Deep learning for computer vision in pulse‐like ground motion identification

Near-fault pulse-like ground motions can cause severe damage to long-period engineering structures. A rapid and accurate identification method is essential for seismic design. Deep learning offers a solution by framing pulse-like motion identification as an image classification task. However, the application of deep learning models faces multiple challenges from data and models for pulse-like motion classification. This study focuses on suitable input images and model architecture optimization through a comprehensive strategy. The diverse datasets are realized by transferring the original time history into Morlet wavelet time-frequency diagram, anomaly-marked velocity time history, Fourier amplitude spectrum and its smoothed diagram, and pixel fusion diagrams. Two types of deep learning models are constructed in the image classification task for these datasets. A convolutional neural network (CNN) is enhanced by integrating the self-attention mechanism (SAM) to concentrate on local image features. Additionally, a seismic parameter layer is added to this enhanced model to reduce reliance on input data features. Visual Transformers, including Vision Transformer (ViT) and Swin Transformer (SwinT), are adopted in this task as well. The results of the enhanced CNN demonstrate that TF outperforms other images with higher classification accuracy and convergence speed, and dual-input image presents inferior performance. The accuracy of all input datasets under the constraint of a single-parameter moment magnitude (Mw) is higher than that under the constraint of rupture distance (Rrup). The accuracy under the two-parameter constraint of Mw and Rrup is higher than that of the single parameter constraint for all input datasets, in which the accuracy from TF is the highest, and that from dual-input data is improved. The performance of SwinT is similar to CNN+SAM and better than ViT for single-input images, in which TF presents the highest accuracy. For dual-input images, ViT is better than SwinT, and both of them are better than CNN+SAM. In a resource-limited environment, the enhanced CNN with single-input TF is the best strategy, and the physical constraint of Mw and Rrup is more effective, especially for the dual-input images.