Steel and Composite Structures

  • 제29권2호
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  • Pages.243-256
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  • 2018
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Computational Lagrangian Multiplier Method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams

  • Shariat, Mehran (Department of Civil Engineering, Hakim Sabzevari University) ;
  • Shariati, Mahdi (Faculty of Civil Engineering, University of Tabriz) ;
  • Madadi, Amirhossein (Department of Civil Engineering, Hakim Sabzevari University) ;
  • Wakil, Karzan (Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University)
  • 투고 : 2018.06.12
  • 심사 : 2018.09.04
  • 발행 : 2018.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study conducts an optimization and sensitivity analysis on rectangular reinforced concrete (RC) beam using Lagrangian Multiplier Method (LMM) as programming optimization computer soft ware. The analysis is conducted to obtain the minimum design cost for both singly and doubly RC beams according to the specifications of three regulations of American concrete institute (ACI), British regulation (BS), and Iranian concrete regulation (ICS). Moreover, a sensitivity analysis on cost is performed with respect to the effective parameters such as length, width, and depth of beam, and area of reinforcement. Accordingly, various curves are developed to be feasibly utilized in design of RC beams. Numerical examples are also represented to better illustrate the design steps. The results indicate that instead of complex optimization relationships, the LMM can be used to minimize the cost of singly and doubly reinforced beams with different boundary conditions. The results of the sensitivity analysis on LMM indicate that each regulation can provide the most optimal values at specific situations. Therefore, using the graphs proposed for different design conditions can effectively help the designer (without necessity of primary optimization knowledge) choose the best regulation and values of design parameters.

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