Reliability-Based Design Optimization of Structures Combining Genetic Algorithms and Finite Element Reliability Analysis

Abstract

Structural optimization has undergone a substantial progress. Most of these research efforts deal with deterministic parameters. However, in a realistic structural design, it is necessary to consider inherent uncertainties in geometric variables and material properties to ensure safety and quality. Then, design constraints are formulated in terms such as probability of failure or reliability index. The process of design optimization enhanced by the addition of reliability constraints is referred as Reliability-Based Design Optimization (RBDO). Most of RBDO methods use classical mathematical optimization algorithms and require computing gradients of objective function and constraints. This task sometimes can be cumbersome and very hard because reliability constraints are implicit functions of design variables. However, the increased power of computers has made possible to apply heuristic methods, especially Genetic Algorithms in RBDO problems. In this paper Genetic Algorithms have been combined with Nonlinear Finite Element Reliability Analysis software, named OpenSees, to solve RBDO problems. A numerical example shows the performance of the implementation.