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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::f8892753c630cc2f3ea9f35b6c6f8dd3 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235273 |
| Access Level: | acceso embargado |
| Palabra clave: | Circular Economy Construction waste management Sustainability Deep Q-network 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación |
| Sumario: | [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. |
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