A Bayesian duration estimation of crack initiation prediction in in-service pavements

Karlaftis, MG; Loizos, A

dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Loizos, A	en
dc.date.accessioned	2014-03-01T02:51:00Z
dc.date.available	2014-03-01T02:51:00Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35290
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84858031634&partnerID=40&md5=7071cc32533be7a21d7f764d0974a50e	en
dc.subject.other	Bayesian inference	en
dc.subject.other	Climatic factors	en
dc.subject.other	Duration models	en
dc.subject.other	Estimated parameter	en
dc.subject.other	European Countries	en
dc.subject.other	Functional forms	en
dc.subject.other	Log-logistic	en
dc.subject.other	Mechanistic models	en
dc.subject.other	Mechanistic-empirical model	en
dc.subject.other	Model specifications	en
dc.subject.other	Parameter uncertainty	en
dc.subject.other	Pavement distress	en
dc.subject.other	Pavement engineering	en
dc.subject.other	Statistical models	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Estimation	en
dc.subject.other	Highway engineering	en
dc.subject.other	Inference engines	en
dc.subject.other	Soil mechanics	en
dc.subject.other	Stochastic models	en
dc.subject.other	Pavements	en
dc.title	A Bayesian duration estimation of crack initiation prediction in in-service pavements	en
heal.type	conferenceItem	en
heal.publicationDate	2007	en
heal.abstract	Research has recently concentrated on modeling and predicting pavement distress and deterioration; this research has, almost exclusively, revolved around mechanistic-empirical models that place restrictions on estimated parameters compromising performance. Recent computational advances enable the estimation of complex and computationally cumbersome statistical models with two very attractive properties: i. they are based on explicit mechanistic models that stem directly from pavement engineering practice, and ii. estimation and interpretation is straightforward, transparent, and tractable. We address here the problem of pavement failure times on the basis of data collected from in-service pavements in 15 European countries using Bayesian stochastic duration models that account for both parameter uncertainty and model specification uncertainty. Results indicate that, as expected, construction, traffic and climatic factors affect pavement distress; further, the loglogistic functional form estimated via the Bayesian Inference we propose, describes distress initiation better than existing approaches. © 2007 Taylor & Francis Group, London.	en
heal.journalName	Advanced Characterisation of Pavement and Soil Engineering Materials - Proceedings of the International Conference on Advanced Characterisation of Pavement and Soil Engineering Materials	en
dc.identifier.volume	2	en
dc.identifier.spage	1209	en
dc.identifier.epage	1220	en