关于回收草混凝土的抗性研究,使用粒子群优化增强人工神经网络进行了研究
1College of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China.
Polymers
|November 27, 2024
概括
这项研究引入了回收吸管混凝土,通过冷解试验评估其抗性. 结果显示,预测模型基于混合比率准确预测业绩.
科学领域:
- 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.
- 可持续建筑 可持续建筑
背景情况:
- 回收材料,如颗粒和草粉,在建筑中提供可持续的替代品.
- 评估新型混凝土复合材料的抗解耐性对于在寒冷气候下耐用性至关重要.
研究的目的:
- 为了研究回收吸管混凝土的抗性.
- 分析影响混凝土在融条件下的性能的微观结构变化和机制.
- 开发一个可预测的模型,回收吸管混凝土的特性.
主要方法:
- 使用颗粒和粉制备回收草混凝土.
- 通过快速结方法进行融试验.
- 通过弹性相对动态模量,质量损失率和屈曲强度来评估抗性.
- 使用扫描电子显微镜 (SEM) 进行微观结构分析.
- 应用粒子优化 (PSO) 和遗传算法来优化BP神经网络用于预测建模.
主要成果:
- 在经过冷解周期后,通过各种物理和机械测试量化了回收草混凝土的抗性.
- 在SEM分析中,发现了混凝土在结-解后的微观结构变化.
- 优化的PSO-BP神经网络模型在预测曲强度和冷-解周期方面表现出高精度和稳定性.
- 该研究阐明了和草添加剂对混凝土抗性的影响和机制.
结论:
- 回收的草混凝土表现出可测量的抗性,受和草添加剂的影响.
- 微结构分析提供了对结解损伤和材料行为机制的见解.
- 开发的PSO-BP神经网络模型是一个可靠的工具,用于预测不同混合比例的回收吸管混凝土的性能.
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