Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers

[EN] This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using L...

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Authors: Yepes-Bellver, Lorena|||0009-0002-8820-2979, Alcalá-González, Julián|||0000-0003-1376-8441, Yepes, V.|||0000-0001-5488-6001
Format: article
Publication Date:2025
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/230024
Online Access:https://riunet.upv.es/handle/10251/230024
Access Level:Open access
Keyword:Carbon footprint
Neural networks
Kriging
Sustainability
Post-tensioned bridges
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
id ES_d2126fbf12d46e5e118b261e19bbb803
oai_identifier_str oai:riunet.upv.es:10251/230024
network_acronym_str ES
network_name_str España
repository_id_str
spelling Predictive Modeling for Carbon Footprint Optimization of Prestressed Road FlyoversYepes-Bellver, Lorena|||0009-0002-8820-2979Alcalá-González, Julián|||0000-0003-1376-8441Yepes, V.|||0000-0001-5488-6001Carbon footprintNeural networksKrigingSustainabilityPost-tensioned bridges09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using Latin-hypercube sampling. Second, that response surface is optimized to identify design configurations with the lowest CO2 emissions. The optimal configuration (deck #37)¿base width 3.40 m, deck depth 1.10 m, and concrete grade C-35 MPa¿achieved a carbon footprint of 386,515 kg CO2, representing a reduction of 12% compared to the reference bridge. Among the models tested, the artificial neural network (ANN) achieved the highest predictive accuracy (RMSE = 8372 kg, MAE = 7356 kg), closely followed by the Kriging 1 model (RMSE = 9235 kg, MAE = 7236 kg). Results indicate that emissions remain minimal for deck depths between 1.10 and 1.30 m, base widths between 3.20 and 3.80 m, and concrete grades of C-35 to C-40 MPa. This study provides practical guidelines for reducing the carbon footprint of prestressed bridges and highlights the value of robust surrogate models in sustainable structural optimization.This research was funded by Grant PID2023-150003OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe .MDPI AGDepartamento de Ingeniería de la Construcción y de Proyectos de Ingeniería CivilDepartamento de Mecánica de los Medios Continuos y Teoría de EstructurasEscuela Técnica Superior de Ingeniería de Caminos, Canales y PuertosEscuela Técnica Superior de Ingeniería IndustrialInstituto Universitario de Investigación de Ciencia y Tecnología del HormigónAGENCIA ESTATAL DE INVESTIGACIONEuropean Regional Development FundRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-08-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/230024reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-150003OB-I00 OPTIMIZACION RESILIENTE DEL CICLO DE VIDA DE ESTRUCTURAS HIBRIDAS Y MODULARES DE ALTA EFICIENCIA SOCIAL Y MEDIOAMBIENTAL BAJO CONDICIONES EXTREMASopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2300242026-06-13T07:49:27Z
dc.title.none.fl_str_mv Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
title Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
spellingShingle Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
Yepes-Bellver, Lorena|||0009-0002-8820-2979
Carbon footprint
Neural networks
Kriging
Sustainability
Post-tensioned bridges
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
title_full Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
title_fullStr Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
title_full_unstemmed Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
title_sort Predictive Modeling for Carbon Footprint Optimization of Prestressed Road Flyovers
dc.creator.none.fl_str_mv Yepes-Bellver, Lorena|||0009-0002-8820-2979
Alcalá-González, Julián|||0000-0003-1376-8441
Yepes, V.|||0000-0001-5488-6001
author Yepes-Bellver, Lorena|||0009-0002-8820-2979
author_facet Yepes-Bellver, Lorena|||0009-0002-8820-2979
Alcalá-González, Julián|||0000-0003-1376-8441
Yepes, V.|||0000-0001-5488-6001
author_role author
author2 Alcalá-González, Julián|||0000-0003-1376-8441
Yepes, V.|||0000-0001-5488-6001
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de la Construcción y de Proyectos de Ingeniería Civil
Departamento de Mecánica de los Medios Continuos y Teoría de Estructuras
Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
Escuela Técnica Superior de Ingeniería Industrial
Instituto Universitario de Investigación de Ciencia y Tecnología del Hormigón
AGENCIA ESTATAL DE INVESTIGACION
European Regional Development Fund
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Carbon footprint
Neural networks
Kriging
Sustainability
Post-tensioned bridges
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Carbon footprint
Neural networks
Kriging
Sustainability
Post-tensioned bridges
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using Latin-hypercube sampling. Second, that response surface is optimized to identify design configurations with the lowest CO2 emissions. The optimal configuration (deck #37)¿base width 3.40 m, deck depth 1.10 m, and concrete grade C-35 MPa¿achieved a carbon footprint of 386,515 kg CO2, representing a reduction of 12% compared to the reference bridge. Among the models tested, the artificial neural network (ANN) achieved the highest predictive accuracy (RMSE = 8372 kg, MAE = 7356 kg), closely followed by the Kriging 1 model (RMSE = 9235 kg, MAE = 7236 kg). Results indicate that emissions remain minimal for deck depths between 1.10 and 1.30 m, base widths between 3.20 and 3.80 m, and concrete grades of C-35 to C-40 MPa. This study provides practical guidelines for reducing the carbon footprint of prestressed bridges and highlights the value of robust surrogate models in sustainable structural optimization.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/230024
url https://riunet.upv.es/handle/10251/230024
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-150003OB-I00 OPTIMIZACION RESILIENTE DEL CICLO DE VIDA DE ESTRUCTURAS HIBRIDAS Y MODULARES DE ALTA EFICIENCIA SOCIAL Y MEDIOAMBIENTAL BAJO CONDICIONES EXTREMAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
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 AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420318429282304
score 15,198674

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