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Optimization of geometrically nonlinear lattice girders. Part I: considering member strengths
In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s.
Optimization of geometrically nonlinear lattice girders. Part I: considering member strengths
In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s.
Optimization of geometrically nonlinear lattice girders. Part I: considering member strengths
Tugrul Talaslioglu (Autor:in)
2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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