多变量机器学习框架用于预测混凝土的电阻力,使用和度和孔结构参数
Youngdae Kim1, Seong-Hoon Kee2, Cris Edward F Monjardin1,3
1School of Civil, Environmental and Geological Engineering, Mapua University, Manila 1102, Philippines.
Materials (Basel, Switzerland)
|January 28, 2026
概括
混凝土的表面电阻 (ER) 主要取决于和度 (DS),随着和度的增加而呈指数级下降. 孔隙结构也影响ER,使得坚固的,非破坏性的混凝土耐久性评估.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
背景情况:
- 表面电阻 (ER) 是混凝土水分状态和孔状结构的关键指标.
- 了解ER和混凝土性能之间的关系对于耐久性评估至关重要.
研究的目的:
- 研究水分和孔隙结构对混凝土表面电阻的影响.
- 使用材料参数和机器学习开发ER的预测模型.
主要方法:
- 在6个混凝土混合物的受控湿湿/干燥周期中持续测量ER.
- 机器学习模型的评估 (高斯过程回归,神经网络) 使用诸如和度 (DS),毛孔度 (P),水比 (WCR) 和压力强度 (f'c) 等参数.
- SHAP分析以确定参数影响和制定非线性多变量回归模型.
主要成果:
- 随着DS的增加,ER呈指数级下降 (R2 = 0.8960.997),证实DS是占主导地位的因素.
- 机器学习模型,特别是高斯过程回归和神经网络,当包括所有参数时,准确地预测了ER.
- SHAP分析强调DS是主要影响者,P和WCR提供次要贡献.
结论:
- 表面电阻与水分动态和混凝土孔隙结构密切相关.
- 综合实验计算方法为非破坏性混凝土耐久性评估提供了一个物理解释和强大的框架.
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