Functional Link Neural Network Learning for Response Prediction of Tall Shear Buildings With Respect to Earthquake Data

This paper proposes the application of functional link neural networks (FLNNs) for structural response prediction of tall buildings due to seismic loads. The ground acceleration data are taken as input, and structural responses of different floors of multistorey shear buildings are considered as out...

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Published inIEEE transactions on systems, man, and cybernetics. Systems Vol. 48; no. 1; pp. 1 - 10
Main Authors Sahoo, Deepti Moyi, Chakraverty, Snehashish
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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ISSN2168-2216
2168-2232
DOI10.1109/TSMC.2017.2700334

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Abstract This paper proposes the application of functional link neural networks (FLNNs) for structural response prediction of tall buildings due to seismic loads. The ground acceleration data are taken as input, and structural responses of different floors of multistorey shear buildings are considered as output. It is worth mentioning that handling of large earthquake data has become a great challenge in the design of tall structures viz., that of shear buildings. As such, here, a functional expansion block in FLNN has been used along with efficient Chebyshev and Legendre polynomials. Training is done with one earthquake data set, and testing is done with different intensities of other earthquake data sets; and it is seen that FLNN can very well predict the structural response of different floors of multistorey shear buildings subject to earthquake data. Results of the FLNN are compared with a multilayer neural network (MNN), and it is found that the FLNN gives better accuracy and takes less computation time compared to MNN, which shows the computational efficiency of FLNN over MNN. Numerical examples of two-, five-, and ten-storey buildings are considered, and corresponding results are presented in the form of tables and plots.
AbstractList This paper proposes the application of functional link neural networks (FLNNs) for structural response prediction of tall buildings due to seismic loads. The ground acceleration data are taken as input, and structural responses of different floors of multistorey shear buildings are considered as output. It is worth mentioning that handling of large earthquake data has become a great challenge in the design of tall structures viz., that of shear buildings. As such, here, a functional expansion block in FLNN has been used along with efficient Chebyshev and Legendre polynomials. Training is done with one earthquake data set, and testing is done with different intensities of other earthquake data sets; and it is seen that FLNN can very well predict the structural response of different floors of multistorey shear buildings subject to earthquake data. Results of the FLNN are compared with a multilayer neural network (MNN), and it is found that the FLNN gives better accuracy and takes less computation time compared to MNN, which shows the computational efficiency of FLNN over MNN. Numerical examples of two-, five-, and ten-storey buildings are considered, and corresponding results are presented in the form of tables and plots.
Author Sahoo, Deepti Moyi
Chakraverty, Snehashish
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Snippet This paper proposes the application of functional link neural networks (FLNNs) for structural response prediction of tall buildings due to seismic loads. The...
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SubjectTerms Acceleration
Aseismic buildings
Buildings
Chebyshev approximation
Computing time
Earthquake
Earthquake prediction
Earthquakes
Floors
functional link neural network (FLNN)
Multi-layer neural network
multilayer neural network (MNN)
Multilayers
multistorey shear buildings
Multistory buildings
Neural networks
Polynomials
response
Seismic engineering
Shear
structure
Tall buildings
Training
Title Functional Link Neural Network Learning for Response Prediction of Tall Shear Buildings With Respect to Earthquake Data
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