混合生成组合方法用于预测回收聚合物的混凝土强度特性
Paul O Awoyera1, Lenganji Simwanda2, Milica V Vasić3
1Department of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 34754, Saudi Arabia. pawoyera@pmu.edu.sa.
Scientific reports
|March 5, 2026
概括
本研究引入了一种混合生成集框架,用于预测回收聚合物的混凝土性能. 梯度增强和支持向量回归准确预测机械性能,指导可持续的混凝土混合设计.
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
- 可持续建筑 可持续建筑
背景情况:
- 回收聚合混凝土 (RAC) 对于可持续建筑至关重要.
- 从混合物比例预测RAC的机械性能是具有挑战性的,因为数据的限制.
- 准确的属性预测对于可靠的结构应用至关重要.
研究的目的:
- 开发一种混合生成集框架,用于预测RAC的关键机械性质.
- 在RAC混合设计中解决数据稀缺问题.
- 为了确定影响RAC特性的主导混合比例变量.
主要方法:
- 使用条件变异自编码器 (CVAE) 来生成合成数据,增加了112个RAC混合的数据库.
- 训练和验证了7个监督学习算法,包括梯度增强和支持向量回归.
- 进行了特征归因分析,以确定混合比例对机械性能的影响.
主要成果:
- 混合框架准确地预测了压力强度,分裂拉力强度,屈曲强度和弹性模量.
- 与基线模型相比,梯度增强和支持向量回归显示出优异的预测性能和稳定性.
- 与结合物相关的变量被确定为强度的主要驱动因素,而与聚合物相关的变量影响了度.
结论:
- 拟议的框架提供了一种数据驱动的方法,用于有效选RAC混合物.
- 这些发现为优化可持续RAC混合设计提供了可解释的见解.
- 这种方法提高了RAC在建筑中的实际应用.
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