基于可解释机器学习和SHAP值的钢筋混凝土梁柱连接器故障模式预测的分析方程
Ioannis Karampinis1, Martha Karabini2, Theodoros Rousakis2
1Laboratory of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一种使用SHapley增材扩展 (SHAP) 值的新方法,用于预测钢筋混凝土梁柱接头的故障模式,这对于地震安全至关重要. 开发的分析方程准确地估计了联合行为,有助于结构完整性的评估.
科学领域:
- 结构工程 结构工程
- 地震危险分析 - - 地震危险分析
- 机器学习应用 机器学习应用
背景情况:
- 梁柱连接对于钢筋混凝土结构的抗震性能至关重要.
- 当前的设计规范规定,在地震事件期间,柔性束在关节剪切故障上屈服.
- 预测现有结构中的关节故障模式对于安全评估至关重要.
研究的目的:
- 开发一种新的方法来推导分析方程,以预测钢筋混凝土梁柱连接的故障模式.
- 为了利用夏普利添加式扩展 (SHAP) 值作为适合分析曲线的转换目标变量.
- 为评估现有结构的地震脆弱性提供可靠的工具.
主要方法:
- 使用了478个实验结果的数据集.
- 一个 eXtreme渐变增强 (XGBoost) 机器学习模型最初被安装.
- 沙普利添加式扩展 (SHAP) 值来自XGBoost模型.
- 分析方程与SHAP值相匹配,以近似模型预测.
主要成果:
- 极端梯度提升模型的整体准确率约为84%.
- 由此得出的分析方程的准确度约为78%.
- 精度,回忆和F1分数在大约76%至80%之间,这表明模型没有偏见.
结论:
- 这种新的方法有效地推导出用于预测梁柱联合故障模式的分析方程.
- 开发的方程为地震结构评估提供了可靠和准确的工具.
- 该方法证明了SHAP值的实用性超出了结构工程中预测建模的解释能力.
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