Bayesian Degradation Modeling in Accelerated Pavement Testing with Estimated Transformation Parameter for the Response
Publication: Journal of Transportation Engineering
Volume 133, Issue 12
Abstract
We discuss Bayesian degradation models that were developed for flexible pavements based on accelerated pavement testing with the heavy vehicle simulator. The models are fitted to data from the Florida Department of Transportation, where rutting performance of three binder types was tested under three temperature settings. The analysis utilizes Bayesian linear mixed-effects models for longitudinal degradation data where the parameter estimates and their posterior marginal distributions are obtained via a Markov chain Monte Carlo (MCMC) technique. The linearity in this model is achieved by utilizing a covariate-dependent Box-Cox transformation of the response variable, where the transformation parameter is estimated as part of the modeling procedure. The paper illustrates the various forms of useful inference that can easily be obtained via the output from the MCMC chains and provides insights regarding the accelerated test experiment at hand. As expected, the results suggest that rut depth development is affected both by the binder type, as well as the test temperature. What is more, the conditional inference made possible by the Bayesian approach utilized here clearly demonstrates the dependence of the inference for the covariate effects on the value of the Box-Cox transformation parameter. Hence the transformation of the response variable is an important step in model building that has to be carefully considered.
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Acknowledgments
Part of the work on this project was made possible for the first author by a James W. McLamore Summer Research Grant from the University of Miami Research Council, and for the second author by financial support from the Swedish Road Administration and the Swedish Governmental Agency for Innovation Systems. The writers also acknowledge the insightful comments of the two referees and the associate editor, which led to an improved version of the manuscript.
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© 2007 ASCE.
History
Received: Dec 7, 2006
Accepted: Mar 21, 2007
Published online: Dec 1, 2007
Published in print: Dec 2007
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