基于神经网络设计方法的FRP钢筋混凝土机械性能降解调查
Wenyuan Xu1, Wei Li1, Dayang Wang2
1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, 150040, China.
Scientific reports
|May 23, 2024
概括
这项研究研究了纤维增强聚合物 (FRP) 增强混凝土的融侵蚀. 一个CNN模型准确地预测了混凝土的耐用性,为材料性能提供了洞察力.
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
- 耐用性工程 耐用性工程
背景情况:
- 纤维增强聚合物 (FRP) 复合材料在混凝土结构中提供耐腐蚀性.
- 了解FRP钢筋混凝土在环境压力因素 (如冷解周期) 下的耐用性,对于基础设施的寿命至关重要.
研究的目的:
- 预测化学结合物侵蚀对四种FRP钢筋混凝土机械性能的影响.
- 评估人工智能 (AI) 模型的预测性能,特别是BP神经网络和卷积神经网络 (CNN),用于评估混凝土的耐用性.
- 为了确定FRP钢筋混凝土样品可承受的最大融周期.
主要方法:
- 在FRP增强混凝土样本上进行了快速融试验.
- 测量的主要性能指标:质量损失率,相对动态弹性模量,压力强度和曲能力.
- 从45个实验结果开发了一个数据库,并训练了BP和CNN的模型进行预测.
- 利用优越的CNN模型,根据质量损失和弹性模量预测耐用性.
主要成果:
- 与BP神经网络相比,卷积神经网络 (CNN) 模型在压力强度和屈曲能力方面表现出更高的预测准确性.
- 确定了冷解周期和FRP钢筋混凝土机械性能降解之间的相关性.
- 通过CNN模型成功预测了不同FRP钢筋混凝土样品的使用寿命 (最大结解周期).
结论:
- CNN模型提供了一种强大而准确的方法来预测FRP钢筋混凝土的耐用性,而FRP钢筋混凝土则经受融侵蚀.
- 该研究提供了一种新的方法来评估和预测FRP增强混凝土结构的长期性能和使用寿命.
- 这些发现有助于加强材料选择和设计策略,以在恶劣环境下为可持续基础设施提供支持.
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