Digital Circular Materials Intelligence Platform

1)    Context and ambition

Across Europe, the transition towards a circular economy is generating an unprecedented volume of data related to construction and demolition waste, industrial by-products, excavated soils, recycled materials and secondary raw materials. At the same time, digital transformation is accelerating the deployment of Building Information Modelling (BIM), Digital Twins, Industrial Data Spaces and smart asset management systems.

Despite this abundance of information, a major gap remains between the identification of available resources and their effective valorization into high-value products and materials. Most existing digital tools can quantify material stocks or map waste streams, but very few can transform this information into actionable recommendations for circular material development and industrial deployment.

The proposed project aims to bridge this gap through the development of a next-generation digital decision-support platform capable of connecting territorial and industrial data with advanced material science models and artificial intelligence. The ambition is to create a European framework allowing stakeholders to identify, assess and optimize circular valorization pathways for regional waste streams and secondary resources.

2)    Vision

The project seeks to establish a Digital Circular Materials Intelligence Platform capable of transforming heterogeneous industrial and territorial data into knowledge-driven recommendations for circular economy applications.

The platform will not only estimate available resource volumes but will also support decision-making by identifying the most relevant valorization pathways according to technical, environmental, economic and regulatory criteria.

By combining digital twins, BIM data, material databases, physics-based modelling and artificial intelligence, the project will create a unique decision engine dedicated to circular materials and industrial symbiosis.

The long-term vision is to provide regions, industries and public authorities with a digital tool capable of accelerating the transition from waste management to resource management.

3)    Scientific and technological challenge

Current approaches generally address resource management, material design and industrial decision-making separately. The proposed project aims to integrate these dimensions into a single digital ecosystem.

The central scientific challenge is to develop hybrid intelligence models combining:

·       Building Information Modelling (BIM);

·       Digital Twins;

·       Industrial and territorial databases;

·       Material science knowledge;

·       Micromechanical modelling;

·       Environmental assessment methodologies;

·       Physics-Informed Neural Networks (PINNs);

·       Neural operators and advanced machine learning approaches.

The objective is to create predictive models capable of linking resource characteristics to material performance and circular valorization opportunities.

Rather than relying solely on data-driven artificial intelligence, the platform will embed physical laws, material behavior models and engineering constraints directly into the learning process. This hybrid approach aims to improve robustness, explainability and industrial confidence in the recommendations produced by the system.

4)    Partners sought

The consortium is currently looking for partners willing to contribute expertise, data or pilot cases in one or more of the following areas:

·       BIM and Digital Twin technologies.

·       Artificial Intelligence and Machine Learning.

·       Physics-Informed Neural Networks and scientific computing.

·       Material science and engineering.

·       Circular economy and industrial symbiosis.

·       Construction and demolition waste management.

·       Industrial data platforms.

·       Life Cycle Assessment and environmental modelling.

·       Regional authorities and public agencies.

·       Clusters and innovation organizations.

5)    Expected impact

The project will contribute to the creation of a European digital infrastructure supporting circular economy decision-making.

By connecting resource availability, scientific knowledge and industrial deployment strategies, the platform will help regions transform waste streams into valuable resources, reduce dependency on virgin materials and accelerate the development of circular industrial ecosystems.

The proposed solution will support the objectives of RIVCircular by strengthening interregional cooperation, improving resource efficiency and enabling data-driven circular innovation across Europe.