An Easy-To-Update Pulse-Like Ground Motion Identification Method Based on Siamese Convolutional Neural Networks
Guochen Zhao, Longjun Xu, Shibin Lin, Qinghui Lai, Xingji Zhu, Lili Xie
Abstract
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.
Topics & Concepts
Pulse (music)Convolutional neural networkIdentification (biology)Pattern recognition (psychology)Computer scienceArtificial intelligenceWaveletTime domainArtificial neural networkMotion (physics)Principal component analysisGround motionComputer visionEngineeringTelecommunicationsStructural engineeringBotanyDetectorBiologyStructural Health Monitoring TechniquesSeismic Performance and AnalysisGeotechnical Engineering and Underground Structures