在轴向压缩下的圆形聚合物CFST柱:预测和可靠性分析.
Khaled Megahed1, Nabil Said Mahmoud2, Saad Elden Mostafa Abd-Rabou2
1Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt. k.megahed@mans.edu.eg.
Scientific reports
|November 1, 2024
概括
机器学习模型准确地预测了聚合混凝土 (RBAC) 填充钢管 (RCFST) 柱的轴强度,优于当前的设计代码. 一个新的设计表达式为结构安全评估提供了实际准确性.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 机器学习应用 机器学习应用
背景情况:
- 钢管增强了聚合混凝土 (RBAC) 的结构完整性,形成了RBAC填充的钢管 (RCFST).
- 现有的设计规范在评估轴向压缩行为和确保圆柱RCFST (CS-RCFST) 柱的结构安全方面存在局限性.
- 关于这些复合体柱的结构安全性的研究很少.
研究的目的:
- 探索机器学习 (ML) 功能来预测CS-RCFST列的轴强度.
- 为了比较各种ML模型对这个预测任务的性能.
- 开发一个实际的设计表达,并评估ML模型在结构设计中的可靠性.
主要方法:
- 使用了145个CS-RCFST列的实验数据库.
- 应用了六种ML模型:符号回归 (SR),XGBoost,CatBoost,随机森林,LightGBM和高斯过程回归.
- 采用贝叶斯优化来进行超参数调整,并进行可靠性分析.
主要成果:
- CatBoost 模型表现出卓越的准确性 (R2=0.999 训练,R2=0.993 测试).
- 基于SR的实用设计表达表现出了良好的准确性 (平均测试与预测比为0.99).
- 在预测轴强度方面,ML模型显著优于AISC360和EC4.
结论:
- 与当前的设计代码相比,ML模型提供了一个高度可靠和准确的方法来预测CS-RCFST列轴强度.
- 开发的基于SR的设计表达式为实际应用提供了可接受的准确性.
- 可靠性分析证实了ML模型适用于实际结构设计的适用性,并提出了耐力设计因素.
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