IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

Pavement performance models play a vital role in any pavement management system. The Regional Government of Biscay (RGB) (Spain) manages a 1200 km road network and conducts pavement data collections, including the International Roughness Index (IRI) values. The aim of the paper is to develop an IRI...

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Autores: Pérez Acebo, Heriberto, Linares Unamunzaga, Alaitz, Rojí, Eduardo, Gonzalo Orden, Hernán
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/7651
Acesso em linha:http://hdl.handle.net/10259/7651
Access Level:acceso abierto
Palavra-chave:International Roughness Index
IRI
Pavement performance model
Pavement management system
Deterministic models
Pavement management
Deterioration models
Two-lane roads
Flexible pavements
Ingeniería civil
Transportes
Civil engineering
Transportation
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spelling IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation WorkPérez Acebo, HeribertoLinares Unamunzaga, AlaitzRojí, EduardoGonzalo Orden, HernánInternational Roughness IndexIRIPavement performance modelPavement management systemDeterministic modelsPavement managementDeterioration modelsTwo-lane roadsFlexible pavementsIngeniería civilTransportesCivil engineeringTransportationPavement performance models play a vital role in any pavement management system. The Regional Government of Biscay (RGB) (Spain) manages a 1200 km road network and conducts pavement data collections, including the International Roughness Index (IRI) values. The aim of the paper is to develop an IRI performance model for two-lane roads with flexible pavement until the first maintenance and/or rehabilitation activity is performed. Due to the huge amount of available information, a deterministic model was selected. A literature review of deterministic models showed that, apart from age and traffic volumes, the pavement structure is a key factor. Therefore, it was decided to analyze the only road stretches whose entire pavement section was known (surface layer + base + subbase). Various variables related to age, traffic volumes and employed materials were introduced as possible factors. The multiple linear regression model with the highest coefficient of determination and all the variables significant included the real pavement age, the cumulated heavy traffic and the total thickness of bituminous layers. As the material employed in the surface layer could affect roughness progression, a qualitative variable was introduced to consider various surface materials. The model improved its accuracy, indicating that the surface layer material is also an influencing factor on IRI evolution.MDPI202320232020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/7651reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésCoatings. 2020, V. 10, n. 2, 97https://doi.org/10.3390/coatings10020097Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/76512026-05-28T07:56:11Z
dc.title.none.fl_str_mv IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
title IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
spellingShingle IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
Pérez Acebo, Heriberto
International Roughness Index
IRI
Pavement performance model
Pavement management system
Deterministic models
Pavement management
Deterioration models
Two-lane roads
Flexible pavements
Ingeniería civil
Transportes
Civil engineering
Transportation
title_short IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
title_full IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
title_fullStr IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
title_full_unstemmed IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
title_sort IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work
dc.creator.none.fl_str_mv Pérez Acebo, Heriberto
Linares Unamunzaga, Alaitz
Rojí, Eduardo
Gonzalo Orden, Hernán
author Pérez Acebo, Heriberto
author_facet Pérez Acebo, Heriberto
Linares Unamunzaga, Alaitz
Rojí, Eduardo
Gonzalo Orden, Hernán
author_role author
author2 Linares Unamunzaga, Alaitz
Rojí, Eduardo
Gonzalo Orden, Hernán
author2_role author
author
author
dc.subject.none.fl_str_mv International Roughness Index
IRI
Pavement performance model
Pavement management system
Deterministic models
Pavement management
Deterioration models
Two-lane roads
Flexible pavements
Ingeniería civil
Transportes
Civil engineering
Transportation
topic International Roughness Index
IRI
Pavement performance model
Pavement management system
Deterministic models
Pavement management
Deterioration models
Two-lane roads
Flexible pavements
Ingeniería civil
Transportes
Civil engineering
Transportation
description Pavement performance models play a vital role in any pavement management system. The Regional Government of Biscay (RGB) (Spain) manages a 1200 km road network and conducts pavement data collections, including the International Roughness Index (IRI) values. The aim of the paper is to develop an IRI performance model for two-lane roads with flexible pavement until the first maintenance and/or rehabilitation activity is performed. Due to the huge amount of available information, a deterministic model was selected. A literature review of deterministic models showed that, apart from age and traffic volumes, the pavement structure is a key factor. Therefore, it was decided to analyze the only road stretches whose entire pavement section was known (surface layer + base + subbase). Various variables related to age, traffic volumes and employed materials were introduced as possible factors. The multiple linear regression model with the highest coefficient of determination and all the variables significant included the real pavement age, the cumulated heavy traffic and the total thickness of bituminous layers. As the material employed in the surface layer could affect roughness progression, a qualitative variable was introduced to consider various surface materials. The model improved its accuracy, indicating that the surface layer material is also an influencing factor on IRI evolution.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/7651
url http://hdl.handle.net/10259/7651
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Coatings. 2020, V. 10, n. 2, 97
https://doi.org/10.3390/coatings10020097
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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