Optimization of Metallic Structures by Applying Genetic Algorithm

Hassaine, M. I. E. Terki; Bourdim, Sidi Mohammed El-Amine; Benanane, Abdelkader; Zelmat, Yassine

doi:10.1007/978-3-030-35533-3_41

Cited by 1 publication

(1 citation statement)

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“…Design notes were prepared manually and as new and complex requirements and demands arose, much more urgent actions were taken to meet these requirements. Progress has been made in the construction technology sector, especially in the search for productivity gains, which has led to two main thrusts [1]:  Industrialization, therefore prefabrication of building elements, with the verification of the validity of an object previously designed and checking its operation afterwards.…”

Section: Introductionmentioning

confidence: 99%

Push-over Analysis of Optimized Steel Frames

Hassaine¹,

Bourdim²,

Varum

et al. 2022

Eng. Technol. Appl. Sci. Res.

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The traditional optimization methods are effective when dealing with small-scale problems. However, for large-scale problems, these methods fail to obtain optimal solutions, and after a long operation, several solutions are obtained. New methods, known as metaheuristics, have provided new implementations to be used in many applications. They have enabled the resolution of many complex industrial and technical problems. They have the merits of avoiding local optima and finding optimal solutions, due to their ease of understanding, flexibility, adaptation simplicity, and ability to get out of local optima traps. This article aims to model a 2D metal frame gantry with two spans and two levels already optimized by ROBOT Millennium software in order to show the effect of structural optimization in the pre-design phase and of obtaining its non-linear behavior by the pushover method. Three optimal dimensional configurations of this gantry were taken into account and the best was chosen, one which satisfied an adequate behavior in the non-linear domain while respecting the CM66 and Eurocode3 regulations.

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Section: Introductionmentioning

confidence: 99%