Abstract—To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.
Index Terms—Optimization, genetic algorithm, earthquake, neural networks, self-organizing map, radial basis function.
Alireza Lavaei and Alireza Lohrasbi are with Department of Civil engineering, College of engineering, Boroujerd Branch, Islamic Azad University, Iran (e-mail: Shetab@gmail.com, Ar_lohrasbi@yahoo.com).
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Cite: Alireza Lavaei and Alireza Lohrasbi, "Dynamic Optimization of Structures Subjected to Earthquake," International Journal of Engineering and Technology vol. 8, no. 4, pp. 227-233, 2016.
