Vol.14, No.4, 2020, pp.315-338, doi:10.32604/sdhm.2020.011083
OPEN ACCESS
ARTICLE
A Deep Learning Based Approach for Response Prediction of Beam-like Structures
  • Tianyu Wang1, Wael A. Altabey1,2, Mohammad Noori3,*, Ramin Ghiasi1
1 International Institute for Urban Systems Engineering, Southeast University, Nanjing, 210096, China
2 Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt
3 Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA, 93405, USA
* Corresponding Author: Mohammad Noori. Email:
Received 18 April 2020; Accepted 21 August 2020; Issue published 04 December 2020
Abstract
Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation of Euler-Bernoulli beam. The Prandtl-Ishlinskii model is introduced as an element of prediction model to simulate the plasticity of this beam structure. Finally the application of the proposed approach is demonstrated through four numerical examples including linear elastic/ideal plastic beam under concentrated/sinusoidal load and elastic/plastic continues beam under seismic load to demonstrate a proof of concept for the effectiveness of this AI-based approach.
Keywords
Beam-like structure; surrogate model; deep neural network; PrandtlIshlinskii model
Cite This Article
Wang, T., Altabey, W. A., Noori, M., Ghiasi, R. (2020). A Deep Learning Based Approach for Response Prediction of Beam-like Structures. Structural Durability & Health Monitoring, 14(4), 315–338.
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