Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure-Reference-Cited by-同舟云学术

Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

Published:2022-07-15 Issue:0 Volume:0 Page:
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Lu Jing¹,Chen Qinyuan¹,Mohamed Hamdy²

Affiliation:

1. School of Information and Architectural Engineering , Anhui Open University , Hefei , , China

2. College of Engineering , Applied Science University , Bahrain

Abstract

Abstract This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furthermore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2022.2.00016

Reference10 articles.

1. He, H. X., Zheng, J. C., Liao, L. C., & Chen, Y. J. Damage identification based on convolutional neural network and recurrence graph for beam bridge. Structural Health Monitoring., 2021; 20(4): 1392–1408

2. Liu, Z., Jiang, A., Shao, W., Zhang, A., & Du, X. Artificial-Neural-Network-Based Mechanical Simulation Prediction Method for Wheel-Spoke Cable Truss Construction. International Journal of Steel Structures., 2021; 21(3): 1032–1052

3. Tan, Z. X., Thambiratnam, D. P., Chan, T. H., Gordan, M., & Abdul Razak, H. Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network. Structure and Infrastructure Engineering., 2020; 16(9): 1247–1261

4. Xia, Z., Lin, Y., Wang, Q., & Fan, Q. Damage detection method for cables based on the change rate of wavelet packet total energy and a neural network. Journal of Civil Structural Health Monitoring., 2021; 11(3): 593–608

5. Sun, S., Liu, Y., & Sharaf Eldean, M. A. Design and implementation of an optical fiber sensing based vibration monitoring system. Journal of Vibroengineering., 2021; 23(2): 496–511