基于空气孔分布参数的间距因子的估计,在空气引进的混凝土中
Jerzy Wawrzeńczyk1, Henryk Kowalczyk1
1Faculty of Civil Engineering and Architecture, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland.
Materials (Basel, Switzerland)
|May 7, 2025
概括
使用人工神经网络 (ANN) 分析空气拖动混凝土的和弦长度分布,可以提供更准确的间距因子 (L2) 估计. 这种方法与标准计算相对应,为具体的质量评估提供了实用方法.
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
- 混凝土技术 混凝土技术
背景情况:
- 标准EN-480-11定义了混凝土中的空气空隙特性.
- 普华斯的间距因子计算模型因没有考虑和弦长度分布而受到批评.
- 准确的间距因素估计对于混凝土的耐用性至关重要.
研究的目的:
- 为了调查分析和弦长度分布是否能改善间隔因子 (L2) 估计.
- 开发一个人工神经网络 (ANN) 模型来预测间距因子.
- 确定影响间距因素的关键空气空隙结构参数.
主要方法:
- 对110个空中拖混凝土数据集的分析.
- 人工神经网络 (ANN) 建模的应用.
- 输入参数包括和弦大小分布特征 (A,d,W),水泥糊含量 (P) 和大孔数量 (N5).
主要成果:
- 该ANN模型准确地估计了间距因子 (L2).
- 峰值振幅 (A),峰值宽度 (W) 和水泥糊含量 (P) 是最有影响力的因素.
- 在ANN模型结果和标准间距因子 (L2) 计算之间观察到强烈的相关性.
结论:
- 对和弦长度分布的分析提高了间距因子 (L2) 估计的准确性.
- 开发的ANN模型为计算间距因子提供了一种可靠的方法.
- 展示了在Excel中实现ANN模型的实用方法.
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