Date of Award


Degree Name

MS in Civil and Environmental Engineering


Civil and Environmental Engineering


Robb Moss


Soil liquefaction, or loss of strength due to excess pore water pressures generated during dynamic loading, is a main cause of damage during earthquakes. When a soil liquefies (referred to as triggering), it may lose its ability to support overlying structures, deform vertically or laterally, or cause buoyant uplift of buried utilities. Empirical liquefaction models, used to predict liquefaction potential based upon in-situ soil index property measurements and anticipated level of seismic loading, are the standard of practice for assessing liquefaction triggering. However, many current models do not incorporate predictor variable uncertainty or do so in a limited fashion. Additionally, past model creation and validation lacks the same rigor found in predictive modeling in other fields.

This study examines the details of creating and validating an empirical liquefaction model, using the existing worldwide cone penetration test liquefaction database. Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic interpretation of model parameters. Our model is built using a hierarchal approach account for intra-event correlation in loading variables and differences in event sample sizes that mirrors the random/mixed effects models used in ground motion prediction equation development. The model is tested using an independent set of case histories from recent New Zealand earthquakes, and performance metrics are reported.

We found that a Bayesian measurement error model considering two predictor variables, qc,1 and CSR, decreases model uncertainty while maintaining predictive utility for new data. Two forms of model uncertainty were considered – the spread of probabilities predicted by mean values of regression coefficients (apparent uncertainty) and the standard deviations of the predictive distributions from fully probabilistic inference. Additionally, we found models considering friction ratio as a predictor variable performed worse than the two variable case and will require more data or informative priors to be adequately estimated.