Location

San Diego, California

Session Start Date

3-26-2001

Session End Date

3-31-2001

Abstract

Three preliminary probability-based models and one artificial neural network model for evaluating soil liquefaction potential using shear wave velocity measurements are presented and compared with the deterministic curves developed by Andrus et al. The probability models are developed using logistic regression and Bayesian techniques applied to the same case history data used to develop the deterministic curves. The case history data consists of in situ shear wave velocity measurements at over 70 sites and field performance data from 26 earthquakes. The artificial neural network model is a high-order function capable of tracking the irregular boundary separating individual liquefaction and no liquefaction case histories. From the logistic regression and Bayesian models, the deterministic curve is characterized with a probability of about 30 %. This finding indicates that the shear wave-based deterministic curve and the SPT-based deterministic curve exhibit similar conservatism. The results provide a method for liquefaction risk analysis.

Department(s)

Civil, Architectural and Environmental Engineering

Appears In

International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics

Meeting Name

Fourth Conference

Publisher

University of Missouri--Rolla

Publication Date

3-26-2001

Document Version

Final Version

Rights

© 2001 University of Missouri--Rolla, All rights reserved.

Document Type

Article - Conference proceedings

File Type

text

Language

English

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Mar 26th, 12:00 AM Mar 31st, 12:00 AM

Probability-Based Liquefaction Evaluation Using Shear Wave Velocity Measurements

San Diego, California

Three preliminary probability-based models and one artificial neural network model for evaluating soil liquefaction potential using shear wave velocity measurements are presented and compared with the deterministic curves developed by Andrus et al. The probability models are developed using logistic regression and Bayesian techniques applied to the same case history data used to develop the deterministic curves. The case history data consists of in situ shear wave velocity measurements at over 70 sites and field performance data from 26 earthquakes. The artificial neural network model is a high-order function capable of tracking the irregular boundary separating individual liquefaction and no liquefaction case histories. From the logistic regression and Bayesian models, the deterministic curve is characterized with a probability of about 30 %. This finding indicates that the shear wave-based deterministic curve and the SPT-based deterministic curve exhibit similar conservatism. The results provide a method for liquefaction risk analysis.