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江守燕, 邓王涛, 孙立国, 杜成斌. 数据驱动的半无限介质裂纹识别模型研究. 力学学报, 待出版. DOI: 10.6052/0459-1879-23-550
引用本文: 江守燕, 邓王涛, 孙立国, 杜成斌. 数据驱动的半无限介质裂纹识别模型研究. 力学学报, 待出版. DOI: 10.6052/0459-1879-23-550
Jiang Shouyan, Deng Wangtao, Sun Liguo, Du Chengbin. Data-driven crack identification models in semi-infinite media. Chinese Journal of Theoretical and Applied Mechanics, in press. DOI: 10.6052/0459-1879-23-550
Citation: Jiang Shouyan, Deng Wangtao, Sun Liguo, Du Chengbin. Data-driven crack identification models in semi-infinite media. Chinese Journal of Theoretical and Applied Mechanics, in press. DOI: 10.6052/0459-1879-23-550

数据驱动的半无限介质裂纹识别模型研究

DATA-DRIVEN CRACK IDENTIFICATION MODELS IN SEMI-INFINITE MEDIA

  • 摘要: 缺陷识别是结构健康监测的重要研究内容, 对评估工程结构的安全性具有重要的指导意义, 然而, 准确确定结构缺陷的尺寸十分困难. 论文提出了一种创新的数据驱动算法, 将比例边界有限元法(Scaled Boundary Finite Element Methods, SBFEM)与自编码器(AutoEncoder, AE)、因果膨胀卷积神经网络(Causal Dilated Convolutional Neural Network, CDCNN)相结合用于半无限介质中的裂纹识别. 在该模型中, SBFEM用于模拟波在含不同裂纹状缺陷半无限介质中的传播过程, 对于不同的裂纹状缺陷, 仅需改变裂纹尖端的比例中心和裂纹开口处节点的位置, 避免了复杂的重网格过程, 可高效地生成足够的训练数据. 模拟波在半无限介质中传播时, 建立了基于瑞利阻尼的吸收边界模型, 避免了对结构全域模型进行计算. 搭建了CDCNN, 确保了时序数据的有序性, 并获得更大的感受野而不增加神经网络的复杂性, 可捕捉更多的历史信息, AE具有较强的非线性特征提取能力, 可将高维的原始输入特征向量空间映射到低维潜在特征向量空间, 以获得低维潜在特征用于网络模型训练, 有效提升了网络模型的学习效率. 数值算例表明: 提出的模型能够高效且准确地识别半无限介质中裂纹的量化信息, 且AE-CDCNN模型的识别效率较单CDCNN模型提高了约2.7倍.

     

    Abstract: Structural defect identification is a crucial research area in the field of structural health monitoring, as it plays a significant role in assessing the safety of engineering structures. However, accurately determining the dimensions of structural defects can be extremely challenging. This paper proposes an innovative data-driven algorithm that combines scaled boundary finite element methods (SBFEM) with autoencoders (AE) causal dilated convolutional neural networks (CDCNN) for crack identification in semi-infinite media. In this model, the SBFEM is used to simulate the propagation of waves in semi-infinite media containing different crack-like defects. For different crack-like defects, by simply changing the scale center at the crack tip and the positions of the nodes at the crack opening, complex remeshing processes can be avoided, enabling efficient generation of enough training data. To simulate wave propagation in semi-infinite media, an absorbing boundary model based on Rayleigh damping is established, eliminating the need to compute the entire structural domain. To ensure the temporal order of data and achieve a larger receptive field without increasing the complexity of the neural network, a CDCNN is implemented in the deep learning framework. This allows for capturing more historical information. The AE, with its strong feature extraction capability, is utilized by mapping input samples to a low-dimensional space using its encoder to obtain latent features for training the neural network model, effectively improving the learning efficiency. Numerical examples demonstrate that the proposed model can efficiently and accurately identify quantitative information about cracks in semi-infinite media, and the identification efficiency of the AE-CDCNN model is about 2.7 times higher than that of a single CDCNN model.

     

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