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The construction sector, accounting for 9% of global GDP, significantly impacts climate change and environmental degradation. It is responsible for 21% of global greenhouse gas emissions, 37% of carbon dioxide emissions, and 34% of energy consumption, while utilising 50% of extracted materials globally and generating 36% of Europes construction and demolition waste. To address these challenges, the European Union aims to decarbonize the building sector by 2050 through circular economy (CE) strategies such as reducing extraction, increasing recycling, and minimizing waste.
In Spain, where the circular material use rate is 8.9% (below the EU average of 11.7%), the España Circular 2030 strategy targets a 30% reduction in material use relative to GDP and a 10-million-ton cut in CO2; emissions by 2030. Achieving these goals requires integrating CE principles early in project design. Despite the critical role of early design decisions in shaping sustainability outcomes, circular and sustainable principles are rarely prioritized in this phase. Decisions are often driven by economic factors, neglecting the long-term environmental and social impacts across a building's lifecycle. Existing tools focus primarily on assessing environmental impacts but lack mechanisms to effectively prioritize sustainable alternatives.
This project aims to address these gaps by developing an innovative decision-support framework for the prioritization of building products in alignment with circular sustainability principles using advanced Machine Learning techniques. The framework will incorporate multi-criteria analysis, leveraging environmental, social, and economic indicators and will also integrate early-stage decision-making with life-cycle thinking, ensuring that material selection aligns with sustainability goals across the entire building lifecycle.
To enhance user engagement and decision-making, the framework will be integrated into virtual reality (VR) systems. This immersive technology will allow stakeholders to visualize and compare product choices in real time, facilitating a deeper understanding of sustainability trade-offs and enabling informed decisions. By integrating machine learning and advanced visualization techniques, this project offers a revolutionary approach to prioritizing and optimizing construction products. It aims to enable data-driven, immersive, and efficient decision-making processes aligned with sustainable development and circular economy principles. By promoting informed, circular, and sustainable choices during the design phase, the project will support the achievement of EU and national decarbonization targets while driving the transition to a low-carbon, circular construction industry.
ADMINISTRATIVE DETAILS
Project title: CIRCULAR1st – Smart Automation for Prioritising Sustainable and Circular Building Solutions in Early Design Stages
Principal Investigators (PIs): Dr. Nuria Forcada and Dr. Kàtia Gaspar
Beneficiary institution: Universitat Politècnica de Catalunya
Funding body: Agencia Estatal de Investigación
Funding programme: Programa estatal para la investigación y desarrollo experimental
Reference: PID2024-158844OB-I00
Funding: €147,500
Duration: 36 months
Start date: September 2025 (project period: September 2025 – August 2028)