A genetic algorithm-based framework for seismic retrofitting cost and expected annual loss optimization of non-conforming reinforced concrete frame structures

The paper presents a novel framework, for the optimization of seismic retrofitting design of existing reinforced concrete (RC) frame structures. The framework is oriented to the to minimization of retrofitting-related costs, simultaneously controlling the associated expected annual loss (EAL). The proposed procedure makes use of the capabilities offered by artificial intelligence (AI) techniques, adopting a genetic algorithm (GA) based optimization routine, handling constrains with a non-penalty approach through the definition of innovative parent and survival selection operators. The framework implements multiple retrofitting techniques optimization for the same structure, so that both serviceability and ultimate limit states are simultaneously controlled. In the paper, the optimization procedure is applied to a case study structure, considering carbon fiber-reinforced polymers (CFRP) wrapping of columns and steel braces bracing as potential retrofitting interventions. For both, the framework provides the optimal position (topological optimization) and design (sizing optimization). Results show that retrofitting costs and EAL are effectively controlled by the proposed GA-based optimization approach.