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Activity Number:
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334
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Type:
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Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #306203 |
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Title:
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Bayesian Degradation Modeling with Covariate-Dependent Box-Cox Transformation of the Response Variable
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Author(s):
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Fridtjof Thomas*+ and Arzu Onar and Bouzid Choubane and Tom Byron
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Companies:
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VTI and St. Jude Children's Research Hospital and Florida Department of Transportation and Florida Department of Transportation
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Address:
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Tunagatan 13 D 4tr, Borlange, 78434, Sweden
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Keywords:
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accelerated testing ; mixed-effects models ; MCMC ; pavements ; transportation engineering
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Abstract:
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We discuss Bayesian degradation models that were developed for flexible road pavements based on accelerated pavement testing with the Heavy Vehicle Simulator (HVS). The models are fitted to data from the Florida Department of Transportation where rutting performance of three pavements was tested under three temperature settings. The analysis utilizes 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.
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