使用机器学习预测FRP包裹的圆形混凝土柱的最终强度和应变
Li Shang1, Haytham F Isleem2, Walaa J K Almoghayer3
1School of Civil and Hydraulic Engineering, Xichang University, Xichang, 615000, China.
Scientific reports
|March 29, 2025
概括
机器学习准确地预测FRP包装混凝土柱中的强度和延展增强比率. 随机梯度增强和极端梯度增强模型显示出卓越的性能,FRP特性和混凝土强度是关键预测因素.
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:
- 精确预测纤维增强聚合物 (FRP) 包裹的圆形混凝土柱的强度和应变增强比对于结构设计至关重要.
- 现有的方法可能缺乏优化这些复合结构性能所需的精度.
- 机器学习 (ML) 为改善结构工程应用中的预测准确性提供了一个有希望的途径.
研究的目的:
- 开发和评估机器学习模型,用于预测FRP包裹圆形混凝土柱的强度和应变增强比率.
- 为了比较决策树 (DT),自适应提升 (ADB),随机梯度提升 (SGB) 和极端梯度提升 (XGB) 模型的性能.
- 确定影响增强比率的最有影响力的参数,并为工程师提供实用工具.
主要方法:
- 收集了来自实验研究和有限元素建模的181个样本的数据集.
- 使用贝叶斯优化训练和优化四个ML模型 (DT,ADB,SGB,XGB).
- 采用沙普利添加式扩张 (SHAP) 分析来确定特征的重要性.
- 为实时预测开发了一个交互式图形用户界面 (GUI).
主要成果:
- 随机梯度增强 (SGB) 证明了强度增强预测的最高准确性 (R2=0.850,RMSE=0.190).
- 极端梯度增强 (XGB) 在应变增强预测方面表现优异 (R2=0.779,RMSE=2.162).
- FRP厚度和弹性模量 (tf × Ef) 和混凝土压力强度 (f'c) 的乘积被确定为最重要的预测因素.
结论:
- 机器学习模型,特别是SGB和XGB,可提供可靠的预测,用于FRP包裹圆形混凝土柱的增强比率.
- 开发的GUI促进了这些ML模型在结构工程中的实际应用.
- 功能重要性分析突出了设计优化和进一步研究的关键参数.
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