Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Geotechnical and Geological Engineering

Volumn 29, Issue 5, 2011, Pages 725-748

  Alkroosh, I ^a   Nikraz, H ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Axial capacity; Cone penetration test; Correlation; Gene expression programming; Pile; Training and validation

Indexed keywords

ARTIFICIAL INTELLIGENT; AXIAL CAPACITY; BORED PILES; COMPUTATIONAL TECHNIQUE; CONE PENETRATION TEST; CONE PENETRATION TESTS; DRIVEN PILE; EXPERIMENTAL DATA; GENE EXPRESSION PROGRAMMING; MODEL CALIBRATION; MODEL VERIFICATION; PILE CAPACITY; SOIL PROPERTY; STEEL PILES; TRAINING SETS;

ARTIFICIAL INTELLIGENCE; GENE EXPRESSION; PILE FOUNDATIONS; RATING; SENSITIVITY ANALYSIS; SOIL TESTING; TESTING;

PILES;

ACCURACY ASSESSMENT; BEARING CAPACITY; CALIBRATION; CONE PENETRATION TEST; CORRELATION; GEOSTATISTICS; LOADING TEST; MODEL VALIDATION; PERFORMANCE ASSESSMENT; PILE; SOIL PROPERTY; SOIL-STRUCTURE INTERACTION;

EID: 80052068571     PISSN: 09603182     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10706-011-9413-1     Document Type: Article

References (31)

1. Abu-Farsakh M, Titi H (2004) Assesment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. J Geotech Environ Eng ASCE 130(9): 935-944.
2. Abu-Kiefa M (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng 124(12): 1177-1185.
3. Alsamman O (1995) The use of the CPT for calculating axial capacity of drilled shafts. PhD Thesis, University of Illinois, Urbana.
4. 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: 616-625.
5. Briaud J (1988) Evaluation of cone penetration test methods using 98 Ple load tests. In: Proceedings of the first international symposium on penetration testing, ISOPT-1, Orlando, vol 2, pp 687-697.
6. Briaud J, Miran J (1992) The cone pentrometer test. Department of Transportation USA.
7. Bustamante M, Gianeselli L (1982) Pile bearing capacity prediction by means of static penetrometer Cpt. In: Proceedings of the 2nd European symposium on penetration testing, ESOPT-II, Amsterdam, The Netherland, vol 2, pp 493-500.
8. Cai G, Liu S, Tong L, Du G (2008) Assessment of direct CPT and CPTU methods for predicting the ultimate bearing capacity of single piles. Eng Geol. doi: 10. 1016/j. enggeo. 200810. 010.
9. Chan W, Chow Y, Liu L (1995) Neural network: an alternative to pile driving formulas. Comput Geotech 17: 135-156.
10. Cramer N (1985) A representation for the adaptive generation of simple sequential programs. Academic press, Carnegie-Mellon University, Pittsburgh.
11. De Ruiter J, Beringen F (1979) Pile foundation for large North Sea structures. Mar Geotechnol 3(3): 267-314.
12. Esalmi A, Fellenius B (1997) Pile capacity by direct CPT and CPTu methods applied to 102 case histories. Can Geotech J 34(6): 886-904.
13. Eslami A (1996) Bearing capacity of piles from cone penetration data. PhD Thesis, University of Ottawa, Ottawa, Canada.
14. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2): 87-129.
15. Ferreira C (2002) Gene expression programming: mathematical modeling by artificial intelligence. Angra do Heroismo, Portugal.
16. Gepsoft (2002) Gene expression programming tool. http://www. gepsoft. com.
17. Goldberg D (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Mass.
18. Hansen JB (1963) Discussion on hyperbolic stress-strain response, cohesive soils. J Soil Mech Found Eng 89(4): 241-242.
19. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan, USA.
20. Javadi A, Rezania M, Nezhad M (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Comput Geotech 33(4-5): 222-233.
21. Koza J (1992) Genetic programming: on the programming of computers by natural selection. Cambridge, MA.
22. Kulhawy F, Mayne P (1990) Manual on estimating soil properties for foundation design. Electric Power Research Institute, Palo Alto.
23. Long J, Wysockey M (1999) Accuracy of methods for predicting axial capacity of deep foundations. Geotech Special Publ 88: 181-195.
24. Master T (1993) Practical neural network recipes in C++. Academic Press, San Diego.
25. Ng C, Simon W, Menzies B (2004) Soil-structure engineering of deep foundations excavations and tunnels. Thomas Telford Ltd, London.
26. Rezania M, Javadi A (2007) A new genetic programming model for predicting settlement of shallow foundations. Can Geotech J 44: 1462-1473.
27. Roberston P, Campanella R, Davies M, Sy A (1988) Axial capacity of driven piles in deltaic soils using CPT. In: Proceedings of the first international symposium on the penetration testing, ISOPT-1 Orlando, vol 2, pp 919-928.
28. Schmertmann J (1978) Guidelines for cone penetration test, performance and design. US Department of Transportation, Washington, DC, p 145. Rep. No FHWA-TS-78-209.
29. Shahin M (2008) Modelling axial capacity of pile foundations by intelligent computing. In: Proceedings of the second BGA international conference on foundations, Dundee, Scotland.
30. Shahin M (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47: 230-243.
31. Shahin M, Maier H, Jaksa M (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng 18(2): 105-114.