用有限元和机器学习模型对使用内圆形钢管增强的长方形CFRP封闭混凝土柱进行参数研究
Haytham F Isleem1, Besukal Befikadu Zewudie2, Alireza Bahrami3
1School of Applied Technologies, Qujing Normal University, Qujing 655011, Yunnan, China.
Heliyon
|December 16, 2024
概括
钢管用碳纤维增强聚合物 (CFRP) 封闭的混凝土柱显示出增强的强度,特别是对于强度较低的混凝土. 机器学习模型准确地预测列行为,极端梯度增强和随机森林表现最好.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 结合碳纤维增强聚合物 (CFRP),混凝土和钢的柱子提供结构优势.
- 由于这些混合复合体柱的增强性质,研究兴趣越来越大.
- 了解这些柱子的轴负荷行为对于结构应用至关重要.
研究的目的:
- 为了研究圆角矩形CFRP封闭混凝土短柱的行为,内部具有高强度圆形钢管在轴承负载下.
- 分析混凝土等级,钢管厚度和尺寸以及CFRP厚度对柱子性能的影响.
- 开发和验证机器学习 (ML) 模型,用于预测最终轴负荷,轴应变和侧向应变.
主要方法:
- 使用非线性有限元素分析 (FEA),并根据实验数据进行验证.
- 进行了参数研究,以评估关键设计变量的影响.
- 开发和评估了多种机器学习模型,包括极端梯度增强,随机森林,人工神经网络和高斯过程回归.
主要成果:
- 与高强度混凝土相比,低强度混凝土经历了CFRP和钢材封闭的较大相对强度增强.
- 有限元模型准确地预测了列的行为.
- 与其他ML模型相比,极端梯度增强和随机森林模型在预测轴承负载,轴承应变和横向应变方面表现出卓越的准确性.
结论:
- 带有内部钢管的CFRP封闭混凝土柱提供了显著的结构优势,特别是对于强度较低的混凝土.
- 开发的ML模型为这些复合列的性能提供了可靠的预测.
- 机器学习,特别是极端梯度增强和随机森林,为预测先进结构元素的行为提供了强大的工具.
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