Incorporating the effectiveness of preservation and rehabilitation techniques on flexible pavement service life predictions using machine learning approach

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dc.contributor.authorCitir, Nazik
dc.contributor.authorCeylan, Halil
dc.contributor.authorKim, Sunghwan
dc.contributor.authorKaya, Orhan
dc.date.accessioned2025-01-06T17:29:56Z
dc.date.available2025-01-06T17:29:56Z
dc.date.issued2021
dc.descriptionThe Transportation and Development Institute (T and DI) of the American Society of Civil Engineers (ASCE)
dc.descriptionInternational Airfield and Highway Pavements 2021: Pavement Materials and Sustainability -- 8 June 2021 through 10 June 2021 -- Virtual, Online -- 169470
dc.description.abstractA flexible pavement network may be exposed to traffic changes over time because of detours that may cause unusually high traffic loading on some parts of the pavement sections. Such sections, therefore, need to be preserved earlier than their expected service life. This study focuses on incorporating the effectiveness of preservation and rehabilitation techniques, e.g., functional thin overlay and structural asphalt overlay on flexible pavement service life predictions using a machine learning approach. The developed models are capable of estimating the current and future international roughness index (IRI) and of detecting performance recovery after application of preservation treatment, thereby forecasting the remaining service life. The traffic, pavement structural, and performance data were obtained from the pavement management information system (PMIS) of the Iowa Department of Transportation (DOT). This study describes realistic what-if scenarios considering various treatment techniques to provide a better understanding of the effectiveness of treatments on extending flexible pavement service life. Such outcomes will help facilitate better pavement preservation management strategies. © ASCE.All right reserved.
dc.identifier.doi10.1061/9780784483510.033
dc.identifier.endpage377
dc.identifier.isbn978-078448351-0
dc.identifier.scopus2-s2.0-85108079631
dc.identifier.startpage365
dc.identifier.urihttps://doi.org/10.1061/9780784483510.033
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1401
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAmerican Society of Civil Engineers (ASCE)
dc.relation.ispartofAirfield and Highway Pavements 2021: Pavement Materials and Sustainability - Selected Papers from the International Airfield and Highway Pavements Conference 2021
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectForecasting
dc.subjectHighway administration
dc.subjectInformation management
dc.subjectMachine learning
dc.subjectPredictive analytics
dc.subjectService life
dc.subjectSustainable development
dc.subjectTraffic surveys
dc.subjectTuring machines
dc.subjectDepartment of Transportation
dc.subjectInternational roughness index
dc.subjectMachine learning approaches
dc.subjectPavement management information systems
dc.subjectPavement preservation
dc.subjectPerformance recovery
dc.subjectRehabilitation techniques
dc.subjectTreatment techniques
dc.subjectPavements
dc.titleIncorporating the effectiveness of preservation and rehabilitation techniques on flexible pavement service life predictions using machine learning approach
dc.typeConference Object

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