Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks

Soils and Foundations

Volumn 54, Issue 3, 2014, Pages 515-522

  Shahin, Mohamed A ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Load settlement; Modeling; Pile foundations; Recurrent neural networks

Indexed keywords

BEARING CAPACITY; FOUNDATIONS; MODELS; PILE FOUNDATIONS; RECURRENT NEURAL NETWORKS;

AXIAL LOADING; CONE PENETRATION TESTS; LOAD SETTLEMENT; PILE LOAD TEST; PREDICTION MODEL; RECURRENT NEURAL NETWORK (RNNS); ROUTINE DESIGN; TECHNICAL NOTES;

PILES;

EID: 84906948208     PISSN: 00380806     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sandf.2014.04.015     Document Type: Conference Paper

References (41)

1. Abu-Kiefa, M.A, 1998. General regression neural networks for driven piles in cohesionless soils. J. Geotech. Geoenviron. Eng. 124 (12), 1177-1185.
2. Ahmad, I., El Naggar, H., Kahn, A.N., 2007. Artificial neural network application to estimate kinematic soil pile interaction response parameters. Soil Dyn. Earthq. Eng. 27 (9), 892-905.
3. Ardalan, H., Eslami, A., Nariman-Zadeh, N, 2009. Piles shaft capacity from CPT and CPTU data by polynomial neural networks and genetic algorithms. Comput. Geotech. 36 (4), 616-625.
4. Alkroosh, I., Nikraz, H., 2011. Simulating pile load-settlement behavior from CPT data using intelligent computing. Cent. Eur. J. Eng. 1 (3), 295-305.
5. Briaud, J.L., Tucker, L.M., Anderson, J.S., Perdomo, D., Coyle, H.M., 1986. Development of an improved Pile Design Procedure for Single Piles in Clays and Sands. Research Report 4981-1. Civil Engineering, Texas A&M University, Texas.
6. Bowles, J.E., 1997. Foundation Analysis and Design. McGraw-Hill, New York.
7. Caudill, M., 1988. Neural networks primer, Part III. AI Expert 3 (6), 53-59.
8. Cybenko, G, 1989. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 3, 303-314.
9. Chan, W.T., Chow, Y.K., Liu, L.F., 1995. Neural network: an alternative to pile driving formulas. Comput. Geotech. 17, 135-156.
10. Cherkassky, V., Krasnopolsky, D.P., Valdes, J., 2006. Computational intelligence in earth sciences and environmental applications: issues and challenges. Neural Netw. 19, 113-121.
11. Das, S.K., Basudhar, P.K., 2006. Undrained lateral load capacity of piles in clay using artificial neural network. Comput. Geotech. 33 (8), 454-459.
12. Das, S.K., Sivakugan, N, 2010. Discussion of: intelligent computing for modeling axial capacity of pile foundations. Can. Geotech. J. 47, 928-930.
13. Eslami, A., 1996. Bearing Capacity of Piles from Cone Penetration Test Data (Ph.D. thesis). University of Ottawa, Ottawa, Ontario.
14. Fellenius, B.H., 1988. Unified design of piles and pile groups. Transp. Res. Rec. 1169, 75-81.
15. Fausett, L.V., 1994. Fundamentals Neural Networks: Architecture, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs, New Jersey.
16. Goh, A.T.C., 1996. Pile driving records reanalyzed using neural networks. J. Geotech. Eng. 122 (6), 492-495.
17. Goh, A.T., Kulhawy, F.H., Chua, C.G, 2005. Bayesian neural network analysis of undrained side resistance of drilled shafts. J. Geotech. Geoenviron. Eng. 131 (1), 84-93.
18. Hecht-Nielsen, R., 1989. Theory of the backpropagation neural network. In: Proceedings of the International Joint Conference on Neural Networks. Washington, DC, pp. 593-605.
19. Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359-366.
20. Hecht-Nielsen, R., 1990. Neurocomputing. Addison-Wesley Publishing Company, Reading, MA.
21. Jordan, M.I., 1986. Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the 8th Annual Conference of the Cognitive Science Society. Amherst, MA, pp. 531-546.
22. Kulhawy, F.H., Mayne, P.W., 1990. Manual on Estimating Soil Properties for Foundation Design. Report EL-6800, Electric Power Research Institute, Palo Alto, CA.
23. Lee, I.M., Lee, J.H., 1996. Prediction of pile bearing capacity using artificial neural networks. Comput. Geotech. 18 (3), 189-200.
24. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, San Diego, California.
25. Microcal, 1999. Microcal Origin Version 6.0. Microcal Software, Inc., Northampton, MA.
26. Murthy, V.N.S., 2003. Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering. Marcel Dekker, Inc., New York.
27. Nejad, F.P., Jaksa, M.B., Kakhi, M., McCabe, B.A., 2009. Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput. Geotech. 36 (7), 1125-1133.
28. Penumadu, D., Zhao, R., 1999. Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Comput. Geotech. 24 (3), 207-230.
29. Pal, M., 2006. Support vector machine-based modeling of seismic liquefaction potential. Int. J. Numer. Anal. Methods Geomech. 30 (10), 983-996.
30. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning internal representation by error propagation. In: Rumelhart, D.E., McClelland, J. L. (Eds.), Parallel Distributed Processing. MIT Press, Cambridge.
31. Reese, L.C., Isenhower, W.M., Wang, S.T., 2006. Analysis and Design of Shallow and Deep Foundations. John Wiley & Sons, New Jersey.
32. Shahin, M.A., Jaksa, M.B., Maier, H.R., 2001. Artificial neural network applications in geotechnical engineering. Aust. Geomech. 36 (1), 49-62.
33. Shahin, M.A., Maier, H.R., Jaksa, M.B., 2004. Data division for developing neural networks applied to geotechnical engineering. J. Comput. Civ. Eng. 18 (2), 105-114.
34. Shahin, M.A., Indraratna, B., 2006. Modeling the mechanical behavior of railway ballast using artificial neural networks. Can. Geotech. J. 43 (1), 1144-1152.
35. Shahin, M.A., Jaksa, M.B., 2006. Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks. Can. Geotech. J. 43 (6), 626-637.
36. Shahin, M.A., Jaksa, M.B., Maier, H.R., 2009. Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. J. Adv. Artif. Neural Syst. (Article ID 308239, 9 pp). http://dx.doi.org/10.1155/2009/308239.
37. Shahin, M.A., 2010. Intelligent computing for modeling axial capacity of pile foundations. Can. Geotech. J. 47 (2), 230-243.
38. Shahin, M.A., 2013. Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. In: Yang, X., Gandomi, A.H., Talatahari, S., Alavi, A.H. (Eds.), Metaheuristics in Water, Geotechnical and Transport Engineering. Elsevier Inc., London, pp. 169-204.
39. Teh, C.I., Wong, K.S., Goh, A.T.C., Jaritngam, S., 1997. Prediction of pile capacity using neural networks. J. Comput. Civ. Eng. 11 (2), 129-138.
40. Tarawneh, B., 2013. Pipe pile setup: database and prediction model using artificial neural network. Soils Found. 53 (4), 607-615.
41. Ward, 2007. NeuroShell 2 Release, 4. Ward Systems Group, Inc. (MA).