Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning

[EN] Achieving a circular construction industry requires reducing waste, reusing materials, and recycling resources (the 3Rs). However, traditional methods for implementing these goals mostly depend on static strategies that do not adapt to changes in demand or resource availability. This paper prop...

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Autores: Guzmán, Eduardo, Tobon, Sandra, Torres, Marta, Andres, B.|||0000-0002-7920-7711
Tipo de documento: artigo
Data de publicação:2026
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:dnet:riunet______::f8892753c630cc2f3ea9f35b6c6f8dd3
Acesso em linha:https://riunet.upv.es/handle/10251/235273
Access Level:Acesso embargado
Palavra-chave:Circular Economy
Construction waste management
Sustainability
Deep Q-network
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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spelling Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement LearningGuzmán, EduardoTobon, SandraTorres, MartaAndres, B.|||0000-0002-7920-7711Circular EconomyConstruction waste managementSustainabilityDeep Q-network09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] Achieving a circular construction industry requires reducing waste, reusing materials, and recycling resources (the 3Rs). However, traditional methods for implementing these goals mostly depend on static strategies that do not adapt to changes in demand or resource availability. This paper proposes an approach that integrates Digital Twins (DTs) and Multi-Agent Reinforcement Learning (MARL) to optimize the 3Rs dynamically and collaboratively. The DTs continuously sim-ulate construction processes, enabling real-time monitoring of waste generation, costs, and energy consumption. Meanwhile, multiple agents learn actions, via deep reinforcement learning, that jointly minimize material waste while balanc-ing cost and carbon footprint targets. The synergy between MARL and DT is demonstrated through a simulated scenario inwhicheachagentspecializesindifferentinterventions(e.g.,recycling,scheduling,logistics).Resultsshowthatthisintegratedapproachoutperformsbaselinestrategies,nointervention(No-Op)andrandomactions,significantlyreducingaveragewasteandimprovingrecyclingrates.Theseresultshighlightthepotentialofintelligent,data-drivenframeworkstoadvancesustainabilityintheconstructionindustry,pavingthewayforlarge-scaleadoptionofcirculareconomyprinciples.This research was funded by the project titled Gestión Integral de Materiales y Residuos en la Industria de la Construcción: Fomentando la Economía Circular mediante la Adopción de Inteligencia Artificial y Sistemas Inteligentes (ref. SI4/PJI/2024-00211). The project is supported by the Comunidad de Madrid through a direct grant agreement aimed at fostering and promoting research and technology transfer at the Universidad Autónoma de Madrid. Additionally, the research leading to these results received funding from the European Union Horizon Europe Programme with grant agreement No. 101147855 Intelligent and Sustainable Building Management powered by Cross-Sectoral Lifecycle (DATAWiSE).SpringerDepartamento de Organización de EmpresasCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyComunidad de MadridCOMISION DE LAS COMUNIDADES EUROPEARepositorio Institucional de la Universitat Politècnica de València Riunet20262026-02-0320262026-05-1920272027-02-03journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/235273reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 HE 101147855 Intelligent and Sustainable Building Management powered by Cross-Sectoral LifecycleCaja de Ahorros del Mediterráneo https://doi.org/10.13039/100012818 SI4%2FPJI%2F2024-00211 Gestión Integral de Materiales y Residuos en la Industria de la Construcción: Fomentando la Economía Circular mediante la Adopción de Inteligencia Artificial y Sistemas Inteligentesembargoed accesshttp://purl.org/coar/access_right/c_f1cfReserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/embargoedAccessoai:dnet:riunet______::f8892753c630cc2f3ea9f35b6c6f8dd32026-06-13T07:49:27Z
dc.title.none.fl_str_mv Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
title Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
spellingShingle Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
Guzmán, Eduardo
Circular Economy
Construction waste management
Sustainability
Deep Q-network
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
title_full Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
title_fullStr Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
title_full_unstemmed Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
title_sort Simulation-Based Optimization of the 3Rs in Circular Construction Using Integrated Digital Twins and Multi-agent Reinforcement Learning
dc.creator.none.fl_str_mv Guzmán, Eduardo
Tobon, Sandra
Torres, Marta
Andres, B.|||0000-0002-7920-7711
author Guzmán, Eduardo
author_facet Guzmán, Eduardo
Tobon, Sandra
Torres, Marta
Andres, B.|||0000-0002-7920-7711
author_role author
author2 Tobon, Sandra
Torres, Marta
Andres, B.|||0000-0002-7920-7711
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Organización de Empresas
Centro de Investigación en Gestión e Ingeniería de Producción
Escuela Politécnica Superior de Alcoy
Comunidad de Madrid
COMISION DE LAS COMUNIDADES EUROPEA
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Circular Economy
Construction waste management
Sustainability
Deep Q-network
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Circular Economy
Construction waste management
Sustainability
Deep Q-network
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] Achieving a circular construction industry requires reducing waste, reusing materials, and recycling resources (the 3Rs). However, traditional methods for implementing these goals mostly depend on static strategies that do not adapt to changes in demand or resource availability. This paper proposes an approach that integrates Digital Twins (DTs) and Multi-Agent Reinforcement Learning (MARL) to optimize the 3Rs dynamically and collaboratively. The DTs continuously sim-ulate construction processes, enabling real-time monitoring of waste generation, costs, and energy consumption. Meanwhile, multiple agents learn actions, via deep reinforcement learning, that jointly minimize material waste while balanc-ing cost and carbon footprint targets. The synergy between MARL and DT is demonstrated through a simulated scenario inwhicheachagentspecializesindifferentinterventions(e.g.,recycling,scheduling,logistics).Resultsshowthatthisintegratedapproachoutperformsbaselinestrategies,nointervention(No-Op)andrandomactions,significantlyreducingaveragewasteandimprovingrecyclingrates.Theseresultshighlightthepotentialofintelligent,data-drivenframeworkstoadvancesustainabilityintheconstructionindustry,pavingthewayforlarge-scaleadoptionofcirculareconomyprinciples.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-02-03
2026
2026-05-19
2027
2027-02-03
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/10251/235273
url https://riunet.upv.es/handle/10251/235273
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 HE 101147855 Intelligent and Sustainable Building Management powered by Cross-Sectoral Lifecycle
Caja de Ahorros del Mediterráneo https://doi.org/10.13039/100012818 SI4%2FPJI%2F2024-00211 Gestión Integral de Materiales y Residuos en la Industria de la Construcción: Fomentando la Economía Circular mediante la Adopción de Inteligencia Artificial y Sistemas Inteligentes
dc.rights.none.fl_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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
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