Optimization of Geometrically Nonlinear Lattice Girders. Part I: Considering Member Strengths

Abstract

In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s.

Design Optimization of Lattice Girders According to Member and Joint-Related Design Constraints

“…Because, their torsional strengths are higher than those with open cross-sections [13][14][15][16]. It was shown that an execution of this generational sizing optimization procedure causes to decrease the number of feasible solutions [17]. Furthermore, it was also demonstrated that involving the shape and geometry of lattice girder into the optimal design procedure leads to the exploration of optimal design with higher economic, serviceability and load-resistance capability.…”

“…Furthermore, the proposed multi-objective optimization approach has to also be capable of handling with the usage of mixing type of design variables due to the discrete nature of steel structures. Furthermore, it must own a simple but an effective search mechanism in order to reduce the computing time in the exploration of optimal designs with higher quality as well [17].…”

“…Hence, the higher complex nonlinear structural problems that exhibits snap-through or snap-back behavior are easily solved through its capability of easily predicting the limit points (bifurcation, branching and etc.). An improved multi-objective optimization approach (ImpNSGAII) is utilized to execute the optimization-related computations [17]. The fundamentals of ImpNSGAII are constituted on the computing procedure of nondominated sorting genetic algorithm II (NSGAII), which is basically governed by the genetic operators.…”

Design Optimization of Tubular Lattice Girders

“…Therefore, the lattice girders are preferably utilized in a number of structural systems (roofs, bridge, cranes etc.) (Nageim & Macginley [1], Talaslioglu [2]).…”

Analytical and Experimental Investi Gati on of Cold-Formed Steel Beam-to-Column Bolte D Gusset-Plate Joints

“…The researches of steel frames with semi-rigid joints showed that the stiffness of joints had a significant influence on the behaviour of frame elements (Del Savio et al 2009;Díaz et al 2011;Daniūnas, Urbonas 2008. The real behaviour of the framework allows designing safer structures and reaching the economic benefit (Daniūnas, Urbonas 2013;Ćirović et al 2014;Fayyadh, Razak 2014;Kala 2015;Misiūnaitė et al 2012;Talaslioglu 2015).…”