Neural network prediction of pullout capacity of marquee ground anchors

Computers and Geotechnics

Volumn 32, Issue 3, 2005, Pages 153-163

  Shahin, M A ^a   Jaksa, M B ^b  

^a UNIVERSITY OF WOLLONGONG   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

B spline neurofuzzy networks; Back propagation; Marquee ground anchors; Multi layer perceptrons; Neural networks; Pullout capacity

Indexed keywords

ALGORITHMS; ANCHORS; BACKPROPAGATION; FAILURE (MECHANICAL); FUZZY SETS; NEURAL NETWORKS;

BACK-PROPAGATION ALGORITHMS; MARQUEES; PULLOUT CAPACITY; TEMPORARY LIGHT STRUCTURES;

GEOTECHNICAL ENGINEERING;

ANCHOR; ARTIFICIAL NEURAL NETWORK;

EID: 17444421871     PISSN: 0266352X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compgeo.2005.02.003     Document Type: Article

References (22)

1. Lau DS, Simmons JV. Interpretation of field tests on small-scale ground anchors. In: Specialty geomeIhanics symposium: interpretation of field testing for design parameters, Adelaide, Barton (IEAust); 1986. p. 85-8
2. W. Su R.J. Fragaszy Uplift testing of model anchors J Geotech Eng ASCE 114 9 1988 961-83
3. Shahin MA, Jaksa MB. Modelling the pullout capacity of marquee ground anchors. Research Report No. R174, School of Civil and Environmental Engineering, The University of Adelaide, Adelaide; 2003
4. M.A. Shahin M.B. Jaksa H.R. Maier Artificial neural network applications in geotechnical engineering Austr Geomech 36 1 2001 49-62
5. M.A. Shahin M.B. Jaksa H.R. Maier Neurofuzzy networks applied to settlement of shallow foundations on granular soils In: Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP9, San Francisco 2003 Millpress Rotterdam 1379-83
6. S.H. Ni P.C. Lu C.H. Juang A fuzzy neural network approach to evaluation of slope failure potential J Microcomputers Civil Eng 11 1996 59-66
7. J.M. Zurada Introduction to artificial neural systems 1992 West Publishing Company St. Paul
8. L.V. Fausett Fundamentals neural networks: Architecture, algorithms, and applications 1994 Prentice-Hall Englewood Cliffs (NJ)
9. M. Stone Cross-validatory choice and assessment of statistical predictions J Roy Stat Soc B 36 1974 111-47
10. M. Smith Neural networks for statistical modelling 1993 Van Nostrand Reinhold New York
11. L.A. Zadeh Fuzzy sets Inf Contr 8 1965 338-53
12. M. Brown C.J. Harris Neurofuzzy adaptive modelling and control 1994 Prentice-Hall Englewood Cliffs (NJ)
13. T. Kavli ASMOD - an algorithm for adaptive spline modelling of observation data Int J Contr 58 4 1993 947-67
14. Neusciences. Neuframe Version 4.0. Southampton, Hampshire: Neusciences Corp.; 2000
15. T. Masters Practical neural network recipes in C++ 1993 Academic Press San Diego (CA)
16. M.A. Shahin H.R. Maier M.B. Jaksa Data division for developing neural networks applied to geotechnical engineering J Computing Civil Eng 18 2 2004 105-14
17. S.A. Tokar P.A. Johnson Rainfall-runoff modeling using artificial neural networks J Hydrologic Eng 4 3 1999 232-9
18. K. Hornik M. Stinchcombe H. White Multilayer feedforward networks are universal approximators Neural Networks 2 1989 359-66
19. M. Caudill Neural networks primer, Part III AI Expert 3 6 1988 53-9
20. Bustamante M, Gianeselli L. Pile bearing capacity prediction by means of static penetrometer CPT. In: Proceedings of the 2nd European symposium on penetration testing, Amsterdam; 1982. p. 493-500
21. B.M. Das Principles of foundation engineering 3rd ed. 1995 PWS Publishing Company Boston (MA)
22. J.E. Bowles Foundation analysis and design 1997 McGraw-Hill New York