Probabilistic Methods for Assessing Soil Liquefaction Potential and Effect

C. Hsein Juang, Jie Zhang, Sara Khoshnevisan, Wenping Gong

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

3 Scopus citations


Cyclic stress-based simplified methods have been widely used for liquefaction potential assessment. While the original simplified procedure pioneered by Seed and Idriss in the early 1970s was based on a large number of fundamental laboratory studies supplemented with some field observations, the more recent simplified methods were almost always developed solely based on the database of field cases using the framework of the original simplified procedure. There are, however, substantial uncertainties in the collected case histories and in the model development process. Coupled with the need for risk assessment and performance-based design requirement, the probabilistic methods have been increasingly used in liquefaction potential and effect assessment. While various probabilistic methods for liquefaction assessment are available in the literature, these methods have not been addressed systematically in a single report. In this paper, the probabilistic methods for liquefaction assessment, including the discriminant analysis, the logistic regression, artificial neural network, Bayesian methods, and performance-based methods, are reviewed. The formulations, key assumptions, advantages and limitations, and their applications for liquefaction assessment are discussed. The challenges and the need for further research are also addressed.

Original languageEnglish
Title of host publicationGeotechnical Special Publication
EditorsD. V. Griffiths, Gordon A. Fenton, Jinsong Huang, Limin Zhang
PublisherAmerican Society of Civil Engineers (ASCE)
Number of pages24
EditionGSP 282
ISBN (Electronic)9780784480694
StatePublished - 2017
EventGeo-Risk 2017: Keynote Lectures - Denver, United States
Duration: 4 Jun 20177 Jun 2017

Publication series

NameGeotechnical Special Publication
NumberGSP 282
ISSN (Print)0895-0563


ConferenceGeo-Risk 2017: Keynote Lectures
Country/TerritoryUnited States


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