Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag
arXiv:2606.06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry. Here, we curated the largest literature-derived alkali-activated slag (AAS) dataset to date, comprising over 3100 compressive strength records, 155 chemically distinct ground granulated blast-furnace slags (GGBSs), and 24 attributes incorporating precursor chemistry, fineness,
The development of a large, literature-derived dataset combined with AI techniques marks a significant advancement in material science for sustainable construction.
Improving the predictability and design of alkali-activated slag materials can accelerate the adoption of greener alternatives to traditional cement, impacting carbon emissions and infrastructure development.
This research provides a data-driven framework for optimizing sustainable cementitious materials, potentially reducing costs and environmental impact in construction.
- · Construction materials industry
- · Sustainable infrastructure developers
- · AI/ML in materials science
- · Traditional cement manufacturers (long-term)
- · Inefficient material design processes
More efficient and sustainable building materials will become available.
Reduced carbon footprint from the construction sector due to wider adoption of alternatives to Portland cement.
The development could spur new material science startups and accelerate research into other sustainable materials through AI-driven discovery.
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Read at arXiv cs.LG