Seismic Liquefaction Potential Assessed by Neural Networks
Publication: Journal of Geotechnical Engineering
Volume 120, Issue 9
Abstract
The feasibility of using neural networks to model the complex relationship between the seismic and soil parameters, and the liquefaction potential has been investigated. Neural‐networks are information‐processing systems whose architectures essentially mimic the biological system of the brain. A simple back‐propagation neural‐network algorithm was used. The neural networks were trained using actual field records. The performance of the neural‐network models improved as more input variables are provided. The model consisting of eight input variables was the most successful. These variables are: the standard penetration test (SPT) value, the fines content, the mean grain size , the equivalent dynamic shear stress , the total stress , the effective stress , the earthquake magnitude , and the maximum horizontal acceleration at ground surface. The most important input parameters have been identified as the SPT and fines content of the soil. Comparisons indicate that the neural‐network model is more reliable than the conventional dynamic stress method.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Berrill, J. B., and Davis, R. O. (1985). “Energy dissipation and seismic liquefaction of sands: revised model.” Soils Found., 25(2), 106–118.
2.
Christian, J. T., and Swiger, W. F. (1975). “Statistics of liquefaction and SPT results.” J. Geotech. Engrg. Div., ASCE, 101(11), 1135–1150.
3.
Crooks, T. (1992). “Care and feeding of neural networks.” AI Expert, 7(9), 36–41.
4.
Eberhart, R. C., and Dobbins, R. W. (1990). Neural network PC tools: a practical guide. Academic Press, San Diego, Calif.
5.
Garson, G. D. (1991). “Interpreting neural‐network connection weights.” AI Expert, 6(7), 47–51.
6.
Ghaboussi, J., Garrett, J. H. Jr., and Wu, X. (1991). “Knowledge‐based modeling of material behavior with neural networks.” J. Engrg. Mech., ASCE, 117(1), 132–153.
7.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities.” Proc. Nat. Acad. Sci., 79, 2554–2558.
8.
Kohonen, T. (1984). Self‐organisation and associative memory. Springer‐Verlag, Berlin, Germany.
9.
Law, K. T., Cao, Y. L., and He, G. N. (1990). “An energy approach for assessing seismic liquefaction potential.” Can. Geotech. J., 27, 320–329.
10.
Liao, S. S. C., Veneziano, D., and Whitman, R. V. (1988). “Regression models for evaluating liquefaction probability.” J. Geotech. Engrg., ASCE, 114(4), 389–411.
11.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representation by error propagation,” Parallel distributed processing: Foundations, Vol. 1, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass.
12.
Seed, H. B., Tokimatsu, H., Harder, L. F., and Chung, R. M. (1985). “Influence of SPT procedure in seismic liquefaction resistance evaluations.” J. Geotech. Engrg., ASCE, 111(12), 1425–1445.
13.
Stein, R. (1993). “Selecting data for neural networks.” AI Expert, 8(2), 42–47.
14.
Tokimatsu, K., and Yoshimi, Y. (1983). “Empirical correlation of soil liquefaction based on SPT N‐value and fines content.” Soils Found., 23(4), 56–74.
Information & Authors
Information
Published In
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: Aug 27, 1993
Published online: Sep 1, 1994
Published in print: Sep 1994
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.