Wang Yameng, Wang Lihua, Ye Wenjing, Zhang Fengyi, Pan Yongdong, Li Yan
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.
Materials (Basel). 2024 Jul 25;17(15):3685. doi: 10.3390/ma17153685.
Concrete structures frequently manifest diverse defects throughout their manufacturing and usage processes due to factors such as design, construction, environmental conditions and distress mechanisms. In this paper, a multilevel convolutional neural network (CNN) combined with array ultrasonic testing (AUT) is proposed for identifying the locations of hole defects in concrete structures. By refining the detection area layer by layer, AUT is used to collect ultrasonic signals containing hole defect information, and the original echo signal is input to CNN for the classification of hole locations. The advantage of the proposed method is that the corresponding defect location information can be obtained directly from the input ultrasonic signal without manual discrimination. It effectively addresses the issue of traditional methods being insufficiently accurate when dealing with complex structures or hidden defects. The analysis process is as follows. First, COMSOL-Multiphysics finite element software is utilized to simulate the AUT detection process and generate a large amount of ultrasonic echo data. Next, the extracted signal data are trained and learned using the proposed multilevel CNN approach to achieve progressive localization of internal structural defects. Afterwards, a comparative analysis is conducted between the proposed multilevel CNN method and traditional CNN approaches. The results show that the defect localization accuracy of the proposed multilevel CNN approach improved from 85.38% to 95.27% compared to traditional CNN methods. Furthermore, the computation time required for this process is reduced, indicating that the method not only achieves higher recognition precision but also operates with greater efficiency. Finally, a simple experimental verification is conducted; the results show that this method has strong robustness in recognizing noisy ultrasonic signals, provides effective solutions, and can be used as a reference for future defect detection.
由于设计、施工、环境条件和损伤机制等因素，混凝土结构在其制造和使用过程中经常会出现各种缺陷。本文提出了一种结合阵列超声检测（AUT）的多级卷积神经网络（CNN），用于识别混凝土结构中孔洞缺陷的位置。通过逐层细化检测区域，利用AUT采集包含孔洞缺陷信息的超声信号，并将原始回波信号输入CNN进行孔洞位置分类。该方法的优点是可以直接从输入的超声信号中获得相应的缺陷位置信息，无需人工判别。它有效地解决了传统方法在处理复杂结构或隐藏缺陷时不够准确的问题。分析过程如下。首先，利用COMSOL-Multiphysics有限元软件模拟AUT检测过程，生成大量超声回波数据。其次，使用所提出的多级CNN方法对提取的信号数据进行训练和学习，以实现内部结构缺陷的渐进定位。然后，将所提出的多级CNN方法与传统CNN方法进行对比分析。结果表明，与传统CNN方法相比，所提出的多级CNN方法的缺陷定位准确率从85.38%提高到了95.27%。此外，该过程所需的计算时间减少，表明该方法不仅实现了更高的识别精度，而且运行效率更高。最后，进行了简单的实验验证；结果表明，该方法在识别有噪声的超声信号方面具有很强的鲁棒性，提供了有效的解决方案，可为未来的缺陷检测提供参考。
