Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique

Date

2015-08-01

Author

Azad, S. Kazemzadeh
Hasançebi, Oğuzhan

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The guided stochastic search (GSS) is a computationally efficient design optimization technique, which is originally developed for discrete sizing optimization problems of steel trusses with a single displacement constraint under a single load case. The present study aims to investigate the GSS in a more general class of truss sizing optimization problems subject to multiple displacement constraints and load cases. To this end, enhancements of the GSS are proposed in the form of two alternative approaches that enable the technique to deal with multiple displacement/load cases. The first approach implements a methodology in which the most critical displacement direction is considered only when guiding the search process. The second approach, however, takes into account the cumulative effect of all the critical displacement directions in the course of optimization. Advantage of the integrated force method of structural analysis is also utilized for further reduction of the computational effort in these approaches. The proposed enhancements of GSS are investigated and compared with some selected techniques of design optimization through six truss structures that are sized for minimum weight. The numerical results reveal that both enhancements generally provide promising solutions with an insignificant computational effort.

Subject Keywords

Structural optimization, Discrete sizing, Guided stochastic search, Integrated force method, Steel trusses, AISC-LRFD specifications

URI

https://hdl.handle.net/11511/40199

Journal

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

DOI

https://doi.org/10.1007/s00158-015-1233-0

Collections

Department of Civil Engineering, Article

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Citation Formats

S. K. Azad and O. Hasançebi, “Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique,” STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, pp. 383–404, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40199.