通过混合机器学习方法提高UHPC光束的剪切强度预测
Sanjog Chhetri Sapkota1, Ajad Shrestha2, Moinul Haq3
1Department of Civil Engineering, Sharda University, Greater Noida, 201310, India.
Scientific reports
|August 2, 2025
概括
通过新的混合机器学习模型,预测超高性能混凝土 (UHPC) 梁切割强度得到了改进. 这些模型将优化算法与极端梯度提升集成在一起,为更安全的结构工程提供高度准确和可解释的预测.
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:
- 超高性能混凝土 (UHPC) 梁的剪切强度预测是复杂的,因为有许多变量.
- 现有的实证方程和传统的机器学习 (ML) 方法往往缺乏所需的准确性.
- 开发精确的预测模型对于确保UHPC结构的安全性和效率至关重要.
研究的目的:
- 开发先进的混合机器学习 (ML) 模型,准确预测UHPC光束剪切强度.
- 将自然启发的元启发算法 (巨型鸟优化,斑点鹿优化,海优化) 与极端梯度增强 (XGB) 集成在一起.
- 提高模型透明度,为工程师提供实用工具.
主要方法:
- 创建一个全面的数据集从广泛的文献评论在UHPC光束.
- 混合ML模型的开发和测试,包括鱼海优化-XGB (LSA-XGB).
- 使用性能指标 (R2,RMSE,MAE,VAF) 和沙普利增量解释 (SHAP) 进行评估和解释.
主要成果:
- LSA-XGB模型实现了高精度,R2值为0.9912 (训练) 和0.9802 (测试).
- SHAP分析提供了各种输入参数对剪切强度预测的影响的见解.
- 拟议的混合模型的性能优于现有的实证方程和传统的ML技术.
结论:
- 混合ML模型,特别是LSA-XGB,提供了可靠和准确的方法来预测UHPC光束剪切强度.
- 通过SHAP增强的模型解释性有助于更好的理解和对预测的信任.
- 开发的模型和附带的GUI为工程师提供了可访问的工具,以便在结构设计中实时做出决策.
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