Reliability-Based Design Optimization of Steel Frames Using Genetic Algorithms

In the design of structures, there are uncertainties of different origin often associated with the properties of materials, geometry and applied loads. With the Reliability-Based Design Optimization (RBDO) method, it is possible to consider design constraints in terms of failure probabilities or target reliability indices, for a structure subject to performance constraints as limit state functions (LSF), in a classical optimization problem. In this way, RBDO analysis takes design variables uncertainties and its effects directly. This work intents to present a RBDO application in a steel frame, with an usual double-loop approach, considering the first and second order structural analysis, with optimization by Genetic Algorithms (GA). Target reliability indices are defined and assessed by FORM (First Order Reliability Method), while GA searches the optimal solution between 18 W-shapes from AISC database (2017), which represents the mininum material mass required for satisfy the constraints. In some cases, it is shown that considering second-order effects can result in lighter frames, as the calculated reliability index can get higher.