使用先进的机器学习,对回收聚合混凝土的裂纹抗拉强度进行基于物理的建模
Kennedy C Onyelowe1,2, Viroon Kamchoom3, Shadi Hanandeh4
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|February 28, 2025
概括
基于物理学的机器学习准确地预测了回收的混凝土固度. 克斯塔尔模型达到94%的准确性,其中水含量和粗粗的天然聚合物是最佳抗拉强度的关键因素.
科学领域:
- 混凝土技术 混凝土技术
- 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.
背景情况:
- 先进的机器学习 (ML) 与基于物理的建模 (PIM) 结合,为混凝土技术提供了一种强大的方法.
- 这种PIM-ML集成提高了混凝土结构的设计效率,可靠性和可持续性.
- 回收聚合混凝土 (RAC) 为可持续建筑提供了机会,但其特性需要准确的预测.
研究的目的:
- 开发和评估先进的机器学习模型,用于预测回收聚合混凝土的裂纹抗拉强度 (Fsp).
- 通过敏感性分析识别影响Fsp的关键混凝土组件.
- 建立一个可靠的预测工具,以优化RAC混合设计.
主要方法:
- 一项广泛的文献审查产生了257个回收聚合物混凝土记录的数据库.
- 数据库被分为培训 (80%) 和验证 (20%) 集.
- 使用Weka软件应用了五种先进的ML技术,其中Kstar模型表现出卓越的性能.
主要成果:
- 克斯塔尔模型实现了高精度 (R2=0.96,精度=94%) 的低误差 (RMSE/MAE=0.15 MPa).
- 灵敏度分析显示,含水量 (W) 40%和粗天然聚合物 (NCAg) 38%是影响最大的成分.
- 其他指标 (WI=0.99,NSE=0.96,KGE=0.96) 证实了Kstar模型的效率和可靠性.
结论:
- 基于物理学的机器学习,特别是Kstar模型,提供了一个非常准确和可靠的方法来预测回收聚合物混凝土的分裂抗拉强度.
- 精心管理含水量和考虑粗质天然聚合物对于优化RAC的抗拉强度和可持续性至关重要.
- 开发的PIM-ML方法可以彻底改变建筑中的混凝土材料的工程,测试和利用.
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