Location
San Diego, California
Presentation Date
29 Mar 2001, 7:30 pm - 9:30 pm
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
Meeting Name
4th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
University of Missouri--Rolla
Document Version
Final Version
Rights
© 2001 University of Missouri--Rolla, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Document Type
Article - Conference proceedings
File Type
text
Language
English
Recommended Citation
Juang, C. Hsein; Andrus, Ronald D.; Jiang, Tao; and Chen, Caroline J., "Probability-Based Liquefaction Evaluation Using Shear Wave Velocity Measurements" (2001). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 20.
https://scholarsmine.mst.edu/icrageesd/04icrageesd/session04/20
Included in
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.
