Estimating local pavement performance and remaining service interval using neural networks-based models and automation tool

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dc.authoridCitir, Nazik/0000-0001-5163-7421
dc.authoridKAYA, ORHAN/0000-0001-6072-3882
dc.contributor.authorCitir, Nazik
dc.contributor.authorKaya, Orhan
dc.contributor.authorCeylan, Halil
dc.contributor.authorKim, Sunghwan
dc.contributor.authorWaid, Danny
dc.date.accessioned2025-01-06T17:36:28Z
dc.date.available2025-01-06T17:36:28Z
dc.date.issued2024
dc.description.abstractThis study introduces an integrated approach to enhance county pavement management, emphasising operational efficiency in determining the Remaining Service Interval (RSI) for rigid and flexible pavements. It establishes a robust methodology for systematically processing raw county road data through dynamic segmentation and summarisation to create a structured pavement database. It also incorporates innovative approaches and input configurations in employing Artificial Neural Networks (ANNs) to predict current and future county pavement performance indicators, including International Roughness Index (IRI), rutting, transverse, and longitudinal cracks, even with limited data. Evaluation of the ANN models on independent county road databases exhibited high prediction accuracies (0.86 < R-2 < 0.99), varying with specific performance indicators. The study results in an automation tool for expediting road performance estimation over multiple years. This tool seamlessly integrates the ANN models, empowering county engineers to make data-driven decisions and optimise resource allocation for effective pavement management, achieving significant cost savings.
dc.description.sponsorshipIowa Department of Transportation; Iowa Highway Research Board; Iowa County Engineers Service Bureau (ICEASB)
dc.description.sponsorshipThe authors gratefully acknowledge the Iowa Highway Research Board and Iowa County Engineers Service Bureau (ICEASB) for supporting this study. The project technical advisory committee (TAC) members from ICEASB, including Lee Bjerke, Zach Gunsolley, Todd Kinney, Mark Nahra, John Riherd, Brad Skinner, Jacob Thorius, and Mark Murphy from Iowa DOT, are gratefully acknowledged for their guidance, support, and direction throughout the research. Special thanks are expressed to Steve De Vries and Danny Waid, who developed the original concept of this study. The authors would also like to express their sincere gratitude to other research team members from Iowa State University's Program for Sustainable Pavement Engineering & Research (PROSPER) for their assistance.
dc.identifier.doi10.1080/14680629.2023.2294468
dc.identifier.endpage2035
dc.identifier.issn1468-0629
dc.identifier.issn2164-7402
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85180193414
dc.identifier.scopusqualityQ1
dc.identifier.startpage2001
dc.identifier.urihttps://doi.org/10.1080/14680629.2023.2294468
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1896
dc.identifier.volume25
dc.identifier.wosWOS:001128584700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofRoad Materials and Pavement Design
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial neural networks
dc.subjectperformance prediction
dc.subjectcounty pavement systems
dc.subjectremaining service interval
dc.subjectpavement management systems
dc.subjectdistress
dc.titleEstimating local pavement performance and remaining service interval using neural networks-based models and automation tool
dc.typeArticle

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