关于ECCs张力构成模型的机械性质和基于ANN的预测的实验研究
Qi Zhao1, Zhangfeng Yang1,2, Xiaofeng Zhang1
1College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China.
Polymers
|December 11, 2025
概括
使用聚乙烯或聚乙烯醇纤维的工程水泥复合材料 (ECC) 显示出增强的机械性能. 一个人工神经网络模型准确地预测了ECC拉伸行为,确定了纤维强度和含量作为关键因素.
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.
- 复合材料 复合材料 复合材料
背景情况:
- 传统的混凝土具有有限的承载能力和柔性.
- 工程水泥复合材料 (ECC) 提供优越的应变硬化和多重裂纹行为.
- ECC是具有显著潜力的高性能材料.
研究的目的:
- 分析不同纤维类型 (聚乙烯,聚乙烯醇) 和含量对ECC机械性能的影响.
- 通过使用SEM来研究界面行为和骨折形态.
- 使用人工神经网络 (ANN) 开发ECC拉伸行为的预测模型.
主要方法:
- 机械测试:压力,曲和单轴拉伸强度测试.
- 扫描电子显微镜 (SEM) 用于骨折表面分析.
- 使用十个输入参数来预测拉伸性质的ANN模型的开发.
主要成果:
- 接口结合显著影响骨折行为.
- 安恩模型准确地预测了第一个裂变应变,第一个裂变应变,最终应变和最终应变.
- 纤维拉伸强度和纤维含量被确定为最有影响力的因素.
结论:
- 使用PE和PVA纤维的ECC显示出更好的机械性能.
- 开发的ANN模型可靠地预测ECC拉伸行为.
- 该ANN方法解决了复杂的非线性行为传统构成模型的局限性.
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