Assessment of asphalt pavement remaining life using artificial neural network modelling

Loizos, A; Georgiou, P; Plati, C

dc.contributor.author	Loizos, A	en
dc.contributor.author	Georgiou, P	en
dc.contributor.author	Plati, C	en
dc.date.accessioned	2014-03-01T02:51:03Z
dc.date.available	2014-03-01T02:51:03Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35321
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-74049153553&partnerID=40&md5=896ea134bb620b21baa33eb26483ddbd	en
dc.subject.other	Computational technique	en
dc.subject.other	Computational tools	en
dc.subject.other	Developed model	en
dc.subject.other	Environmental aspects	en
dc.subject.other	Falling weight deflectometer (FWD)	en
dc.subject.other	Field data	en
dc.subject.other	Ground penetrating radar (GPR)	en
dc.subject.other	Highway networks	en
dc.subject.other	Mechanical characteristics	en
dc.subject.other	Pavement material	en
dc.subject.other	Real-time data	en
dc.subject.other	Remaining life	en
dc.subject.other	Road section	en
dc.subject.other	Traffic loads	en
dc.subject.other	Ground penetrating radar systems	en
dc.subject.other	Highway engineering	en
dc.subject.other	Neural networks	en
dc.subject.other	Stiffness	en
dc.subject.other	Soil mechanics	en
dc.title	Assessment of asphalt pavement remaining life using artificial neural network modelling	en
heal.type	conferenceItem	en
heal.publicationDate	2007	en
heal.abstract	The pavement remaining life (RL) is mainly dependent on pavement materials and mechanical characteristics, traffic load and several environmental aspects. The optimum evaluation of such parameters is more than demanding for pavement engineers. In light of the above the present research work focuses on the evaluation of Asphalt Concrete (AC) layers stiffness using the Artificial Neural Networks (ANN) computational technique. Thus an ANN model is developed based on field data, which is gathered non-destructively from representative road sections of the Greek highway network using Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) measurements. The developed model, which successfully predicts AC layer stiffness, proves to be an attractive computational tool for rapidly analyzing real time data. © 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	1	en
dc.identifier.spage	993	en
dc.identifier.epage	1002	en