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使用独立的元启发算法和它们与CatBoost的组合进行扫描深度估计.

Nasrin Eini1, Saeid Janizadeh1, Sayed M Bateni1,2

  • 1Department of Civil, Environmental and Construction Engineering & Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.

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概括

本研究介绍了先进的机器学习和优化技术,以准确预测桥梁码头扫描深度,提高结构安全性和降低维护成本. 优化的模型显著优于传统方法,提供更可靠的工程解决方案.

关键词:
桥梁码头是一个桥梁码头.在 CatBoost 中使用 CatBoost.显式方程 显式方程 显式方程这就是 SHAP SHAP 的意思.搜索深度 搜索深度

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 液压工程 液压工程 液压工程
  • 计算力学 计算力学 计算力学

背景情况:

  • 桥梁支柱周围的扫描是结构故障的主要原因,需要准确预测平衡扫描深度 (Seq).
  • 现有的经验方程往往缺乏准确性,因为影响冲动的液压过程的复杂,非线性性质.
  • 需要先进的计算方法来克服传统扫描预测模型的局限性.

研究的目的:

  • 开发和评估新的方法来估计在桥梁支柱周围的平衡扫地深度 (Seq).
  • 为了比较优化机器学习模型和衍生显式方程与现有方法的性能.
  • 通过可解释性分析,确定影响扫描深度的关键因素.

主要方法:

  • 使用五个元启发式算法优化一个分类提升 (CatBoost) 机器学习模型:哈里斯优化 (HHO),火焰优化 (MFO),优化算法 (WOA),优化算法 (POA) 和狐优化算法 (FOX).
  • 开发明确的扫描深度预测方程,这些方程来自相同的元启发式优化算法.
  • 使用夏普利添加式扩展 (SHAP) 和灵敏度分析来理解模型行为和因素的重要性.

主要成果:

  • 混合HHO-CatBoost模型表现出卓越的性能,达到0.9670,0.0286米的根平均平方误差 (RMSE) 和0.0178米的平均绝对误差 (MAE) 的确定系数 (R2).
  • 除了雷诺兹数之外,HHO衍生的显式方程表现优于现有的18个方程,R2=0.828,RMSE=0.066 m,MAE=0.043 m.
  • SHAP分析确定码头直径是最有影响力的因素,而灵敏度分析强调码头直径与流水深度的比率在动荡条件下是最重要的.

结论:

  • 与传统方法相比,优化的机器学习模型和衍生出的明确方程在预测平衡深度方面提供了显著的改进.
  • 超听觉算法,特别是HHO,在提高机器学习模型和显式扫描预测方程的准确性方面是有效的.
  • 了解码头直径和流深等因素的影响对于准确的桥梁扫描评估和风险管理至关重要.