综合数据集用于SCM混凝土混凝土的性能评估
Weilin Liu1, Huadong Mo2, Chi King Lee1
1School of Engineering and Technology, University of New South Wales, Canberra, Australia.
Data in brief
|June 12, 2025
概括
补充性水泥材料 (SCM) 减少了混凝土的碳足迹. 一个新的SCM混凝土混凝土全球数据集帮助机器学习模型实现可持续的高性能混合物.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 补充性水泥材料 (SCM) 通过取代波特兰水泥来减少混凝土对环境的影响至关重要.
- 由于它们的pozzolanic反应,SCM提高了混凝土的机械性能和耐用性.
- 机器学习 (ML) 为预测具体属性的实验方法提供了一个有效的替代方案.
研究的目的:
- 编制一个全面的,全球数据集的SCM混合混凝土属性.
- 克服现有的特定区域数据集的局限性.
- 促进使用ML开发可持续和高性能混凝土混合物.
主要方法:
- 汇编了来自27个国家的136个出版物的数据.
- 包括包含飞灰 (FA),烟 (SF) 和磨砂颗粒高炉渣 (GGBFS) 的混凝土.
- 专注于机械性能和耐用性.
主要成果:
- 创建了一个广泛的数据集,涵盖了各种地理和环境条件.
- 该数据集包括机械性能和耐用性指标.
- 为ML模型培训和验证提供了宝贵的资源.
结论:
- 编制的数据集解决了特定区域数据的局限性.
- 它可以在全球范围内对SCM混合混凝土进行更准确的基于ML的预测.
- 促进可持续发展和高性能混凝土技术的进步.
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