Li Zhaoyang, Xu Ping, Xing Jie, Yang Chengxing
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Key Laboratory for Track Traffic Safety of Ministry of Education, Central South University, Changsha 410075, China.
Sensors (Basel). 2022 Mar 18;22(6):2358. doi: 10.3390/s22062358.
Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based on specific damage index needs the designer to have rich experience and background knowledge, and the designed damage index is hard to apply to different structures. In this paper, a U-shaped efficient structural strain damage identification network SDFormer (structural damage transformer) based on self-attention feature is proposed. SDFormer regards the problem of structural strain damage identification as an image segmentation problem, and introduces advanced image segmentation technology for structural damage identification. This network takes the strain field map of the structure as the input, and then outputs the predicted damage location and level. In the SDFormer, the low-level and high-level features are smoothly fused by skip connection, and the self-attention module is used to obtain damage feature information, to effectively improve the performance of the model. SDFormer can directly construct the mapping between strain field map and damage distribution without complex damage index design. While ensuring the accuracy, it improves the identification efficiency. The effectiveness and accuracy of the model are verified by numerical experiments, and the performance of an advanced convolutional neural network is compared. The results show that SDFormer has better performance than the advanced convolutional neural network. Further, an anti-noise experiment is designed to verify the anti-noise and robustness of the model. The anti-noise performance of SDFormer is better than that of the comparison model in the anti-noise experimental results, which proves that the model has good anti-noise and robustness.
损伤识别是结构健康监测领域的一个关键问题，对于提高工程结构的可靠性和安全性具有重要意义。过去，基于特定损伤指标的结构应变损伤识别方法需要设计者具备丰富的经验和背景知识，且所设计的损伤指标难以应用于不同结构。本文提出了一种基于自注意力特征的U形高效结构应变损伤识别网络SDFormer（结构损伤变换器）。SDFormer将结构应变损伤识别问题视为图像分割问题，并引入先进的图像分割技术用于结构损伤识别。该网络以结构的应变场图为输入，然后输出预测的损伤位置和程度。在SDFormer中，通过跳跃连接将低级和高级特征进行平滑融合，并使用自注意力模块获取损伤特征信息，以有效提高模型性能。SDFormer无需复杂的损伤指标设计即可直接构建应变场图与损伤分布之间的映射。在保证准确性的同时，提高了识别效率。通过数值实验验证了模型的有效性和准确性，并与一种先进的卷积神经网络的性能进行了比较。结果表明，SDFormer比先进的卷积神经网络具有更好的性能。此外，设计了一个抗噪声实验来验证模型的抗噪声能力和鲁棒性。在抗噪声实验结果中，SDFormer的抗噪声性能优于对比模型，这证明该模型具有良好的抗噪声能力和鲁棒性。
