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논문 기본 정보

자료유형
학술저널
저자정보
Kanak Kalita (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology) Partha Dey (Academy of Technology) Milan Joshi (SVKM’s NMIMS Mukesh Patel School of Technology Management & Engineering) Salil Haldar (Indian Institute of Engineering Science and Technology)
저널정보
국제구조공학회 Steel and Composite Structures, An International Journal Steel and Composite Structures, An International Journal Vol.32 No.4
발행연도
2019.1
수록면
455 - 466 (12page)

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Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like R2, R2adj, R2pred and Q2F3. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

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