基于内部产物矩阵和框架结构平行卷积神经网络的损伤识别
Yingying He1,2, Ji Feng3, Baogang Sun1
1School of Computer Engineering, Chongqing College of Humanities, Science & Technology, Chongqing, 401524, China.
Scientific reports
|December 18, 2024
概括
这项研究引入了一种新的并行卷积神经网络 (PCNN),与内部产物矩阵 (IPM) 结合,用于增强结构损伤识别. IPM-PCNN方法使用振动信号准确检测钢结构损坏,达到96.60%的准确性.
科学领域:
- 结构工程 结构工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 振动信号分析对于结构健康监测和损坏识别至关重要.
- 传统的机器学习方法,如卷积神经网络 (CNN),通常使用单域输入,限制损坏识别准确性.
- 提取多维特征是克服这些局限性的关键.
研究的目的:
- 提出一种新的方法,即内部产品矩阵-并行卷积神经网络 (IPM-PCNN),用于增强结构损伤检测.
- 利用振动信号的多维特征来更准确地识别结构损坏.
- 验证IPM-PCNN对现有方法的有效性和优越性.
主要方法:
- 开发了一个新的IPM-PCNN框架,集成一维 (1D) CNN用于时间序列数据和二维 (2D) CNN用于模态数据.
- 使用五层钢框架模型进行实验验证.
- 在相同的条件下将IPM-PCNN模型与其他五种机器学习方法进行了比较.
主要成果:
- IPM-PCNN模型在测试组上识别结构损伤时达到96.60%的高精度.
- 与其他评估的机器学习技术相比,提出的方法显示出更高的性能.
- 该研究提供了内部推理过程的可视化,以更好地了解模型的决策.
结论:
- IPM-PCNN有效地从振动信号中提取多维时间和模式特征,以准确识别结构损坏.
- 这种新的方法在结构性健康监测中明显优于传统的机器学习方法.
- 这些发现为确保结构完整性的先进技术提供了有价值的见解.
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