可解释的基于机器学习的预测模型用于FRP强化RC梁的混凝土覆盖分离
Sheng Zheng1, Tianyu Hu2, Yong Yu3
1School of Digital Construction, Shanghai Urban Construction Vocational College, Shanghai 201415, China.
Materials (Basel, Switzerland)
|May 11, 2024
概括
机器学习模型XGBoost准确地预测了用纤维增强聚合物 (FRP) 增强的钢筋混凝土梁中的混凝土覆盖分离 (CCS). 这种先进的模型优于现有的代码和研究人员模型,为结构分析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:
- 纤维增强聚合物 (FRP) 的增强对于提高钢筋混凝土 (RC) 梁的柔性能力至关重要.
- 混凝土覆盖分离 (CCS) 是一个关键的故障模式,影响FRP增强RC结构的长期性能和安全性.
- 准确预测CCS对于可靠的结构设计和评估至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测FRP增强RC梁中的混凝土覆盖分离 (CCS).
- 确定最适合的机器学习算法,用于准确的CCS预测.
- 将机器学习模型的性能与现有的设计代码和经验模型进行比较.
主要方法:
- 构建多个机器学习模型,包括线性回归,支持向量回归,BP神经网络,决策树,随机森林和XGBoost.
- 使用标准指标评估模型性能,以确定最有效的预测器.
- 应用夏普利添加式解释 (SHAP) 来对影响因素进行详细的参数分析.
主要成果:
- 与其他机器学习模型,代码和现有的研究人员模型相比,XGBoost算法在预测CCS方面表现出卓越的表现.
- 现有的代码条款和研究人员模型在CCS预测中被发现过于保守或不准确.
- SHAP分析确定了对切削力和钢筋度强度的具体贡献,作为影响CCS的最重要的参数.
结论:
- XGBoost是预测FRP增强RC光束中CCS的最佳模型,提供更高的准确性和可靠性.
- 目前的设计代码和经验模型需要进行重大修订,以提高CCS的预测准确性.
- 该研究提供了对控制CCS的关键参数的关键见解,有助于制定更强大的设计准则.
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