Development and Application of a Self-Attention Residual Network Model in Diagnosing Abnormal Structural Health Monitoring Data

In the structural health monitoring system, a large amount of testing data from sensors has been used to detect the health status of a structure. However, due to factors such as sensor failure and electromagnetic interference, the data collected by the structural health monitoring system may exhibit various abnormal states, which will affect the accuracy of structural damage identification and state assessment. How to process large amounts of data with high accuracy and recognize those abnormal data with high efficiency poses significant challenges. This paper proposes an innovative deep-learning method for data abnormality diagnosis by designing and establishing a self-attention residual network (SARN) model. In the method, all the monitoring data, including the abnormal data, are first converted into a time–frequency diagram by continuous wavelet transform, which are then used as the input of the SARN model for training. Then the convolutional layer, batch normalization, max pooling layer, the set self-attention residual building unit-1, and unit-2 are established successively to process the data, and finally the abnormal data can be identified by deep-learning technique. The proposed method broadens the receptive field of convolutional neural networks limited by the size of convolution kernels, allowing for the extraction of richer global features of data, thereby enabling high-precision automatic diagnosis and classification of a large amount of abnormal monitoring data. As an application case, a long-span cable-stayed bridge being monitored is introduced, and its acceleration monitoring data during one month are used as a dataset, and the developed SARN model is used for data anomaly diagnosis. The test accuracy of the model is found to be 97.80%, indicating its efficiency and accuracy in identifying abnormal data in the structural health monitoring dataset.