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# BOxCrete: Bayesian Optimization for Sustainable Concrete Mix Design
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Concrete, the second most widely used material in the world, accounts for **6–8% of global anthropogenic CO₂ emissions**, largely due to Portland cement production (~0.8 tons CO₂ per ton of cement). Partial replacement with Supplementary Cementitious Materials (SCMs) such as fly ash, slag, and natural pozzolan reduces embodied carbon and often improves durability, but high SCM usage makes compressive strength a highly nonlinear function of multiple interacting mix parameters, rendering traditional design empirical and trial-and-error driven. To systematically navigate this complex composition space, data-driven frameworks are needed.
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> 🌐 **[Try the interactive explorer →](https://facebookresearch.github.io/SustainableConcrete/)** — predict concrete strength from mix composition in your browser, no installation needed.
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Concrete, the second most widely used material in the world, accounts for **6–8% of global anthropogenic CO₂ emissions**, largely due to Portland cement production (~0.8 tons CO₂ per ton of cement).
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Here, we introduce BOxCrete, an open-source Bayesian optimization framework for probabilistic strength curve prediction and sustainable mix design.
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We invite researchers and practitioners from all disciplines including AI, machine learning, computer science, materials science, and civil engineering
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to collaborate on discovering more sustainable concrete formulations that are applicable
<metaname="description" content="Interactive Bayesian Optimization explorer for sustainable concrete: predict 28-day compressive strength, GWP, and cost from mix composition in your browser.">
<metaproperty="og:description" content="Predict concrete compressive strength, GWP, and cost from mix composition. A Bayesian Optimization model running entirely in your browser.">
<metaproperty="og:image:alt" content="BOxCrete: a close-up photograph of fresh concrete, used as the social-share thumbnail for the interactive explorer.">
<metaname="twitter:description" content="Predict concrete compressive strength, GWP, and cost from mix composition. A Bayesian Optimization model running entirely in your browser.">
"description": "Bayesian Optimization model for predicting concrete compressive strength, global warming potential, and cost from mix composition. Runs entirely in the browser.",
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"applicationCategory": "EngineeringApplication",
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"operatingSystem": "Any",
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"browserRequirements": "Requires JavaScript and a modern browser",
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"offers": {
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"@type": "Offer",
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"price": "0",
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"priceCurrency": "USD"
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},
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"author": [
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{"@type": "Person","name": "Bayezid Baten"},
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{"@type": "Person","name": "M. Ayyan Iqbal"},
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{"@type": "Person","name": "Sebastian Ament"},
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{"@type": "Person","name": "Julius Kusuma"},
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{"@type": "Person","name": "Nishant Garg"}
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],
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"citation": [
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{
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"@type": "ScholarlyArticle",
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"name": "BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization",
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"url": "https://arxiv.org/abs/2603.21525"
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},
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{
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"@type": "ScholarlyArticle",
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"name": "Sustainable Concrete via Bayesian Optimization",
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