A Unified Optimal Design Approach for Geometrically Nonlinear Skeletal Dome Structures

In this study, a unified optimal design approach is proposed for the design of skeletal dome structure (SDS). Thus, this study has three objectivities, i) presenting the emergence of proposed design integrity, ii) applying the proposed optimal design approach for the design optimization geometrically nonlinear SDS with both ellipse and sphere-shaped forms considering both the shape, size and topology-related design variables, iii) determining the dominant design criteria in the design of SDS. In this framework, the design of SDS is optimized thereby minimizing its entire weight and joint displacements and maximizing its member forces at the same time. The design constraints are borrowed from the provisions of American Petroleum Institute (API RP2A-LRFD) specification. A multi-objective optimization algorithm (MOA) named Pareto Archived Genetic Algorithm (PAGA), as an optimization tool is integrated by an automatic dome generating tool. Therefore, the novelty of this study comes from being the first attempt to obtain the optimal design in a way of integrating both member and joint-related design constraints by the geometrically nonlinear structural analysis. Consequently, it is displayed that that the proposed optimal design approach facilitates to determine an appropriate optimal design through a tradeoff analysis for designers depending on their preferences. The design concepts concerned with buckling, axial stress, combination of axial & bending, and yielding have the higher dominant effects in the optimal design of SDS. Furthermore, it is also demonstrated that the inclusion of diagonal members into the design of SDS provides a reduction in the violation of dominant design constraints

Optimization of geometrically nonlinear lattice girders. Part I: considering member strengths

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

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature

Multiobjective Design Optimization of Grillage Systems according to LRFD-AISC

Both the entire weight and joint displacements of grid structures are minimized at the same time in this study. Four multiobjective optimization algorithms, NSGAII, SPEAII, PESAII, and AbYSS are employed to perform computational procedures related to optimization processes. The design constraints related to serviceability and ultimate strength of grid structure are implemented from Load and Resistance Factor Design-American Institute of Steel Constructions (LRFD-AISC Ver.13). Hence, while the computational performances of these four optimization algorithms are compared using different combinations of optimizer-related parameters, the various strengths of grid members are also evaluated. For this purpose, multiobjective optimization algorithms (MOAs) employed are applied to the design optimization of three application examples and achieved to generate various optimal designations using different combinations of optimizer-related parameters. According to assessment of these optimal designations considering various quality indicators, IGD, HV, and spread, AbYSSS shows a better performance comparatively to the other three proposed MOAs, NSGAII, SPEAII, and PESAII