Training and using neural networks to represent heuristic design knowledge
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A hybrid genetic algorithm for reinforced concrete flat slab buildings
Computers and Structures
Computationally efficient analysis of cable-stayed bridge for GA-based optimization
Engineering Applications of Artificial Intelligence
Knowledge and Information Systems
ANN-based GA for generating the sizing curve of stand-alone photovoltaic systems
Advances in Engineering Software
A fuzzy integrated methodology for evaluating conceptual bridge design
Expert Systems with Applications: An International Journal
Design of reinforced concrete road vaults by heuristic optimization
Advances in Engineering Software
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The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design responses which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained using grillage analysis program for different bridge deck configurations and the correlation between sectional parameters and design responses has been established. Subsequently, GA is employed for arriving at optimum configuration of the bridge deck system by minimizing the total cost. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on cost optimization of T-girder bridge deck system for different spans. The method presented in this paper, would greatly reduce the computational effort required to find the optimum solution and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error.