Validation and Application of Empirical Liquefaction Models
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 136, Issue 12
Abstract
Empirical liquefaction models (ELMs) are the standard approach for predicting the occurrence of soil liquefaction. These models are typically based on in situ index tests, such as the standard penetration test (SPT) and cone penetration test (CPT), and are broadly classified as deterministic and probabilistic models. No objective and quantitative comparison of these models have been published. Similarly, no rigorous procedure has been published for choosing the threshold required for probabilistic models. This paper provides (1) a quantitative comparison of the predictive performance of ELMs; (2) a reproducible method for choosing the threshold that is needed to apply the probabilistic ELMs; and (3) an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine (SVM). Deterministic and probabilistic ELMs have been developed for SPT and CPT data. For deterministic ELMs, we compare the “simplified procedure,” the Bayesian updating method, and the SVM models for both SPT and CPT data. For probabilistic ELMs, we compare the Bayesian updating method with the SVM models. We compare these different approaches within a quantitative validation framework. This framework includes validation metrics developed within the statistics and artificial intelligence fields that are not common in the geotechnical literature. We incorporate estimated costs associated with risk as well as with risk mitigation. We conclude that (1) the best performing ELM depends on the associated costs; (2) the unique costs associated with an individual project directly determine the optimal threshold for the probabilistic ELMs; and (3) the more recent ELMs only marginally improve prediction accuracy; thus, efforts should focus on improving data collection.
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Acknowledgments
The work of the first and second writers is funded by National Science Foundation Grant No. NSFCMMI-0547190. This financial support is greatly appreciated. The writers also gratefully acknowledge the editorial comments and suggestions to the first draft of this paper provided by Dr. Robb E. S. Moss of California Polytechnic State University, Dr. Robert Kayen of the United States Geologic Survey, Dr. Eric M. Thompson of Tufts University, and reviewers at JGGE.
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 136 • Issue 12 • December 2010
Pages: 1618 - 1633
Copyright
© 2010 ASCE.
History
Received: Jul 30, 2009
Accepted: Jun 2, 2010
Published online: Nov 15, 2010
Published in print: Dec 2010
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