Structural and Multidisciplinary Optimization

, Volume 34, Issue 3, pp 243–260 | Cite as

Least-cost design of singly and doubly reinforced concrete beam using genetic algorithm optimized artificial neural network based on Levenberg–Marquardt and quasi-Newton backpropagation learning techniques

Industrial Applications

Abstract

In this work, least-cost design of singly and doubly reinforced beams with uniformly distributed and concentrated load was done by incorporating actual self-weight of beam, parabolic stress block, moment–equilibrium and serviceability constraint besides other constraints. Also, this design expertise was incorporated into a genetically optimized artificial neural network based on steepest descent, Levenberg–Marquardt, and quasi-Newton backpropagation learning techniques. The initial solution for the optimization procedure was obtained using limit state design as per IS: 456-2000.

Keywords

Reinforced concrete Cost optimization Beam design Artificial neural network Genetic algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologyKurukshetraIndia
  2. 2.Thapar Institute of Engineering and TechnologyPatialaIndia

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