2014
DOI: 10.2478/bpasts-2014-0011
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Graph based discrete optimization in structural dynamics

Abstract: Abstract. In this study, a relatively simple method of discrete structural optimization with dynamic loads is presented. It is based on a tree graph, representing discrete values of the structural weight. In practical design, the number of such values may be very large. This is because they are equal to the combination numbers, arising from numbers of structural members and prefabricated elements. The starting point of the method is the weight obtained from continuous optimization, which is assumed to be the l… Show more

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Cited by 8 publications
(8 citation statements)
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References 26 publications
(24 reference statements)
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“…The first one involves direct coupling of algorithms for discrete optimization and reliability analysis, as proposed in, e.g. [21,22,23,24,25,26]. The second class of approaches consists of constructing a surrogate model for reliability, thus rendering the application of the optimization algorithm trivial, see e.g.…”
Section: Introductionmentioning
confidence: 99%
“…The first one involves direct coupling of algorithms for discrete optimization and reliability analysis, as proposed in, e.g. [21,22,23,24,25,26]. The second class of approaches consists of constructing a surrogate model for reliability, thus rendering the application of the optimization algorithm trivial, see e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Constraints were imposed on stresses, displacement and accelerations. According the algorithm presented in paper [3], in order to find minimum weight of the composite floor the following steps have been proceeded:…”
Section: Numerical Examplementioning
confidence: 99%
“…In this paper, an alternative approach has been proposed. It is based on graph representation of the structural optimization problem and is described in detail in previous authors' papers [2,3].…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, this method is laborious and does not guarantee optimum results, especially when the number of independent parameters is large. Automated geometry optimization is therefore highly desirable, however, quite challenging [37][38][39]. Majority of conventional techniques (such as gradient-based algorithms or derivative free methods, e.g., pattern search algorithms) require considerable number of objective function evaluations (and, associated EM simulations) to yield an optimized design [4].…”
Section: Introductionmentioning
confidence: 99%