Reliability Genetic Algorithms Optimization RGAO Approach Based FORM and Monte Carlo Simulation: Application for Bridge Structures

The Darwinian Theory serves as the authentic inspiration behind several mathematical techniques known as metaheuristic methods and evolutionary algorithms, which emulate aspects of biological species' development. Among these methods, Genetic Algorithms have emerged at the forefront and have become the most practical technique. Previous studies have highlighted their capabilities in solving complex problems and their wide utilization across numerous disciplines, particularly optimization. This paper focuses on implementing Genetic Algorithms for structural optimization within the context of reliability assessment and safety. The proposed methodology, termed Reliability Genetic Algorithms Optimization (RGAO), combines Genetic Algorithms with reliability constraints. These constraints are defined by the probability of violating safety criteria within the structural problem's limit state function. To convey the idea, the proposed approach is applied to two case studies: The first case involves the reliability optimization of a bridge pier subjected to seismic loading, utilizing response spectrum analysis. Formulating the reliability optimization problem involves considering minimal design requirements, buckling conditions, and allowable stress constraints to determine the optimal and reliable dimensions for the studied structural component. The second case involves the optimization of the height of a prestressed bridge deck slab, while ensuring reliability by considering prestress geometrical conditions according to the French standard BPEL 91 revised 99. This work contributes to enhancing the use of bio-inspired techniques in structural optimization and reliability analysis, offering valuable perspectives for researchers and engineers.