An approach for optimizing pavement design - redesign parameters in PPP projects

Loizos, A; Karlaftis, AG; Karlaftis, MG

dc.contributor.author	Loizos, A	en
dc.contributor.author	Karlaftis, AG	en
dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T01:25:53Z
dc.date.available	2014-03-01T01:25:53Z
dc.date.issued	2007	en
dc.identifier.issn	1573-2479	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17793
dc.subject	Neural networks	en
dc.subject	Pavement engineering	en
dc.subject	Tensile stresses	en
dc.subject	Unbound materials	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Correlation methods	en
dc.subject.other	Eigenvalues and eigenfunctions	en
dc.subject.other	Elasticity	en
dc.subject.other	Error analysis	en
dc.subject.other	Internet protocols	en
dc.subject.other	Models	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Pavement overlays	en
dc.subject.other	Roads and streets	en
dc.subject.other	Athens , Greece	en
dc.subject.other	Back analysis (BA)	en
dc.subject.other	BOT projects	en
dc.subject.other	Design parameters.	en
dc.subject.other	Elasticity modulus	en
dc.subject.other	External factors	en
dc.subject.other	Falling weight deflectometer (FWD)	en
dc.subject.other	Granular layers	en
dc.subject.other	Modulus of elasticity (MOE)	en
dc.subject.other	Non linear phenomenon	en
dc.subject.other	Parameter values	en
dc.subject.other	Pavement design	en
dc.subject.other	Pavement materials	en
dc.subject.other	Physical phenomenon	en
dc.subject.other	Practical implications	en
dc.subject.other	Predicted values	en
dc.subject.other	Relative estimation	en
dc.subject.other	Unbound materials	en
dc.subject.other	Widespread use	en
dc.subject.other	Pavements	en
dc.title	An approach for optimizing pavement design - redesign parameters in PPP projects	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/15732470500365554	en
heal.identifier.secondary	http://dx.doi.org/10.1080/15732470500365554	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Modeling the elasticity modulus of unbound granular pavement materials has attracted significant research interest because of its importance in pavement design particularly in PPP/BOT projects. These efforts have been hampered by three factors: (i) inability to capture the correlations between the asphalt and granular layers and the subgrade, (ii) inadequate modeling of the effects of external factors on the elasticity modulus of unbound materials, and (iii) widespread use of linear statistical relationships to model a complex and non-linear phenomenon. In this paper genetically optimized neural networks and falling weight deflectometer (FWD) back-analysis results from a newly constructed BOT project in Athens, Greece, are employed in order to evaluate pavement section design parameters. It is shown that parameter values adopted during design do not co-inside with those observed from the back-analysis studies. Further, the results indicate that the relative estimation error for the modulus of elasticity of the unbound material does not exceed 25%, while the correlation between actual and predicted values is 86%, both suggesting that the proposed approach models the physical phenomenon adequately, a finding with important practical implications particularly in PPP projects.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	Structure and Infrastructure Engineering	en
dc.identifier.doi	10.1080/15732470500365554	en
dc.identifier.isi	ISI:000247064500006	en
dc.identifier.volume	3	en
dc.identifier.issue	3	en
dc.identifier.spage	257	en
dc.identifier.epage	265	en