The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Optimum design of structures by an improved genetic algorithm using neural networks
Advances in Engineering Software - Selected papers from civil-comp 2003 and AlCivil-comp 2003
Advances in Engineering Software
Parametric Model Based on GA and SVM
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Nonlinear analysis and optimal design of structures via force method and genetic algorithm
Computers and Structures
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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Optimum design of a cable-stayed bridge structure is very complicated because of large number of design variables. Use of genetic algorithms (GAs) in optimizing such structure consumes significant computational time. Due to nonlinearity, structural analysis itself takes considerable computational time and the genetic algorithm has to perform a large number of iterations in order to obtain global minima. A new approach combining GA and support vector machine (SVM) has been adopted. This drastically reduces the computation time of optimization. The genetic algorithm is employed to obtain the minimum cost of the cable-stayed bridge. Constraint evaluation is done using SVM which is trained by a data base generated through FEM analysis. System level optimization is carried out considering configuration and cross-sectional parameters as design variables. In the present study, optimization was carried out for bridge lengths ranging from 100 to 500m. Final optimum designs were reanalyzed to check the adequacy of the developed approach.