@Article{sdhm.2020.011083,
AUTHOR = {Tianyu Wang, Wael A. Altabey, Mohammad Noori, Ramin Ghiasi},
TITLE = {A Deep Learning Based Approach for Response Prediction of Beam-like Structures},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {14},
YEAR = {2020},
NUMBER = {4},
PAGES = {315--338},
URL = {http://www.techscience.com/sdhm/v14n4/40710},
ISSN = {1930-2991},
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.},
DOI = {10.32604/sdhm.2020.011083}
}