A channel-spatial-temporal attention-based network for vibration-based damage detection

Structural health monitoring (SHM) is extremely vital for the diagnosis and prognosis of civil structures. As an important part of the SHM system, vibration-based damage detection (VBDD) methods have become a research hotspot with the development of sensor technologies. These methods are utilized to assess structural conditions or localize and classify damages. Recently end-to-end deep learning architectures have been widely used in VBDD tasks and achieved state-of-the-art results. However, there are seldom investigations on the attention mechanism in VBDD, which has been demonstrated as an effective module to extract features in other domains. In this paper, we propose a channel-spatial-temporal attention-based network to refine and enrich the discriminative sample-specific features in three dimensions, namely, channel, space, and time simultaneously. Specifically, the local and global block we designed is to extract the local and global spatial features adaptively, and the grouped self-attention is presented to extract the long- and short-term temporal features. Moreover, the squeeze-and-excitation block is selected to emphasize vital channels. Extensive experiments are conducted on three-span continuous rigid frame bridge scale model and IASC-ASCE benchmark datasets, and the results prove that the proposed method is superior to the existing state-of-the-art methods.