Show simple item record

dc.contributor.advisorMüller, Peter, 1963 August 9-en
dc.creatorSerigos, Pedro Antonio, M.S. in Statisticsen
dc.date.accessioned2015-11-16T19:07:13Zen
dc.date.available2015-11-16T19:07:13Zen
dc.date.issued2015-05en
dc.date.submittedMay 2015en
dc.identifierdoi:10.15781/T2CK70en
dc.identifier.urihttp://hdl.handle.net/2152/32500en
dc.descriptiontexten
dc.description.abstractA challenge currently faced by local, state and federal transportation agencies is the constantly increasing traffic demand, combined with a less increasing availability of funds for the maintenance of the highway infrastructure. A key factor for the success of a pavement management system is that it contains accurate and reliable pavement performance models. Inadequate prediction of the highway infrastructure future condition can lead to an inappropriately estimated budget or misallocation of funds. This study had the main objectives of quantifying the uncertainty of pavement performance model parameters and proposing a hierarchical model specification in order to account for heterogeneity across different subpopulations of pavements. The uncertainty of each pavement performance parameter was quantified by estimating their marginal posterior distribution using both a non-hierarchical and a hierarchical specification of the model. The posterior distribution of each model parameter was sampled using a combination of the Gibbs and Metropolis-Hastings techniques. The hierarchical model was specified in order to capture the different damaging effect that environmental factors and traffic characteristics have on pavements between the subpopulations with thinner and thicker hot-mix asphalt layer. The results from the study showed a significant dispersion of the pavement performance parameters. In addition, accounting for the heterogeneous effect between subpopulations resulted in a significant improvement of the fitting of the model as opposed to assuming complete pooling across pavement sections.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectPavementen
dc.subjectHeterogeneityen
dc.subjectAASHO road testen
dc.subjectBayesian hierarchical modellingen
dc.titleBayesian hierarchical modelling of pavement performanceen
dc.typeThesisen
dc.date.updated2015-11-16T19:07:13Zen
dc.contributor.committeeMemberProzzi, Jorge Aen
dc.description.departmentStatisticsen
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record