Soil dynamic properties determination: A neurofuzzy system approach

Control and Intelligent Systems

Volumn 34, Issue 1, 2006, Pages 1-11

  Garcia S R ^a   Romo, M P ^a   Figueroa Nazuno, J ^b  

^a UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^b INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

Author keywords

Artificial neural networks; Neurofuzzy systems; Soil dynamic properties

Indexed keywords

NEUROFUZZY SYSTEMS; SHEAR STRAIN CURVES; SOIL DYNAMIC PROPERTIES;

ELASTIC MODULI; LEARNING SYSTEMS; SOILS;

FUZZY SETS;

EID: 33644797052     PISSN: 14801752     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. R-G. Campanella, W.P. Stewart, D. Roy, & M.P. Davies, Low strain characteristics of soils with the downhole seismic piezocone penetrometer, Proc. Symp. on Dynamic Geotechnical Testing II, San Francisco, 1994, 73-87.
2. Y. Yoshimi, K. Tokimatsu, & Y. Hosaka, Evaluation of liquefaction resistance of clean sands based on high-quality undisturbed samples, Soils and Foundations, 29(1), 1989, 3-104.
3. M.G. Jefferies & M.P. Davies, Use of CPTu to estimate equivalent SPT, Geotechnical Testing Journal, 16(4), 1993, 458-468.
4. H. Harder & G. von Bloh, Determination of representative CPT-parameters, Proc. Geotechn. Conf. Penetration Testing in the UK, Birmingham, Institute of Civil Engineers, 1988, 237-240.
5. W.P. Stewart & R.G. Campanella. Practical aspects of in-situ measurements of material damping with the SCPT, Canadian Geotechnical Journal, 30(2), 1993, 211-219.
6. R.G. Campanella & M.A. Kokan, A new approach to measuring dilatancy in saturated sands, Geotechnical Testing Journal, ASTM GTJODJ, 16(4), 1993, 485-495.
7. A. Jaime & M.P. Romo, Correlations between dynamic and static properties of Mexico City clay, Earthquake Spectra, EERI, 4(4), 1988, 787-804.
8. E. Ovando & M.P. Romo, Estimación de la velocidad de ondas S en la arcilla de la ciudad de México con ensayos de cono, Revista Sismodinámica, 2, 1991, 107-123.
9. K. Zen, Y. Umehara, & K. Hamada, Laboratory tests and in-situ seismic survey on vibratory shear modulus of clavey soils with different plasticities, Proc. 5th Japan Earthquake Engineering Symp., Tokyo, 1978, 721-728.
10. T. Kokushu, Y. Yoshida, & Y. Esashi, Dynamic properties of soft clay for wide strain range, Soils and Foundations, 22(4), 1982, 1-18.
11. R. Dobry & M. Vucetic, Dynamic properties and seismic response of soft clay deposits, Proc. Int. Symp. on Geotechnical Engineering of Soft Soils, 3, Mexico City, 1987, 51-87.
12. J.I. Sun, R. Golesorkhi, & H.B. Seed, Dynamic moduli and camping ratios for cohesive soils, Report no. EERC-88/15, Earthquake Engineering Research Center, University of California, Berkeley, 1988.
13. M.P. Romo & E. Ovando, Modeling the dynamic behaviour of Mexican clays, Proc. 11th World Conf. on Earthquake Engineering, Acapulco, Mexico, paper no. 1028, June 1996, Proc. in CD ROM.
14. B.O. Hardin & V.P. Drnevich, Shear modulus and damping in soils: design equations and curves, Journal of Geotechnical Division, ASCE, 98(GT7), 1972, 667-692.
15. L.A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, 90, 1997, 111-127.
16. P.P. Bonissone, Soft computing: The convergence of emerging reasoning technologies, Soft Computing, 1, 1997, 6-18.
17. A.W. Burke, The logic of evolution, and the reduction of holistic coherent systems to hierarchical feedback systems, in W.L. Harper & R. Skyrms (Eds.), Causation in Decision, Belief Change, and Statistics (Dordrecht: Kluwer, 1988).
18. J. Mendel, Fuzzy logic systems for engineering: A tutorial, Proc. IEEE, 83, 1995, 345-377.
19. V. Novak, Paradigm, formal properties and limits of fuzzy logic, International Journal of General Systems, 24, 1996, 377-406.
20. P. Hajek, Fuzzy logic as logic, in G. Coletti, D. Dubois, & R. Scozzafava (Eds.), Mathematical model for handling partial knowledge in artificial intelligence (New York: Plenum Press, 1995).
21. D. Dubois & H. Prade, Possibility theory: An approach to computerized processing of uncertainty (New York: Plenum Press, 1988).
22. N. Kosko, Neural Networks, fuzzy systems: Dynamical systems approach to machine intelligence (Engelwood Cliffs, NJ: Prentice-Hall, 1992).
23. T. Takagi & M. Sugeno. Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. on Systems, Man, and Cybernetics, SMC-15(1), 1985, 116-132.
24. J. Moody & C. Darken, Fast learning in networks of locally-tuned processing units, Tech. Rep. YALEU/DCS/RR-654, Dept. of Computer Science, Yale University, New Haven, CT, 1989, 227.
25. S. Fahlman, Faster-learning variations on backpropagation: An empirical study, in D. Touretzky, G. Hinton, & T. Sejnowski (Eds.), Proc. Connectionist Model Summer School (San Mateo, CA: Morgan Kaufmann, 1988).
26. R. Marsal & M. Mazari, The subsoil of Mexico City (Ciudad de México, Mexico: UNAM, 1969).
27. S. Uchigama, K. Tonouchi, & T. Imai, Measurement of S wave velocity of the ground and application of S wave velocity data for civil engineering, Oyo Technical Note No. 52, Tokyo, 1984.
28. M.P. Romo, Clay behaviour, ground response and soil-structure interaction studies in Mexico City, State of the Art Paper, 3rd Int. Conf. on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics, 2, St. Louis, Missouri, 1995, 1039-1051.
29. M.P. Romo, Comportamiento dinámico de la arcilla de la ciudad de México y su repercusion en la ingeniería de cimentaciones, Memoria del Simposio El Subsuelo de la Cuenca del Valle de México y su Relación con la Ingeniería de Cimentaciones a Cinco Anos del Sismo, Ciudad de México, Mexico, 1990, 83-94.
30. M. Vucetic & R. Dobry, Effect of soil plasticity on cyclic response, Journal of Geotechnical Engineering, 117(1), 1991, 89-107.
31. M.P. Romo, J.L. Rangel, S. García, & E. Ovando, Forecasting of shear wave velocities from CPT resistances by means of ANN, Proc. GEOTECH-YEAR 2000, Developments in Geotechnical Engineering, Bangkok, Thailand, 2000, 27-30.