign, Urbana, Ill.
4. Briaud, J.L., and Miran, J. 1992. The cone penetrometer test. Federal Highway Administration, U.S. Department of Transportation, McLean, Va. Report FHWA-SA-91-043.
5. Brinch Hansen, J. 1963. Discussion on hyperbolic stress-strain response, cohesive soils. Journal of Soil Mechanics and Foundation Engineering, 89(SM4): 241-242.
6. Bustamante, M., and Gianeselli, L. 1982. Pile bearing capacity prediction by means of static penetrometer CPT. In Proceedings of the 2nd European Symposium on Penetration Testing, Amsterdam, 24-27 May 1982. A.A. Balkema, Rotterdam, the Netherlands.Vol. 2, pp. 493-500.
7. Caudill, M. 1988. Neural networks primer, Part III. AI Expert, 3(6): 53-59.
8. de Ruiter, J., and Beringen, F.L. 1979. Pile foundation for large North Sea structures. Marine Geotechnology, 3(3): 267-314. doi:10.1080/10641197909379805.
9. Eslami, A. 1996. Bearing capacity of piles from cone penetration test data. Ph.D. thesis, University of Ottawa, Ottawa, Ont.
10. Eslami, A., and Fellenius, B.H. 1997. Pile capacity by direct CPT and CPTu methods applied to 102 case histories. Canadian Geotechnical Journal, 34(6): 886-904. doi:10.1139/cgj-34-6-886.
11. Fausett, L.V. 1994. Fundamentals neural networks: Architecture, algorithms, and applications. Prentice-Hall, Englewood Cliffs, N.J.
12. Hornik, K., Stinchcombe, M., and White, H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366. doi:10.1016/0893-6080(89)90020-8.
13. Kulhawy, F.H., and Mayne, P.W. 1990. Manual on estimating soil properties for foundation design. Electric Power Research Institute, Palo Alto, Calif. Report EL-6800.
14. Lee, I.M., and Lee, J.H. 1996. Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 18(3): 189-200. doi:10.1016/0266-352X(95)00027-8.
15. Long, J.H., and Wysockey, M.H. 1999. Accuracy of methods for predicting axial capacity of deep foundations. In Proceedings of the OTRC'99 Conference: Analysis, Design, Construction, and Testing of Deep Foundations, Austin, Tex., 29-30 April 1999.
16. GSP NO. 88. Edited by J.M. Roesset. American Society of Civil Engineers (ASCE), Reston, Va. pp. 180-195.
17. Masters, T. 1993. Practical neural network recipes in C++. Academic Press, San Diego, Calif.
18. Neusciences. 2000. Neuframe. Version 4.0 [computer program]. Neusciences Corp., Southampton, Hampshire, UK.
19. Reese, L.C., and O'Neill, M.W. 1988. Drilled shafts: Construction procedures and design methods. U. S. Department of Transportation, Dallas, Tex. Report FHWA-HI-88-042.
20. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. 1986. Learning internal representation by error propagation. In Parallel distributed processing. Edited by D.E. Rumelhart and J.L. McClelland. MIT Press, Cambridge, Mass. Vol. 1, pp. 318-362.
21. Schmertmann, J.H. 1978. Guidelines for cone penetration test, performance and design. U. S. Department of Transportation, Washington, D.C. Report No. FHWA-TS-78-209.
22. Shahin, M.A. 2008. Modelling axial capacity of pile foundations by intelligent computing. In Proceedings of the Second BGA International Conference on Foundations, ICOF2008, Dundee, Scotland, 24-27 June 2008. Edited by M.J. Brown, M.F. Bransby, A.J. Brennan, and J.A. Knappett. IHS BRE Press, Watford, Hertfordshire, UK. pp. 283-294.
23. Shahin, M.A., and Indraratna, B. 2006. Modelling the mechanical behaviour of railway ballast using artificial neural networks. Canadian Geotechnical Journal, 43(11): 1144-1152. doi:10.1139/T06-077.
24. Shahin, M.A., and Jaksa, M.B. 2006. Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks. Canadian Geotechnical Journal, 43(6): 626-637. doi:10.1139/T06-029.
25. Shahin, M.A., Jaksa, M.B., and Maier, H.R. 2001. Artificial neural network applications in geotechnical engineering. Australian Geomechanics, 36(1): 49-62.
26. Shahin, M.A., Maier, H.R., and Jaksa, M.B. 2002a. Predicting settlement of shallow foundations using neural networks. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 128(9): 785-793. doi:10.1061/(ASCE)1090-0241(2002)128:9(785).
27. Shahin, M.A., Jaksa, M.B., and Maier, H.R. 2002b. Artificial neural network-based settlement prediction formula for shallow foundations on granular soils. Australian Geomechanics, 37(4):45-52.
28. Shahin, M.A., Maier, H.R., and Jaksa, M.B. 2004. Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, ASCE, 18(2): 105-114. doi:10.1061/(ASCE)0887-3801(2004)18:2(105).
29. Shahin, M.A., Jaksa, M.B., and Maier, H.R. 2009. Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Journal of Advances in Artificial Neural Systems [online], 2009: Article ID 308239.Available from www.hindawi.com/journals/aans/2009/308239.html [accessed 21 January 2010].