Seismic liquefaction triggering correlations within a Bayesian framework

Date

2003-01-01

Author

Moss, RES
Çetin, Kemal Önder
Seed, RB

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Liquefaction is a broad term that describes a complex phenomenon where soil looses substantial strength, resulting in instability and strain potential. The complexity of the phenomenon makes analyzing the problem analytically intractable. Laboratory testing is important in determining trends and patterns, but cannot reproduce critical in situ soil characteristics (such as soil fabric and the effects of aging) that can dominate liquefaction. Therefore, in determining if a soil will liquefy under seismic loading, it is common practice to correlate in situ index data with evidence of liquefaction/non-liquefaction from previous seismic events. A Bayesian framework allows for careful and thorough treatment of all types of uncertainties associated with the vagaries of observed liquefaction/non-liquefaction. Using a statistical framework and parameter estimation technique of this type allows for the formulation and optimization of the model to be based on the underlying physics of the problem. This paper outlines procedures for parameter estimation using SPT (standard penetration test) and CPT (cone penetration test) data, and the development of probabilistic triggering correlations. The results are curves of equal probability of seismic liquefaction triggering within normalized load vs. resistance space, for SPT and CPT field measurements, which can be used in performance-based engineering decisions.

Subject Keywords

Bayesian, Uncertainty, Liquefaction, Seismic, Probability

URI

https://hdl.handle.net/11511/55591

Conference Name

9th International Conference on Applications of Statistics and Probability in Civil Engineering

Collections

Department of Civil Engineering, Conference / Seminar

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Citation Formats

R. Moss, K. Ö. Çetin, and R. Seed, “Seismic liquefaction triggering correlations within a Bayesian framework,” presented at the 9th International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, CA, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55591.