使用机器学习技术预测可拆卸的剪连接器的最终负载能力
Ahmed I Saleh1, Nabil S Mahmoud2, Fikry A Salem2
1Civil Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt. ahmed.saleh@deltauniv.edu.eg.
Scientific reports
|August 21, 2025
概括
机器学习模型准确地预测钢混凝土结构中可拆卸的剪切连接器的负载能力. XGBoost和随机森林模型显示了最高的准确性,确定了螺栓直径和材料强度等关键因素.
科学领域:
- 结构工程
- 计算力学
- 材料科学
背景情况:
- 可拆卸的剪切连接器对于钢混凝土复合结构至关重要.
- 预测它们的最终负载能力对于结构安全和设计优化至关重要.
- 现有的预测方法可能缺乏准确性或效率.
研究的目的:
- 研究各种机器学习 (ML) 算法的有效性,以预测可拆卸的剪切连接器的最终负载能力.
- 确定影响剪切连接器性能最重要的参数.
- 将不同ML模型的预测性能进行比较.
主要方法:
- 编制了239个实验和数值记录的数据集.
- 评估了八种监督的ML算法:线性回归,Ridge,Lasso,KNN,SVR,决策树,随机森林和XGBoost.
- 使用R2,MAE和MSE指标进行性能评估,并对特征的重要性进行SHAP分析.
主要成果:
- XGBoost和随机森林模型显示出更高的预测准确度 (R2分别为0.9477和0.9255).
- 螺栓直径,钢铁的最终强度和螺栓对混凝土的摩擦被确定为最重要的预测因素.
- 特性重要性热图证实了SHAP分析的发现.
结论:
- 先进的机器学习模型,特别是XGBoost和Random Forest,为预测可拆卸剪切连接器容量提供了可靠的方法.
- 了解关键影响因素可以指导材料选择和设计改进.
- 需要进一步的研究来解决数据不平衡,并提高模型的通用性.
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