A Unified Optimal Design Approach for Geometrically Nonlinear Skeletal Dome Structures

Abstract

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.

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

“…It is mentioned that the nature-inspired multi-objective algorithms are mostly preferred in the design optimization of structural engineering applications due to their both higher search capabilities and simple search mechanisms. Particularly, the genetic algorithms (GAs), a branch of evolutionary algorithms has been accomplished to take more attention in the various engineering areas, particularly structural engineering applications [29][30][31][32][33][34][35][36]. Therefore, their fundamentals have been frequently utilized to arrange the basic elements of multiobjective optimization approaches.…”

A Configuration Optimization Method of Integrating Concentrating & Migration Genetic Algorithm for Cellular Reconfigurable Spacecrafts

“…Overall, the problem of CRS configuration design can be modeled as a constrained multi-objective optimization problem. There are still many methods for utilizing an optimization approach with such multiple objectives, which are widely used for many similar engineering problems [17]- [20]. The optimization problem can be described as follows:…”