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

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's method. 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-LSTM models 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.