基于机器学习的预测模型,用于穿孔纤维增强聚合物增强混凝土板的切削强度,使用梯度增强的回归树
Emad A Abood1, Marwa Hameed Abdallah2, Mahmood Alsaadi3
1Department of Material Engineering, College of Engineering, Al-Shatrah University, Al-Shatrah 64007, Iraq.
Materials (Basel, Switzerland)
|August 29, 2024
概括
梯度增强回归树 (GBRT) 模型准确地预测了纤维增强聚合物 (FRP) 混凝土板的穿孔切削强度,优于传统方法. SHAP分析揭示了影响改善结构设计预测的关键因素.
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
- 机器学习 机器学习
背景情况:
- 纤维增强聚合物 (FRP) 在混凝土板上具有优势,但容易发生穿孔切割故障.
- 预测FRP钢筋混凝土板的穿孔切削强度的现有实证模型存在重大不准确性.
- 对于结构完整性的更可靠的预测模型有着至关重要的需求.
研究的目的:
- 开发和验证梯度增强回归树 (GBRT) 模型,以准确预测FRP钢筋混凝土板的穿孔剪强度.
- 为了解决这些局限性,并改进现有的经验预测模型.
- 利用机器学习来加强土木工程中的结构分析.
主要方法:
- 编制了一个包含238个FRP钢筋混凝土板实验结果的综合数据库.
- 与其他机器学习算法相比,开发和评估了梯度增强回归树 (GBRT) 模型.
- 为了模型的可解释性,采用了夏普利添加式解释 (SHAP) 方法.
主要成果:
- GBRT模型显示出高预测准确度,其中R2=0.955,RMSE=64.85和MAE=42.89.
- 在预测穿孔剪切强度方面,GBRT模型显著超过了传统的实证模型.
- SHAP分析确定了板块厚度,FRP增强比率和关键截面周长为关键影响变量.
结论:
- GBRT模型提供了一个高度准确和可靠的方法来预测FRP钢筋混凝土板的穿孔剪切强度.
- 机器学习提供了一种强大的方法来克服结构工程中传统实证模型的局限性.
- 通过SHAP分析了解关键影响因素可以指导未来的FRP混凝土结构的研究和设计优化.
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