Optimal discrete sizing of truss structures subject to buckling constraints

Abstract

The selective dynamic rounding (SDR) algorithm previously developed by the authors, and based on a dual step rounding approach, is used for the optimal sizing design of truss structures subject to linear buckling constraints. The algorithm begins with a continuous optimum followed by a progressive freezing of individual variables while solving the remaining continuous problems. The allowable member stresses are predicted by the linear regression of the tabular section properties, while the exact allowable compressive stresses are back-substituted for those variables fixed on discrete values in each intermediate mixed-discrete nonlinear problem. It is shown that a continuous design based on the regression analysis of section effectiveness vs. area is effective as a starting point for the dual step discrete optimization phase. A range of examples is used to illustrate that with "conservative" regression, discrete designs can be achieved which are significantly lighter than those in which the variables have been rounded up.

Optimal Design of Steel Towers Using a Multi-Metaheuristic Based Search Method

“…The allowable values of ±1500 kgf /cm 2 are employed for compressive and tensile stresses, and the buckling stress constraints for the compressive members, based on IS-808 code, are considered as follows [29,30]: For kl/r ≤ 120 And if kl/r >120, then…”

An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization

“…The problems include a 15-bar planar truss (Tang, Tong, & Gu, 2005), a 72-bar space truss (Wu & Chow, 1995), and a 160-bar space truss (Groenwold & Stander, 1997) as shown in Fig. 6, Fig.…”

“…The structure is designed for the eight independent load cases, and the design constraints are considered for compression members. The details for these input data are given in (Groenwold & Stander, 1997;Ho-Huu et al, 2016).…”

Layout optimization of simplified trusses using mixed integer linear programming with runtime generation of constraints

“…A number of other means of addressing this are also possible, such as the implicit programming approach of Achtziger (2007), and the post-processing rationalization method used by He and Gilbert (2015). MILP formulations have also been used to incorporate a range of other constraints, including buckling (Groenwold and Stander 1997;Mela 2014), stress constraints (Kanno and Guo 2010), and the requirements of real-world design codes (Van Mellaert et al 2018). Complex real-world and design code constraints have also been studied using metaheuristic methods (e.g.…”