使用FNN和1D-CNN在移动负载下的梁状结构中基于能量消耗的疲劳损伤检测
Luan Vuong-Cong1, Toan Pham-Bao2, Vien Le-Ngoc1
1Laboratory of Applied Mechanics (LAM), Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Viet Nam.
Scientific reports
|November 17, 2025
概括
这项研究引入了一种新的方法,用于检测和测量使用能量消散的光束中的疲劳损伤. 该方法准确地确定损伤位置和严重程度,增强结构健康监测.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 梁状结构的疲劳损伤对结构完整性构成重大风险.
- 早期发现和精确量化疲劳损伤对于有效的结构健康监测 (SHM) 至关重要.
- 现有的方法可能缺乏用于早期疲劳检测所需的灵敏度或准确性.
研究的目的:
- 开发和验证一种两步方法,用于定位和量化梁结构中疲劳损伤.
- 利用能量消耗作为损害评估的关键特征.
- 提高工程应用中SHM系统的可靠性和准确性.
主要方法:
- 在随机移动负载下生成完整的光束响应,通过将不同速度的响应结合起来.
- 从模拟的随机反应计算了散射光谱 (DS).
- 使用Feedforward神经网络 (FNN) 来分析DS对损伤定位的相关性.
- 使用1D卷积神经网络 (1D-CNN) 来使用本地DS量化损伤严重程度.
主要成果:
- 通过使用FNN对DS相关性的分析,在定位疲劳损伤方面获得了96-100%的准确性.
- 通过1D-CNN分析局部DS,在量化损伤严重程度方面达到80-100%的准确性.
- 证明了能量消耗的有效性作为疲劳损伤的敏感指标.
结论:
- 拟议的两步方法为结构性健康监测提供了一种可靠的方法.
- 该技术为检测早期疲劳损伤提供了高灵敏度.
- 这种方法对工程应用需要 robust 损害评估的有希望的进步.
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