一个可解释的整体机器学习框架,用于预测BFRP增强混凝土梁的最终柔性能力
Sebghatullah Jueyendah1, Elif Ağcakoca1
1Department of Civil Engineering, Sakarya University, Esentepe Campus, Serdivan 54050, Sakarya, Türkiye.
Polymers
|March 14, 2026
概括
使用优化的机器学习堆叠组件,预测基岩纤维增强聚合物 (BFRP) 增强混凝土梁的最终时刻能力得到了改进. 这种可解释的框架准确地预测了光束容量,确定了跨度长度和光束深度作为关键因素.
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
- 计算力学 计算力学 计算力学
背景情况:
- 由于复杂的参数相互作用和BFRP的线性弹性行为,预测基岩纤维增强聚合物 (BFRP) 增强混凝土梁的最终时刻容量 (Mu) 是一个挑战.
- 传统的设计模型往往对这些结构缺乏准确性.
研究的目的:
- 开发和优化机器学习 (ML) 框架,以可靠地预测BFRP钢筋混凝土梁中的Mu.
- 将可解释AI (XAI) 与ML集成,以提高可解释性和物理一致性.
主要方法:
- 一个优化的ML框架是使用组合策略开发的,包括随机森林,额外的树木,梯度增强,Adaboost,包装,支持向量回归和基于直方图的梯度增强.
- 分析了材料,几何,增强和BFRP机械参数的全面数据库.
- 模型性能使用80/20列车测试分割和10倍交叉验证进行评估,使用R2,RMSE,MAE和MAPE指标.
- 使用SHAP,PDP,ALE和ICE进行了可解释性分析.
主要成果:
- 堆叠回归器表现出卓越的预测性能,R2为0.999 (训练) 和0.988 (测试),表明了高精度和稳定性.
- 解释性分析确定了跨度长度 (L) 和光束深度 (h) 作为影响Mu预测的控制参数.
- 开发的框架为预测Mu提供了强大的概括能力.
结论:
- 一个优化和可解释的堆叠组合ML框架已成功开发,用于预测BFRP钢筋混凝土梁的最终时刻能力.
- 该框架将可解释的AI与经典的柔性力学集成在一起,提供物理一致和可靠的预测.
- 跨度长度和梁深度被确定为Mu预测的关键参数,为结构设计提供了宝贵的见解.
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