使用堆叠组合机器学习方法预测透混凝土的透性和压力强度
Fan Yu1,2, Wei Chu1,2, Rui Zhang3
1Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, Three Gorges University, Yichang, 443002, China.
Scientific reports
|July 2, 2025
概括
机器学习模型通过考虑孔隙结构而不仅仅是孔隙性来准确预测透的混凝土特性. 与传统方法相比,整体模型显著改善了透性和压力强度的预测.
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
- 数据科学数据科学数据科学
背景情况:
- 预测穿透混凝土的性能至关重要,但由于孔隙结构分析有限,目前的模型缺乏准确性.
- 现有的模型主要使用毛孔度,忽视其他重要的毛孔特性,导致预测不准确.
研究的目的:
- 开发基于机器学习的模型,用于预测透式混凝土的透性和压力强度.
- 通过结合多个孔隙结构参数来提高预测准确性.
主要方法:
- 使用六个独立模型构建了一个整体模型,包括多重线性回归和堆叠算法.
- 准备和测试了90个通透的混凝土样本,以创建初始数据集,然后用于模型培训.
主要成果:
- 集成组合模型实现了高预测准确性,R2值为0.925的透性和0.928的压力强度.
- 整体模型显示比单一模型预测有15.9-23.9%的改进,有效地克服了传统实证公式的局限性.
- 六个输入参数被证明是有效的,以实现高和简单的模型实现.
结论:
- 开发的整体模型在预测混凝土的透性和压力强度方面明显优于传统的实证配方.
- 穿透式混凝土的压力强度更多地受到孔径变化的影响,而不是透性变化.
- 机器学习提供了一种强大的方法来增强透式混凝土性能的预测能力.
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