一种神经网络方法来估计具有随机多重损伤的杆束的频率
1Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58104, USA.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
神经网络模型准确地预测受损杆梁中的自然频率,为结构健康监测的传统方法提供了更快的替代方案. 这种损坏检测方法对于评估结构完整性至关重要.
科学领域:
- 结构健康监测 (SHM) 是一种结构健康监测.
- 计算力学 计算力学 计算力学
- 机器学习应用 机器学习应用
背景情况:
- 自然频率是SHM系统结构完整性的关键指标.
- 自然频率的变化标志着由于损坏而导致的结构变化.
- 像有限元分析这样的传统方法可能是计算密集的.
研究的目的:
- 为测量自然频率提出神经网络 (NN) 模型,作为有限元 (FE) 方法的有效替代方案.
- 评估NN模型在识别随机多重损伤的横向梁的自然频率方面的能力.
- 用曲线拟合方程验证NN模型的预测.
主要方法:
- 使用了通过MATLAB脚本通过Ansys参数设计语言 (APDL) 模拟生成的数据集.
- 使用蒙特卡洛 (MC) 技术模拟无限的随机损坏场景 (严重程度和大小).
- 开发并验证了一个神经网络模型与模拟数据和曲线拟合方程.
主要成果:
- 与前三种模式的模拟数据相比,NN模型实现了高精度,误差最小 (0.2-3%).
- 证明损害严重程度的增加显著影响频率转移 (例如,10%的区域减少与30%相比).
- 在频率转移上,损伤严重程度被确定为比损伤大小更具影响力的因素.
结论:
- 拟议的NN模型有效地估计了各种场景中受损光束的频率转移.
- 这种基于振动的损害识别方法为预测频率变化提供了计算效率高的解决方案.
- 该NN模型可以成为实时结构健康监测和损害评估的宝贵工具.
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