开源模型BOxCrete用AI预测混凝土强度并优化配比，兼顾性能与碳排放。
BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization
- 基于高斯过程回归建模，融合多龄期强度数据进行概率预测
- 平均决定系数R²达0.94，均方根误差仅0.69 ksi
- 支持多目标优化，适合材料研发与绿色建筑领域研究者
现代混凝土需同时满足力学性能、工作性、耐久性和可持续性要求，导致配合比设计日益复杂。近年来，人工智能与机器学习模型在预测抗压强度和指导配合比优化方面展现出潜力，但多数研究依赖专有工业数据集和闭源实现。本文提出BOxCrete，一个开源的概率建模与优化框架，基于包含超过500组强度测量值（1–15 ksi）的新开放数据集训练，涵盖123种配合比（69种砂浆、54种混凝土），在五个养护龄期（1、3、5、14和28天）下测试。BOxCrete采用高斯过程（GP）回归预测强度发展，平均R²=0.94，RMSE=0.69 ksi，可量化不确定性，并实现抗压强度与碳足迹的多目标优化。该数据集与模型为基于AI的配合比优化设计提供了可复现的开源基础。
原文摘要 · Abstract (English)
Modern concrete must simultaneously satisfy evolving demands for mechanical performance, workability, durability, and sustainability, making mix designs increasingly complex. Recent studies leveraging Artificial Intelligence (AI) and Machine Learning (ML) models show promise for predicting compressive strength and guiding mix optimization, but most existing efforts are based on proprietary industrial datasets and closed-source implementations. Here we introduce BOxCrete, an open-source probabilistic modeling and optimization framework trained on a new open-access dataset of over 500 strength measurements (1-15 ksi) from 123 mixtures - 69 mortar and 54 concrete mixes tested at five curing ages (1, 3, 5, 14, and 28 days). BOxCrete leverages Gaussian Process (GP) regression to predict strength development, achieving average R$^2$ = 0.94 and RMSE = 0.69 ksi, quantify uncertainty, and carry out multi-objective optimization of compressive strength and embodied carbon. The dataset and model establish a reproducible open-source foundation for data-driven development of AI-based optimized mix designs.
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