Location
San Diego, California
Presentation Date
27 May 2010, 7:30 pm - 9:00 pm
Abstract
Empirical Liquefaction Models (ELMs) are the usual approach for predicting the occurrence of soil liquefaction. These ELMs 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. The deterministic model provides a “yes/no” response to the question of whether or not a site will liquefy. However, Performance-Based Earthquake Engineering (PBEE) requires an estimate of the probability of liquefaction (PL) which is a quantitative and continuous measure of the severity of liquefaction. Probabilistic models are better suited for PBEE but are still not consistently used in routine engineering applications. This is primarily due to the limited guidance regarding which model to use, and the difficulty in interpreting the resulting probabilities. The practical implementation of a probabilistic model requires a threshold of liquefaction (THL). The researchers who have used probabilistic methods have either come up with subjective THL or have used the established deterministic curves to develop the THL. In this study, we compare the predictive performance of the various deterministic and probabilistic ELMs within a quantitative validation framework. We incorporate estimated costs associated with risk as well as with risk mitigation to interpret PL using precision and recall and to, compute the optimal THL using Precision- Recall (P-R) cost curve. We also provide the P-R cost curves for the popular probabilistic model developed using Bayesian updating for SPT and CPT data by Cetin et al. (2004) and Moss et al. (2006) respectively. These curves should be immediately useful to a geotechnical engineer who needs to choose the optimal THL that incorporates the costs associated with the risk of liquefaction and the costs associated with mitigation.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
5th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
Missouri University of Science and Technology
Document Version
Final Version
Rights
© 2010 Missouri University of Science and Technology, 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
Oommen, Thomas and Baise, Laurie G., "A Practical Approach for Implementing the Probability of Liquefaction in Performance Based Design" (2010). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 1.
https://scholarsmine.mst.edu/icrageesd/05icrageesd/session09/1
Included in
A Practical Approach for Implementing the Probability of Liquefaction in Performance Based Design
San Diego, California
Empirical Liquefaction Models (ELMs) are the usual approach for predicting the occurrence of soil liquefaction. These ELMs 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. The deterministic model provides a “yes/no” response to the question of whether or not a site will liquefy. However, Performance-Based Earthquake Engineering (PBEE) requires an estimate of the probability of liquefaction (PL) which is a quantitative and continuous measure of the severity of liquefaction. Probabilistic models are better suited for PBEE but are still not consistently used in routine engineering applications. This is primarily due to the limited guidance regarding which model to use, and the difficulty in interpreting the resulting probabilities. The practical implementation of a probabilistic model requires a threshold of liquefaction (THL). The researchers who have used probabilistic methods have either come up with subjective THL or have used the established deterministic curves to develop the THL. In this study, we compare the predictive performance of the various deterministic and probabilistic ELMs within a quantitative validation framework. We incorporate estimated costs associated with risk as well as with risk mitigation to interpret PL using precision and recall and to, compute the optimal THL using Precision- Recall (P-R) cost curve. We also provide the P-R cost curves for the popular probabilistic model developed using Bayesian updating for SPT and CPT data by Cetin et al. (2004) and Moss et al. (2006) respectively. These curves should be immediately useful to a geotechnical engineer who needs to choose the optimal THL that incorporates the costs associated with the risk of liquefaction and the costs associated with mitigation.
