Empirical model for liquefaction resistance of soils based on artificial neural network learning of case histories

Chien Hsun Chen, C. Hsein Juang, Matt J. Schuster

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In recent years, the method of artificial neural network (ANN) has been applied to solving geotechnical problems, especially for mapping high complexity soil response with a multi-parameter input vector. Successful applications of simple but robust, feed-forward neural networks have been reported. In this paper, a procedure for developing an empirical model for assessing liquefaction potential of soils based on ANN learning of case histories is presented. The intended model is based on piezocone penetration test (CPTu). A pore pressure parameter Bq (excess pore pressure ratio) is included in the formulation of soil behavior index Ic, which, in turn, is used along with the normalized cone tip resistance qc1N to characterize liquefaction resistance. For ANN learning, each case history consists of three input variables, qc1N, Ic, and CSR (cyclic stress ratio, as a proxy to liquefaction loading), and one output variable, binary field observation (yes or no), and the learning of case histories is carried out with a feed-forward back-propagation neural network. The successfully learned or trained neural network is then used to search for a limit state for liquefaction triggering, and the searched data is employed to develop the intended empirical model for liquefaction resistance. Characteristics and applications of the developed ANN-based empirical model are presented. Copyright ASCE 2008.

Original languageEnglish
Title of host publicationProceedings of Sessions of GeoCongress 2008 - GeoCongress 2008
Subtitle of host publicationCharacterization, Monitoring, and Modeling of GeoSystems, GSP 179
Number of pages8
StatePublished - 2008
EventGeoCongress 2008: Characterization, Monitoring, and Modeling of GeoSystems - New Orleans, LA, United States
Duration: 9 Mar 200812 Mar 2008

Publication series

NameGeotechnical Special Publication
ISSN (Print)0895-0563


ConferenceGeoCongress 2008: Characterization, Monitoring, and Modeling of GeoSystems
Country/TerritoryUnited States
CityNew Orleans, LA


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