Seismic Stability Assessment of Single-Layer Reticulated Dome Structures by the Development of Deep Learning Action Recognition Network

Abstract

Structural seismic stability is an important content of research in the field of structural engineering safety. Current methods of seismic stability assessment of single-layer reticulated dome structures, supporting engineering decisions regarding the strengthening, repair, or demolition of structures, are complex and do not facilitate engineering applications, so the deep learning action recognition networks to analyze the structural deformation are employed and assessment of the seismic stability. In order to tackle the problem of an insufficient receptive field of structural global change during the dynamic response process within networks, a Dual-Branch Attention Module (DBAM) is innovatively proposed, which enables the effective perception of the global deformation of reticulated dome structures. The DBAM consists of the Maxpooling Channel Attentional (MCA) branch and the Large Kernel Pyramid Attentional (LKPA) branch, which can provide the network with multi-scale global perceptual information, thus enhancing the recognition ability of the model. In addition, the ReticDomeSeismic dataset is created by the mapping relations from the displacement intervals to RGB colors proposed, which contains a large amount of video data on the seismic analysis of single-layer reticulated dome structures under different parameters. The dataset was employed to verify the proposed DBAM method, and the experimental results show that the DBAM improves the Mean Accuracy of base action recognition methods by 4.37% on average, the highest Top-1 Accuracy of 93.48%. Therefore, the method proposed for structural deformation recognition can quickly and accurately assess the seismic stability of single-layer reticulated dome structures, and also provides significant insights and guidance for engineering practice.