组装基于增强的软计算模型,用于预测钢板和CFRP板之间的粘合强度
Irwan Afriadi1, Chanachai Thongchom2, Divesh Ranjan Kumar3
1Research Unit in Structural and Foundation Engineering, Department of Civil Engineering, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Khlong Luang, Pathumthani, Thailand.
Scientific reports
|July 2, 2025
概括
本研究探讨了碳纤维增强聚合物 (CFRP) 到钢结合连接的基于增强机器学习的方法. ADABoost在预测债券行为方面表现出卓越的性能,提高了结构设计的准确性和耐用性.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 纤维增强聚合物 (FRP) 为结构增强和维修提供高强度与重量比和耐腐蚀性.
- 了解FRP与钢结合的连接行为对于模拟解结失败至关重要.
- 增强碳纤维增强聚合物 (CFRP) 与钢的键的基于机器学习的增强是一个研究不足的领域.
研究的目的:
- 通过基于增强的整体机器学习来研究与钢梁结合的CFRP板块的粘合行为.
- 为了评估XGBoost,GBM,CATBoost,LGBM和ADABoost算法的CFRP-钢结合强度的预测性能.
- 确定最佳的机器学习模型来预测这些结合连接的最大负载能力.
主要方法:
- 使用了基于增强的集体机器学习算法 (XGBoost,GBM,CATBoost,LGBM,ADABoost).
- 选择了八个输入变量和一个输出变量 (最大负载,PU) 用于模型训练和测试.
- 使用了来自现有文献的317个实验数据集的数据库.
- 使用多个性能标准进行排名分析,以确定表现最佳的模型.
主要成果:
- 在预测CFRP板在钢梁上的粘合行为方面,ADABoost的表现优于其他算法.
- ADABoost 实现了高精度,其 R2 值为训练时为 1,测试时为 0.99882.
- 排名分析证实ADABoost是基于各种绩效指标的最佳模型.
结论:
- 基于增强的机器学习,特别是ADABoost,对于分析CFRP与钢的结合行为是有效的.
- 这种方法可以提高结构设计的准确性,降低成本,提高CFRP增强结构的性能和耐用性.
- 这些发现为建筑行业在利用先进的计算技术进行结构改造和加强方面提供了宝贵的见解.
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