江守燕, 万晨, 储冬冬. 基于数据驱动的薄板结构多裂纹反演方法. 力学与实践, 2023, 45(3): 632-643. DOI: 10.6052/1000-0879-22-375
引用本文: 江守燕, 万晨, 储冬冬. 基于数据驱动的薄板结构多裂纹反演方法. 力学与实践, 2023, 45(3): 632-643. DOI: 10.6052/1000-0879-22-375
Jiang Shouyan, Wan Chen, Chu Dongdong. Multiple crack detection method for thin-plate structures based on data-driven algorithm. Mechanics in Engineering, 2023, 45(3): 632-643. DOI: 10.6052/1000-0879-22-375
Citation: Jiang Shouyan, Wan Chen, Chu Dongdong. Multiple crack detection method for thin-plate structures based on data-driven algorithm. Mechanics in Engineering, 2023, 45(3): 632-643. DOI: 10.6052/1000-0879-22-375

基于数据驱动的薄板结构多裂纹反演方法

MULTIPLE CRACK DETECTION METHOD FOR THIN-PLATE STRUCTURES BASED ON DATA-DRIVEN ALGORITHM

  • 摘要: 采用比例边界有限元法 (scaled boundary finite element methods,SBFEM) 模拟薄板结构内Lamb波的传播过程,将SBFEM和最大信息系数相结合,研究了缺陷参数与观测点位置的相关性,为缺陷反演时传感器布置位置的选取提供了依据。在此基础上建立了基于SBFEM数据集和深度学习的结构内部多裂纹反演方法,将多裂纹反演归类为分类和回归预测问题,可在未知裂纹数量的情况下反演出裂纹的数量、位置和大小,并通过数值算例验证了该方法能够较好地进行裂纹状缺陷数量和参数的反演。

     

    Abstract: In this paper, the scaled boundary finite element methods (SBFEM) are used to simulate the propagation process of Lamb wave in thin-plate structures, and the correlation between the defect parameters and the location of observation points when the Lamb wave propagates in the structure is studied by combining the SBFEM and the maximum information coefficient, which provides a basis for the selection of sensor location during defect inversion. On this basis, a multiple crack inversion method for thin-plate structure based on SBFEM data sets and deep learning is established. The multiple crack inversion is classified as a classification and regression prediction problem, which can inverse the number, location and size of cracks without any prior knowledge about the number of cracks. Finally, the performance of the model is verified by numerical examples. The proposed method can better classify the number of crack-like defects and identify their parameters.

     

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