Affiliation:
1. Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2. Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
3. School of Civil Engineering, Yantai University, Yantai 264005, China
Abstract
A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is required to determine if the selected filter passband is appropriate. A better method is to use a traversal search combined with visual inspection to determine the cut-off frequency, which is the traditional method. However, this method is inefficient and unsuitable for processing massive strong-motion records. In this study, convolutional neural networks (CNNs) were used to replace visual inspection to achieve the automatic judgment of the reasonableness of the filtered displacement time series. This paper chose the pre-trained deep neural network (DNN) models VGG19, ResNet50, InceptionV3, and InceptionResNetV2 for transfer learning, which are only trained in the fully connected layer or in all network layers. The effect of adding probability constraints on the results when predicting categories was analyzed as well. The results obtained through the VGG19 model, in which all network layers are trained and probability constraints are added to the prediction, have the lowest errors compared to the other models. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are 0.82, 0.038, 0.026, and 2.99%, respectively.
Funder
the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration
the Natural Science Foundation of Heilongjiang Province
the National Key Research and Development Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
2 articles.
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