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A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test

Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2012-11-24

Included Journals: Scopus、CPCI-S、EI

Volume: 275-277

Page Number: 2620-2623

Key Words: earthquake; liquefaction; dynamic penetration test; artificial neural network

Abstract: Evaluation of liquefaction potential of soils is important in geotechnical earthquake engineering. Significant phenomena of gravelly soil liquefaction were reported in 2008 Wenchuan earthquake. Thus, further studies on the liquefaction potential of gravelly soil are needed. This paper investigates the potential of artificial neural networks-based approach to assess the liquefaction potential of gravelly soils form field data of dynamic penetration test. The success rates for occurrence and non-occurrence of liquefaction cases both are 100%. The study suggests that neural networks can successfully model the complex relationship between seismic parameters, soil parameters, and the liquefaction potential of gravelly soils.

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