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Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks

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Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks

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dc.contributor.author Llopis-Castelló, David es_ES
dc.contributor.author Paredes Palacios, Roberto es_ES
dc.contributor.author Parreño-Lara, Mario es_ES
dc.contributor.author García-Segura, Tatiana es_ES
dc.contributor.author Pellicer, Eugenio es_ES
dc.date.accessioned 2022-11-17T19:01:52Z
dc.date.available 2022-11-17T19:01:52Z
dc.date.issued 2021-12-01 es_ES
dc.identifier.uri http://hdl.handle.net/10251/189868
dc.description.abstract [EN] Pavement condition assessment is a critical step in road pavement management. In contrast to the automatic and objective methods used for rural roads, the most commonly used method in urban areas is the development of visual surveys usually filled out by technicians that leads to a subjective pavement assessment. While most previous studies on automatic identification of distresses focused on crack detection, this research aims not only to cover the identification and classification of multiple urban flexible pavement distresses (longitudinal and transverse cracking, alligator cracking, raveling, potholes, and patching), but also to quantify them through the application of Convolutional Neural Networks. Additionally, this study also proposes a methodology for an automatic pavement assessment considering the different stages developed in this research. This methodology allows for a more efficient and reliable pavement assessment, minimizing the cost and time required by the current visual surveys. es_ES
dc.description.sponsorship The study presented in this paper is part of the research project titled SIMEPU Sistema Integral de Mantenimiento Eficiente de Pavimentos Urbanos, funded by the Spanish Ministries of Science and Innovation and Universities, as well as the European Regional Development Fund under Grant No. RTC-2017-6148-7. The authors also acknowledge the support of partner companies Pavasal Empresa Constructora, S.A. and CPS Infraestructuras, Movilidad y Medio Ambiente, S.L. and the Valencia City Council. es_ES
dc.language Inglés es_ES
dc.publisher American Society of Civil Engineers es_ES
dc.relation.ispartof Journal of Transportation Engineering, Part B: Pavements es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Pavement maintenance es_ES
dc.subject Pavement distress es_ES
dc.subject Deep learning es_ES
dc.subject Convolutional neural network es_ES
dc.subject Image processing es_ES
dc.subject.classification INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification PROYECTOS DE INGENIERIA es_ES
dc.title Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1061/JPEODX.0000321 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6148-7-AR//SISTEMA INTEGRAL DE MANTENIMIENTO EFICIENTE DE PAVIMENTOS URBANOS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Llopis-Castelló, D.; Paredes Palacios, R.; Parreño-Lara, M.; García-Segura, T.; Pellicer, E. (2021). Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks. Journal of Transportation Engineering, Part B: Pavements. 147(4):1-8. https://doi.org/10.1061/JPEODX.0000321 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1061/JPEODX.0000321 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 8 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 147 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 2573-5438 es_ES
dc.relation.pasarela S\445931 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES
dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


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