Dynamic displacement estimation of structures using one-dimensional convolutional neural network

Frontiers in Physics

Physics

Yuanpeng He¹  Xin Zhou² 

[1] Faculty of Geosciences and Ground Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China;

关键词: convolutional neural network;    displacement estimation;    acceleration;    visualization;    portable measurement;   

DOI  :  10.3389/fphy.2023.1290880

 received in 2023-09-08, accepted in 2023-10-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

For large infrastructures, dynamic displacement measurement in structures is an essential topic. However, limitations imposed by the installation location of the displacement sensor can lead to measurement difficulties. Accelerometers are characterized by easy installation, good stability and high sensitivity. For this regard, this paper proposes a structural dynamic displacement estimation method based on a one-dimensional convolutional neural network and acceleration data. It models the complex relationship between acceleration signals and dynamic displacement information. In order to verify the reliability of the proposed method, a finite element-based frame structure was created. Accelerations and displacements were collected for each node of the frame model under seismic response. Then, a dynamic displacement estimation dataset is constructed using the acceleration time series signal as features and the displacement signal at a certain moment as target. In addition, a typical neural network was used for a comparative study. The results indicated that the error of the neural network model in the dynamic displacement estimation task was 9.52 times higher than that of the one-dimensional convolutional neural network model. Meanwhile, the proposed modelling scheme has stronger noise immunity. In order to validate the utility of the proposed method, data from a real frame structure was collected. The test results showed that the proposed method has a mean square error of only 5.097 in the real dynamic displacement estimation task, which meets the engineering needs. Afterwards, the outputs of each layer in the dynamic displacement estimation model are visualized to emphasize the displacement calculation process of the convolutional neural network.

【 授权许可】

Unknown   
Copyright © 2023 Zhou and He.