基于机器学习的快速地震后损坏检测RC阻力时刻框架建筑的快速地震后损害检测
Edisson Alberto Moscoso Alcantara1, Taiki Saito1
1Department of Architecture and Civil Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种机器学习 (ML) 方法,用于预测钢筋混凝土 (RC) 建筑物的损坏. 该方法使用地震数据和建筑特征准确预测结构完整性.
科学领域:
- 结构工程 结构工程
- 机器学习应用 机器学习应用
- 地震工程的工程是地震工程.
背景情况:
- 钢筋混凝土 (RC) 耐动力框架建筑是关键基础设施.
- 预测地震损害对于安全和弹性至关重要.
- 现有的方法可能无法完全捕捉地震负载下的复杂结构行为.
研究的目的:
- 开发和验证机器学习 (ML) 方法,用于预测RC抗时刻框架建筑物的损坏状况.
- 为了确定最佳的输入参数和ML模型,以准确预测损坏.
- 加强对地震事件下的结构性能的评估.
主要方法:
- 使用虚拟工作方法设计600座RC建筑,楼层和跨度各不相同.
- 执行了6万个时间历史分析,其中包括10个频谱匹配的地震记录和缩放因子.
- 使用来自传感器数据的27个强度测量 (IMs) 作为ML模型的输入特征.
主要成果:
- 训练和评估了七种机器学习 (ML) 方法,以确定损害预测的准确性.
- 输入数据包括IM,故事数和跨度 (X和Y方向).
- 输出数据是最大层间漂移比率,这是一个关键损坏指标.
结论:
- 该研究成功建立了一个强大的ML方法来预测RC建筑损坏.
- 研究确定了训练数据,IM和ML算法的最有效组合,以获得高预测准确度.
- 这种方法为地震风险评估和结构健康监测提供了有价值的工具.
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