Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification
Publication: Journal of Engineering Mechanics
Volume 145, Issue 1
Abstract
This study presents a deep convolutional neural network (CNN)-based approach to estimate the dynamic response of a linear single-degree-of-freedom (SDOF) system, a nonlinear SDOF system, and a full-scale 3-story multidegree of freedom (MDOF) steel frame. In the MDOF system, roof acceleration is estimated through the input ground motion. Various cases of noise-contaminated signals are considered in this study, and the conventional multilayer perceptron (MLP) algorithm serves as a reference for the proposed CNN approach. According to the results from numerical simulations and experimental data, the proposed CNN approach is able to predict the structural responses accurately, and it is more robust against noisy data compared with the MLP algorithm. Moreover, the physical interpretation of CNN model is discussed in the context of structural dynamics. It is demonstrated that in some special cases, the convolution kernel has the capability of approximating the numerical integration operator, and the convolution layers attempt to extract the dominant frequency signature observed in the ideal target signal while eliminating irrelevant information during the training process.
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Acknowledgments
The authors would like to thank Dr. Ting-Yu Hsu from the National Taiwan University of Science and Technology in Taiwan for providing the shake-table data collected at NCREE.
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Published In
Journal of Engineering Mechanics
Volume 145 • Issue 1 • January 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 10, 2017
Accepted: Jul 6, 2018
Published online: Oct 31, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 31, 2019
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