使用符号回归和ML技术预测混合FRP-钢筋梁的屈曲强度
1Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt. k.megahed@mans.edu.eg.
Scientific reports
|June 25, 2025
概括
先进的计算模型,包括机器学习和符号回归,大大改善了混合纤维增强聚合物 (FRP) 和钢筋混凝土梁的屈曲强度预测. 这些方法比目前的设计准则提供了更高的准确性.
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:
- 混合纤维增强聚合物 (FRP) 和钢铁增强混凝土 (混合FRP-钢RC) 梁比纯FRP增强梁提供更好的曲性能.
- 现有的设计规范,如ACI 440.11-22,在准确预测这些混合系统的屈曲强度方面存在局限性.
研究的目的:
- 为了提高混合FRP-钢RC梁的屈曲强度模型的预测准确性和可解释性.
- 开发使用机器学习 (ML) 和符号回归的先进计算模型.
主要方法:
- 使用134个实验数据点的数据集开发预测模型.
- 机器学习技术的应用,包括高斯过程回归 (GPR),NGBoost和CatBoost.
- 使用符号回归来导出透明的数学表达式.
主要成果:
- 与ACI 440.11-22方程相比,ML和符号回归模型都显示出明显更低的误差 (MAE,MAPE,RMSE) 和更高的准确性 (R2).
- 机器学习模型,特别是GPR,实现了高预测准确度,R2值接近1.0和低MAPE (5.19%的培训,11.51%的测试).
- 符号回归提供了一个可解释的数学模型,平均预测比为1.003,MAPE为11.08%.
结论:
- 先进的计算方法,ML和符号回归,大大超出了混合FRP-钢RC梁的当前设计准则.
- 符号回归通过产生可靠,可解释和高效的设计方程提供了实际优势.
- 该研究强调了ML和符号回归在改进混合混凝土系统结构设计和分析方面的潜力.
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