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LRFD-based design optimization of steel box girder sections using genetic algorithms

  • Structural Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

A design optimization problem for steel box girder sections is formulated and a numerical solution procedure is presented. The formulation is based on the load and resistance factor design (LRFD) procedure. The application of genetic algorithms (GAs) for a class of numerical examples is investigated. The steel box girder may have longitudinal stiffeners on flanges and webs and transverse intermediate stiffeners. Thus, the design variables may be continuous or discrete, which naturally makes the use of GAs attractive. Design constraints represent the LRFD versions of the Standard Specifications of Korean Highway Bridges. The dimensions of concrete slab and other appurtenance are assumed to be pre-determined and minimization of the unit weight of the steel box girder is taken as a design objective. As the numerical optimizers, the Internet version of simple GA (SGA) and micro-GA (μGA) are used and their performance and results are compared. In addition, the sequential quadratic programming algorithm is also applied for purposes of comparison. It is found that μGA with the population size of 5 is a suitable and reliable tool for the design optimization of steel box girder sections. The resulting design can be effectively used as an initial design for practical proportioning

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Authors and Affiliations

  1. Department of Ocean Engineering, Pukyong National University, Busan, Korea

    Yeon-Sun Ryu (Member, Professor), Je-Heon Kim (Research Assistant), Hyun-Man Cho (Research Assistant) & Jeong-Tae Kim (Member, Associate Professer)

Authors
  1. Yeon-Sun Ryu
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  2. Je-Heon Kim
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  3. Hyun-Man Cho
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  4. Jeong-Tae Kim
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Corresponding author

Correspondence to Yeon-Sun Ryu.

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The manuscript for this paper was submitted for review on November 26, 2001.

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Ryu, YS., Kim, JH., Cho, HM. et al. LRFD-based design optimization of steel box girder sections using genetic algorithms. KSCE J Civ Eng 6, 127–134 (2002). https://doi.org/10.1007/BF02829129

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  • DOI: https://doi.org/10.1007/BF02829129

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