Multi-Objective Optimization of FRP Jackets for Improving the Seismic Response of Reinforced Concrete Frames

In this study, a multi-objective Genetic Algorithm (GA) optimization procedure is proposed for the seismic retrofitting of Reinforced Concrete (RC) building frames via Fiber-Reinforced Polymer (FRP) jackets. The optimization problem is solved via numerically efficient but accurate Finite-Element (FE) models able to take into account the strengthening and ductility increase contribution for a given FRP jacketing configuration. Based on a reference RC frame case study, an optimization approach aimed to maximize the frame ductility and minimize the FRP volume/cost is proposed, by taking into account different FRP jackets thicknesses for the internal and external columns and well as for each separate frame floor. In doing so, careful consideration is paid also to the expected collapse mechanism for the frame and the approach to embed a further objective able to control the collapse mechanism into the procedure is described. The results show the potential of the approach, which not only provides the entire Pareto Front of the multi-objective optimization problem, but also allows for general considerations about the influence of the design variables on the response of a given RC building.