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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 6843574, 13 pages
Research Article

An Automated Structural Optimisation Methodology for Scissor Structures Using a Genetic Algorithm

1Department of Mechanics of Materials and Constructions (MeMC) and Department of Architectural Engineering (æ-lab), Vrije Universiteit Brussel (VUB), Brussels, Belgium
2Department of Mechanics of Materials and Constructions (MeMC), VUB, Brussels, Belgium
3BATir Department, Université Libre de Bruxelles (ULB), Bruxelles, Belgium
4Department of Architectural Engineering (æ-lab), VUB, Brussels, Belgium

Correspondence should be addressed to Aushim Koumar;

Received 2 September 2016; Revised 20 December 2016; Accepted 26 December 2016; Published 18 January 2017

Academic Editor: Vahid Hajipour

Copyright © 2017 Aushim Koumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We developed a fully automated multiobjective optimisation framework using genetic algorithms to generate a range of optimal barrel vault scissor structures. Compared to other optimisation methods, genetic algorithms are more robust and efficient when dealing with multiobjective optimisation problems and provide a better view of the search space while reducing the chance to be stuck in a local minimum. The novelty of this work is the application and validation (using metrics) of genetic algorithms for the shape and size optimisation of scissor structures, which has not been done so far for two objectives. We tested the feasibility and capacity of the methodology by optimising a 6 m span barrel vault to weight and compactness and by obtaining optimal solutions in an efficient way using NSGA-II. This paper presents the framework and the results of the case study. The in-depth analysis of the influence of the optimisation variables on the results yields new insights which can help in making choices with regard to the design variables, the constraints, and the number of individuals and generations in order to obtain efficiently a trade-off of optimal solutions.