An Easy-To-Update Pulse-Like Ground Motion Identification Method Based on Siamese Convolutional Neural Networks.
- Resource Type
- Article
- Authors
- Zhao, Guochen; Xu, Longjun; Lin, Shibin; Lai, Qinghui; Zhu, Xingji; Xie, Lili
- Source
- Journal of Earthquake Engineering. 2024, Vol. 28 Issue 1, p1-19. 19p.
- Subject
- *GROUND motion
*CONVOLUTIONAL neural networks
*FEATURE extraction
*IMAGE recognition (Computer vision)
*MOTION
- Language
- ISSN
- 1363-2469
A pulse-like ground motion identification method is proposed based on the Siamese Convolutional Neural Networks (SCNNs). The wavelet coefficient graphs of pulse-like ground motions are used as the input data, and the features extracted by the SCNNs are used for the identification. Based on the time-domain features, pulse-like and non-pulse-like ground motions are classified into several classes. The results indicated that all the identified pulse-like ground motions have similar pulse features to the pre-selected training data. The principal advantage of the method is that the misclassification problem can be minimized by an updating procedure proposed by this paper. [ABSTRACT FROM AUTHOR]