Reincarnate D3.4 – Advanced valuation models for recycled materials

This deliverable presents advancements in evaluating recycled materials for high-performance construction, integrating these materials into sustainable building practices and supporting circular-economy goals. It introduces a large open-science dataset combining decades of laboratory tests with carbon-estimation methodologies, enabling prioritization of sustainable materials, particularly for concrete with reduced embodied CO₂. The core work experimentally and computationally validates new AI-driven design methods—adaptive design and zero-shot design—minimizing laboratory testing by using artificial intelligence to accelerate development of complex sustainable formulations. A major output is the SLAMD software, a digital laboratory twin reaching TRL-6, tested under realistic conditions to optimize material compositions to meet ecological and economic criteria. The report includes six case studies demonstrating application across Europe, such as recycled aggregates, sustainable asphalts, secondary cementitious materials, and mid-scale construction sites. Together, these results show the potential of AI-assisted design to transform sustainable construction practices.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-31
DOI
https://doi.org/10.5281/zenodo.17856793
Primary Topic
Recycled Aggregate Concrete Performance
Type
article
Field-Weighted Citation Impact
0.00
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article

Reincarnate D3.4 – Advanced valuation models for recycled materials

Zenodo (CERN European Organization for Nuclear Research)
Recycled Aggregate Concrete Performance
article

Reincarnate D3.4 – Advanced valuation models for recycled materials

article en

Abstract

This deliverable presents advancements in evaluating recycled materials for high-performance construction, integrating these materials into sustainable building practices and supporting circular-economy goals. It introduces a large open-science dataset combining decades of laboratory tests with carbon-estimation methodologies, enabling prioritization of sustainable materials, particularly for concrete with reduced embodied CO₂. The core work experimentally and computationally validates new AI-driven design methods—adaptive design and zero-shot design—minimizing laboratory testing by using artificial intelligence to accelerate development of complex sustainable formulations. A major output is the SLAMD software, a digital laboratory twin reaching TRL-6, tested under realistic conditions to optimize material compositions to meet ecological and economic criteria. The report includes six case studies demonstrating application across Europe, such as recycled aggregates, sustainable asphalts, secondary cementitious materials, and mid-scale construction sites. Together, these results show the potential of AI-assisted design to transform sustainable construction practices.

Zenodo (CERN European Organization for Nuclear Research)
Federal Institute For Materials Research and Testing (DE)
Openalex Percentile: Top 44%
Recycled Aggregate Concrete Performance
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