圆形混凝土填充钢管状短柱的压缩行为使用数值调查和机器学习技术
Hazem Samih Mohamed1, Tang Qiong2, Haytham F Isleem3,4
1College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, 350002, Fujian, China.
Scientific reports
|November 6, 2024
概括
这项研究开发了一个有限元模型和机器学习技术,以预测圆形混凝土填充钢管柱 (CFST) 的承载能力. 梯度增强回归器和XGBoost回归器模型显示出高精度,确定偏心负载是关键因素.
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:
- 混凝土填充的钢管柱 (CFST) 在建筑中被广泛使用.
- 圆形CFST短柱提供独特的结构优势.
- 准确预测承载能力对于结构安全和设计优化至关重要.
研究的目的:
- 开发和验证一种非线性有限元模型 (FEM),用于预测各种圆CFST短列配置的承载能力.
- 调查几何和材料参数对圆CFST短柱的性能的影响.
- 评估多种机器学习 (ML) 技术的有效性,以预测圆CFST短列的承载能力.
主要方法:
- 开发了一个非线性有限元模型 (FEM) 来模拟圆形CFST短列.
- 进行了参数和分析研究,以分析关键参数的影响.
- 几种ML算法,包括渐变增强回归器 (GBR) 和XGBoost回归器 (XGBR),被使用和评估.
- 用SHapley添加式扩展 (SHAP) 来解释ML模型中的特征重要性.
主要成果:
- FEM提供了不同圆形CFST配置的承载能力的准确预测.
- GBR和XGBR模型实现了高预测准确性,R2分数分别为0.9888和0.9885.
- SHAP分析显示,偏心负载比率,外侧钢管屈服强度和内部宽度显著影响承载能力.
结论:
- 开发的FEM和ML模型为预测圆CFST短列的性能提供了可靠的工具.
- 机器学习技术,特别是GBR和XGBR,显示了结构能力预测的巨大潜力.
- 异常负荷被确定为影响这些结构元件承载能力的最关键参数.
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