Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard Sand mixtures

Expert Systems with Applications

Volumn 37, Issue 1, 2010, Pages 842-851

  Firat Cabalar, Ali ^a   Cevik, Abdulkadir ^a   Gokceoglu, Candan ^b   Baykal, Gokhan ^c  

^a GAZIANTEP UNIVERSITY   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

^c BOGAZICI UNIVERSITY   (Turkey)

Author keywords

Constitutive modeling; Leighton Buzzard Sand fraction B; Leighton Buzzard Sand fraction E; Neuro fuzzy; Triaxial testing

Indexed keywords

CONSTITUTIVE MODELING; LEIGHTON BUZZARD SAND FRACTION B; LEIGHTON BUZZARD SAND FRACTION E; NEURO-FUZZY; TRIAXIAL TESTING;

CEMENTS; CLAY MINERALS; MIXTURES; PORE PRESSURE; PRESSURE DISTRIBUTION; WATER; WATER CONTENT;

SAND;

EID: 70349469832     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.05.085     Document Type: Article

References (31)

1. Bai Y., Zhuang H., and Wang D. Advanced Fuzzy Logic Technologies in industrial applications (2006), Springer
2. Basheer, I. A. (1988). Neuromechanistic-based modeling and simulation of constitutive behavior of fine-grained soils. Ph.D. Thesis, Kansas State University.
3. Ellis G.W., Yao C., Zhao R., and Penumadu D. Stress-strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering Division, ASCE 121 5 (1995) 429-435
4. Gilboy G. The compressibility of sand-mica mixtures. Proceedings of the ASCE 2 (1928) 555-568
5. Ghaboussi J., Garret Jr. J.H., and Wu X. Knowledge-based-modeling of material behavior with neural networks. Journal of Engineering Mechanics, ASCE 117 1 (1991) 132-153
6. Ghaboussi. J., Garret, J. H., Jr., & Wu, X. 1990. Material modeling with neural networks. In Proceedings of the international conference on numerical methods in engineering: Theory and applications (pp. 701-717). Swansea, UK.
7. Gokceoglu C., Yesilnacar E., Sonmez H., and Kayabasi A. A neuro-fuzzy model for modulus of deformation of jointed rock masses. Computers and Geotechnics 31 (2004) 375-383
8. Haris, J. (2006). Fuzzy Logic applications in engineering science. Springer.
9. Hight, D. W., Georgiannou, V. N., Martin, P. L., & Mundegar, A. K. (1998). Flow slides in micaceous sand. In E. Yanagisawa, N. Moroto, & T. Mitachi (Eds.), Problematic soils (pp. 945-958). Balkema, Rotterdam, Sendai, Japan.
10. Jang JR., Sun CT., and Mizutani E. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence (1997), Prentice Hall
11. Jin Y., and Jiang J. Techniques in neural-network based fuzzy system identification and their application to control of complex systems. In: Leondes C.T. (Ed). Fuzzy theory systems, techniques and applications (1999), Academic Press, New York 112-128
12. Kortesis S., and Panagiotopoulos P.D. Neural networks for computing in structural analysis: Methods and prospects of applications. International Journal for Numerical Methods in Engineering 36 (1993) 2305-2318
13. Lade P.V., Liggio Jr. C.D., and Yamamuro J.A. Effects of nonplastic fines on minimum and maximum void ratios of sand. Geotechnical Testing Journal, ASTM 21 4 (1998) 336-347
14. McCarthy D.F., and Leonard R.J. Compaction and compression characteristics of micaceous fine sands and silts. Highway Research Record 22 (1963), Transportation Research Board, Washington, DC (pp. 23-37)
15. Monkul, M. M., & Ozden, G. (2005). Effect of intergranular void ratio on one-dimensional compression behavior. In Proceedings of international conference on problematic soils, international society of soil mechanics and geotechnical engineering (pp. 1203-1209). Famagusta, Turkish Republic of Northern Cyprus, 3.
16. Mundegar, A. K. (1997). An investigation into the effects of platy mica particles on the behaviour of sand. M.Sc. Thesis, Imperial College, London.
17. Naeini S.A., and Baziar M.H. Effect of fines content on steadystate strength of mixed and layered samples of a sand. Soil Dynamics and Earthquake Engineering 24 (2004) 181-187
18. Olson, R. E., & Mesri, G. (1970). Mechanisms controlling the compressibility of clay. Journal of the Soil Mechanics and Foundations Division, ASCE, 96(SM6), 1863-1878, Proc. Paper 7649.
19. Penumadu, D., Jin-Nan, L., Chameau, J.-L., & Sandarajah. A. (1994). Anisotropic rate dependent behavior of clays using neural networks. In Proceedings of XIIIICSMFE (pp. 1445-1448). ICSMFE, New Delhi, India, 4.
20. Rutkowski L. Flexible neuro-fuzzy systems: structures, Learning and performance evaluation (2004), Kluwer Academic Publishers
21. Salgado R., Bandini P., and Karim A. Shear strength and stiffness of silty sand. Journal of Geotechnical and Geoenvironmental Engineering, ASCE 126 5 (2000) 451-462
22. Sidarta, Djoni E. (2000). Neural Network-Based Constitutive Modeling of granular material. Ph.D. Thesis, University of Illinois at Urbana-Champaign.
23. Sivanandam S.N., Sumathi S., and Deepa S.N. Introduction to Fuzzy Logic using MATLAB (2007), Springer
24. Terzaghi, K. (1925). Erdbaumechanik auf bodenphysikalischer grundlage. Deuticke, Leipzig/Vienna.
25. Theron, M. (2004). The effect of particle shape on the behaviour of gold tailings. Ph.D. Thesis, University of Southampton, UK.
26. Waszczyszyn Z. Some new results in applications of backpropagation neural networks in structural and civil engineering. Advances in engineering computational technology (1998), Civil-Comp Press, Edinburgh (pp. 173-187)
27. Waszczyszyn, Z. (2000). Neural networks in plasticity: Some new results and prospects of applications. In European congress on computational methods in applied sciences and engineering, ECCOMAS.
28. Wu, X. (1991). Neural network-based material modeling. Ph.D. Thesis, Department of Civil Engineering, University of Illinois at Urbana-Champaign.
29. Yagawa C., and Okuda H. Neural networks in computational mechanics. Archives of Computational Methods in Engineering 4 (1996) 435-512
30. Zadeh L.A. Fuzzy sets. Information and Control 8 3 (1965) 338-353
31. Zhu J.-H., Zaman M.M., and Anderson S.A. Modelling of shearing behaviour of a residual soil with recurrent neural network. International Journal for Numerical and Analytical Methods in Geomechanics 22 8 (1998) 671-687