A genetic algorithm-based framework for seismic retrofitting cost and expected annual loss optimization of non-conforming reinforced concrete frame structures. (15th October 2022) - Di Trapani, Fabio; Sberna, Antonio Pio; Marano, Giuseppe Carlo
Highlights: An novel AI-based framework for the seismic retrofitting cost optimization of RC buildings is proposed. The framework also controls service life cost through the evaluation of the expected annual loss (EAL) The method can provide multiple topological and sizing optimization of the reinforcement. The optimization process is based on a genetic algorithm handling a fiber-section model realized in OpenSees. The application of the method can effectively reduce retrofitting costs maintaining a specified EAL. The framework can be implemented as a tool for cost-effective and sustainable design of retrofitting. Abstract: 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.