基于混凝土和钢材的特性,ML对腐蚀钢筋的最终和相对结合强度的建模
Alireza Hosseinzadeh Kashani1, Mansour Ghalehnovi2, Hossein Etemadfard1
1Dept. of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 93, Iran.
Scientific reports
|July 23, 2025
概括
本研究引入了一种机器学习模型,用于预测腐蚀钢筋混凝土 (RC) 结构中的键强度. XGBoost模型准确地预测了最终和相对键强度,识别了腐蚀水平和材料特性等关键因素.
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
- 数据科学数据科学数据科学
背景情况:
- 腐蚀显著降低了钢筋混凝土 (RC) 的结合强度,影响了结构耐用性和性能.
- 准确预测这种债券退化对于评估结构完整性和计划维修至关重要.
- 评估债券强度的现有方法可能耗时且资源密集.
研究的目的:
- 开发一个数据驱动的框架来预测最终债券强度 (UBS) 和相对债券强度 (RBS) 在腐蚀的RC.
- 使用多输出机器学习 (ML) 方法同时预测UBS和RBS.
- 确定影响腐蚀的RC中影响键强度降解的最有影响力的因素.
主要方法:
- 编制了来自对腐蚀RC的实验研究的综合数据集.
- 训练了六个ML算法 (MLP,SVR,DT,RF,GBoost,XGBoost) 来预测UBS和RBS. 这是一个很好的方法.
- 在特征重要性分析中使用了SHapley添加式解释 (SHAP).
主要成果:
- XGBoost模型显示出优异的预测性能,UBS的R2值为0.977,RBS的R2值为0.966.
- 该模型实现了较低的预测误差:UBS的MAPE为9.6%,RBS的MAPE为7.0%.
- 腐蚀水平,压力强度和收益率强度被确定为债券强度的最重要的预测指标.
结论:
- 拟议的ML框架有效地预测了腐蚀的RC中的键强度降解.
- 可解释的ML工具为评估债券行为的传统实验室测试提供了有效的替代方案.
- 这些发现支持使用ML来评估受腐蚀影响的RC结构的耐用性.
关键词:
债券强度降低 债券强度降低腐蚀的钢筋加强已经被腐蚀了.数据驱动的建模.机器学习 (ML) 是指机器学习.多输出回归的多输出回归方法钢筋混凝土 (RC) 是一种强化混凝土.在SHAP分析中,我们分析了SHAP.更多相关视频
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