使用机器学习算法对FRP增强钢筋混凝土梁的柔性能力进行智能预测建模
Majid Khan1, Adil Khan2, Asad Ullah Khan3
1COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
Heliyon
|January 3, 2024
概括
机器学习模型准确地预测了纤维增强聚合物 (FRP) 增强钢筋混凝土 (RC) 梁的柔性能力,优于传统方法. 影响容量的关键因素包括光束宽度和强化比率.
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:
- 纤维增强聚合物 (FRP) 提高了混凝土结构的效率和耐用性.
- 现有的代码条款往往无法准确估计FRP增强钢筋混凝土 (RC) 成员的性能.
- 差异源于当前代码的局限性,以捕捉这些复合结构的复杂行为.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测FRP增强RC梁的曲能力.
- 将ML模型的预测精度与实证和线性回归模型进行比较.
- 通过特征重要性分析,确定影响柔性能力的关键参数.
主要方法:
- 编制了一套广泛的FRP强化RC梁实验结果数据集.
- 利用基因表达编程 (GEP) 和多表达编程 (MEP) 进行数据驱动的模型开发.
- 使用统计指标 (如R,MAE) 和SHapley添加式扩展 (SHAP) 来进行模型评估和解释.
主要成果:
- GEP模型实现了高精度,R=0.98和低MAE (4.08培训,5.39验证).
- 在培训和验证阶段,MEP模型表现出强的表现,R=0.96.
- 基于ML的模型在预测曲能力方面明显优于经验和线性回归模型.
结论:
- ML模型,特别是GEP,为估计FRP增强RC梁的曲能力提供了优越和可靠的方法.
- 这些数据驱动模型对实际的工程应用有很大的前景.
- 梁宽度,切口有效深度和拉力强化比率是曲能力的关键预测指标.
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