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Computer-aided design and optimization of steel structural systems

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Warianty tytułu
Języki publikacji
PL
Abstrakty
EN
A new computer-aided methodology for design and optimization of steel structures based on hybrid genetic algorithm with gradient learning of the best individual has been reported. The optimum design problem is formulated as the structural-parametric mathematical programming task with Boolean, integer and real design variables. In this way, cross sectional sizes of structural members, node coordinates as well as topology parameters can be considered as design variables. The system of constraints includes load-carrying capacity and stiffness conditions for structural members and entire steel construction according to building standards and regulations. Architectural, technological and other requirements can be integrated to constraint system as well. Determination of purpose function takes into account design specifications and ability to formulate the analytical expression as function of design variables. Hybrid genetic algorithm based on the parallel operations of genetic operators and update gradient method was used for solving the structural-parametric optimization task. Proposed technique was realized with elaborated software. Numerical example with new optimal design decision of plane two-hinged transverse frame with lattice structural members demonstrates the effectiveness of the proposed optimization methodology.
Twórcy
  • Lviv Politechnic National University, Lviv, Ukraine
Bibliografia
  • [1] Bendsøe M. P., Optimization of structural topology, shape and materials, Berlin, Springer-Verlag, 1995.
  • [2] Czarnecki S., Multithreaded genetic program in truss shape optimization, Theoretical Foundations of Civil Engineering, VIII, 556-560, 2000.
  • [3] Davis L., Smith D., Adaptive design for layout synthesis, Dallas, Texas Instruments, 1985.
  • [4] ДБH B.1.2-2:2006. Loads and effects. Design requirements. - Kiev, MINBUD, 2006. (in Ukrainian)
  • [5] Gen M., Cheng R., Genetic algorithms and engineering design, John Wiley & Sons. 1997.
  • [6] Goldberg D. E., Genetic algorithms in search, optimization and machine learning, Reading, MA, Addison-Wesley, 1989.
  • [7] Jenkins W. M., Towards structural optimization via the genetic algorithm, Computers and Structures, 40 (5), 1321-1327,1991.
  • [8] Haug A., Arora J., Applied Optimal Design; Mechanical Systems and Structures, Moscow, Mir, 1983 (in Russian).
  • [9] Holland J. H., Adaptation in natural and artificial systems, Ann Arbor, University of Michigan Press, 1975.
  • [10] Kirsch U., Optimum Structural Design, New York, McGraw-Hill, 1981.
  • [11] Permyakov V. O., Yurchenko V. V., Peleshko I. D., An Optimum Structural Computer-Aided Design Using Hybrid Genetic Algorithm, Proceeding of the International Conference "Progress in Steel, Composite and Aluminium Structures", Gizejowski, Kozlowski, Sleczka & Ziolko (eds.), Taylor & Francis Group, London, 819-826, 2006.
  • [12] Permyakov V. A., Perelmuter A. V., Yurchenko V. V. Optimal designing of steel structural systems. Kiev, Publisher "Steel", 2008. (in Russian)
  • [13] Peleshko I., Yurchenko V., An optimum structural computer-aided design using update gradient method, Modem Building Materials, Structures and Techniques-. Proc. 8th Int. Conf., Vilnius, 2004.
  • [14] CHиП II-23-81*. Steel structures. - M.: 1996, - 96 p. (in Russian)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BTB2-0055-0125
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