PCA-LSTM:An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning  

作　　者：Yizhao Wang 

机构地区：[1]College of Pipeline and Civil Engineering,China University of Petroleum,Qingdao,266580,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期3029-3045,共17页工程与科学中的计算机建模（英文）

摘　　要：Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.

关 键 词：Impulsive ground-shaking principal component analysis artificial intelligence deep learning impulse recognition 

分 类 号：P315.4[天文地球—地震学]