Probabilistic Liquefaction Triggering and Manifestation Models Based on Cumulative Absolute Velocity
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 148, Issue 3
Abstract
This study proposes the capacity cumulative absolute velocity () as a novel measure for the resistance of granular soils to earthquake-induced liquefaction. The is defined as the cumulative absolute velocity (CAV) needed to generate a threshold excess pore pressure ratio () value at a specified depth in a layer of liquefiable soil. A probabilistic model for predicting corresponding to values between 0.5 and 1.0 is developed using a database of over 280,000 estimates of from one-dimensional (1D), nonlinear, effective stress site-response analyses. These models provide as a function of depth, the presence and location of low-permeability interlayers in the soil profile, and soil stiffness (as reflected in the normalized cone tip resistance from cone penetration test results). The standard deviation around the model’s estimates of ranges from 0.59 natural log units for an threshold of 1.0 to 0.83 natural log units for an threshold of 0.5. Correlation models are provided for predicting throughout the depth of a soil profile, across multiple thresholds, or both. Finally, is implemented in a proposed modification of the liquefaction potential index (). The new index is validated using data from three earthquakes in Canterbury, New Zealand, and has a slightly improved predictive capability compared to existing indices, while making use of a relatively predictable intensity measure (i.e., CAV of the outcropping rock motion); this intensity measure is also compatible with performance-based methods for predicting liquefaction consequences. Finally, a guide for model implementation and examples of various applications are provided.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Support for this research was provided partly by the US Department of Education (Award no. P200A150042) and partly by the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. This work utilized the Summit supercomputer, which is supported by the National Science Foundation (Awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.
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Journal of Geotechnical and Geoenvironmental Engineering
Volume 148 • Issue 3 • March 2022
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© 2021 American Society of Civil Engineers.
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Received: May 13, 2021
Accepted: Oct 5, 2021
Published online: Dec 24, 2021
Published in print: Mar 1, 2022
Discussion open until: May 24, 2022
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