Assessing Probability-based Methods for Liquefaction Potential Evaluation

TLDR

Logistic regression is a well‑established statistical procedure, while Bayesian mapping is a newer application of Bayes’ theorem to soil liquefaction evaluation. The study assesses and compares probabilistic liquefaction potential methods, specifically logistic regression and Bayesian mapping. Using simplified methods (Seed–Idriss, Robertson–Wride, Andrus–Stokoe) based on SPT, CPT, and shear‑wave velocity, the authors develop Bayesian mapping functions and compare the resulting liquefaction probabilities. Bayesian mapping is preferred over logistic regression for site‑specific liquefaction probability, though both yield comparable results, and the authors advocate probability‑based procedures for evaluating liquefaction potential.

Abstract

This paper presents an assessment of existing and new probabilistic methods for liquefaction potential evaluation. Emphasis is placed on comparison of probabilities of liquefaction calculated with two different approaches, logistic regression and Bayesian mapping. Logistic regression is a well-established statistical procedure, whereas Bayesian mapping is a relatively new application of the Bayes' theorem to the evaluation of soil liquefaction. In the present study, simplified procedures for soil liquefaction evaluation, including the Seed–Idriss, Robertson–Wride, and Andrus–Stokoe methods, based on the standard penetration test, cone penetration test, and shear wave velocity measurement, respectively, are used as the basis for developing Bayesian mapping functions. The present study shows that the Bayesian mapping approach is preferred over the logistic regression approach for estimating the site-specific probability of liquefaction, although both methods yield comparable probabilities. The paper also compares the three simplified methods in the context of probability of liquefaction, and argues for the use of probability-based procedures for evaluating liquefaction potential.