一种使用CWT和CNN的动态应变预测方法,用于在受地震负荷影响的建筑物中的传感器故障
Hyo Seon Park1, Sang Hoon Yoo1, Byung Kwan Oh2
1Department of Architecture and Architectural Engineering, Yonsei University, Seoul, 03722, Korea.
Scientific reports
|November 15, 2024
概括
本研究介绍了一种使用卷积神经网络 (CNN) 预测传感器故障时结构性应变反应的方法. 该方法集成了连续波束变换 (CWT) 数据,用于增强的地震负载分析.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 数据科学数据科学数据科学
背景情况:
- 结构健康监测 (SHM) 对于评估结构安全至关重要.
- 传感器故障和数据丢失是SHM系统的重大挑战.
- 准确的应变反应预测对于保持结构完整至关重要.
研究的目的:
- 提出一种用于预测传感器故障期间结构应变反应的新方法.
- 利用卷积神经网络 (CNN) 进行准确的应变预测.
- 通过结合连续波量变换 (CWT) 数据来增强CNN的预测.
主要方法:
- 一个CNN模型被开发来预测时间历史菌株数据.
- 输入数据包括邻近结构成员的应变测量和CWT结果.
- 目标输出是带有故障传感器的成员的应变反应.
主要成果:
- 拟议的CNN方法有效地预测了传感器故障的结构成员的应变反应.
- 整合CWT数据提高了CNN在地震事件期间捕捉动态结构行为的能力.
- 钢框架上的数值模拟和钢筋混凝土框架上的实验测试都验证了该方法的有效性.
结论:
- 开发的基于CNN的方法为预测有传感器损伤的结构应变反应提供了可靠的解决方案.
- 整合CWT数据有效地提高了对非线性结构反应的预测.
- 这种方法有助于建立更强大,更有弹性的结构性健康监测系统.
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