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Assessment of asphalt pavement remaining life using artificial neural network modelling

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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 http://hdl.handle.net/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


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