Please use this identifier to cite or link to this item:
|Title:||Optimized column design using genetic algorithm based neural networks|
|Authors:||Rao, H Sudarsana|
Babu, B Ramesh
|Abstract:||In the structural design of columns, the dimensions of the column and reinforcement are initially assumed and then the interaction formula is used to verify the suitability of chosen dimensions and reinforcement. This approach necessitates few trials for coming up with an economical and safe design. This paper demonstrates the applicability of artificial neural networks (ANN) and genetic algorithms (GA) for the design of short columns under biaxial bending. A hybrid neural network model that combines the features of feed forward neural networks and genetic algorithms has been developed for the design of short column subjected to biaxial bending. The network has been trained with design data obtained from design experts in the field. The hybrid neural network model learned the design of column in just 1800 training cycles. After successful learning, the model predicted the percentage of steel required for new problems with good accuracy satisfying all design constraints. The results of the hybrid network model are compared with the solution of an optimizer, which uses the interior penalty function method. The various stages involved in the development of genetic algorithm based neural network model are addressed.|
|ISSN:||0975-1017 (Online); 0971-4588 (Print)|
|Appears in Collections:||IJEMS Vol.13(6) [December 2006]|
Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.