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 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.