使用神经网络识别用花岩粉修饰的精选水泥复合材料的物理和机械性能
1Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland.
Materials (Basel, Switzerland)
|August 28, 2025
概括
这项研究使用人工神经网络开发了一个预测模型,以评估花岩粉的混凝土性能. 该模型准确地预测性能,通过利用废物材料支持可持续建筑.
科学领域:
- 材料科学
- 土木工程
- 人工智能
背景情况:
- 混凝土行业对可持续建筑材料的需求日益增长.
- 需要减少与水泥生产相关的二氧化碳排放.
- 探索废弃的副产品,如花岩粉作为补充性水泥材料 (SCM).
研究的目的:
- 开发一个可靠的预测模型,以测量石粉的水泥复合材料的物理和机械性能.
- 评估花岩粉作为部分水泥替代的可持续替代品.
- 在混凝土技术中利用机器学习进行多输出预测.
主要方法:
- 基于水泥的复合材料的实验测试,其中大约有30%的花岩粉.
- 专注于压力强度,粘合强度和包装密度.
- 开发和应用两个隐藏层 (10和15个神经元) 的多层感知器 (MLP) 人工神经网络.
主要成果:
- 对于所有测试属性,MLP模型实现了高预测精度 (R > 0.9,MAPE < 6%).
- 随着时间的推移,花岩粉的加入对混凝土的性能产生了积极的影响.
- 该模型的预测能力被验证为高达30%的花岩粉含量.
结论:
- 石英粉是一种可行的SCM,用于增强混凝土的性能和可持续性.
- 机器学习,特别是神经网络,对于优化混凝土复合设计是有效的.
- 这项研究促进了基于数据的方法来开发环保的建筑材料.
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