数字和机器学习模拟GFRP封闭的混凝土-钢空心圆柱
Haytham F Isleem1, Tang Qiong2, Mostafa M Alsaadawi3,4
1School of Applied Technologies, Qujing Normal University, Qujing, 655011, Yunnan, China. haythamisleem@mail.qjnu.edu.cn.
Scientific reports
|August 12, 2024
概括
本研究探讨混合FRP混凝土-钢柱,开发机器学习模型来预测性能. 分类增强 (CATB) 和极度梯度增强 (XGB) 模型显示出高准确度,CATB是预测列行为最强大的.
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
- 计算力学 计算力学 计算力学
背景情况:
- 混合FRP混凝土-钢柱的圆形截面越来越多地用于建筑.
- 了解它们在负载下的行为对于结构完整性和设计优化至关重要.
- 现有的分析模型可能无法完全捕捉这些复合结构中的复杂相互作用.
研究的目的:
- 为了研究混合FRP混凝土-钢柱与圆横截面的行为.
- 开发和评估机器学习模型,用于预测有限的最终应变和最终负载.
- 为了确定这个结构应用程序最准确和最强大的预测模型.
主要方法:
- 文献审查和参数研究收集116个数据点.
- 机器学习模型的开发,包括决策树 (DT),随机森林 (RF),自适应提升 (ADAB),分类提升 (CATB) 和极端梯度提升 (XGB).
- 开发的预测模型的视觉和定量验证和评估.
主要成果:
- 机器学习模型已成功开发,以估计最终负载和有限的最终应变.
- 分类增强 (CATB) 和极度梯度增强 (XGB) 展示了高精度和概括能力.
- 在测试过程中,CATB模型的性能略高,错误率始终较低.
结论:
- CATB和XGB是非常有效的机器学习模型,用于预测混合FRP混凝土-钢柱的行为.
- 推CATB模型作为最佳选择,因为它具有卓越的准确性和抗过的强度.
- 准确预测柱子性能可以提高土木工程应用中的结构设计和安全性.
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