Multi-objective optimization of seismic upgrading interventions in existing RC buildings

Existing reinforced concrete (RC) structures designed before the implementation of modern seismic codes often exhibit inadequate seismic performances and high vulnerability to earthquake-induced damage. Seismic retrofitting is therefore crucial for addressing construction deficiencies in non-conforming RC buildings, ensuring structural safety, and mitigating seismic risk. However, this task is not straightforward, owing to two key challenges. First, identifying the critical structural members that require retrofitting, along with determining the appropriate type and extent of retrofit, has a major impact on the overall effectiveness of the intervention. Second, conventional retrofitting design approaches often rely on subjective judgments only, typically resulting in non-optimal solutions that prioritize compliance with seismic safety standards while overlooking economic and environmental aspects. To overcome these challenges and support more informed decisions, this study introduces a novel computational framework for optimizing seismic retrofit interventions in RC buildings taking into account a wide range of potential strengthening techniques. It employs a multi-objective genetic algorithm to effectively manage conflicting design criteria, including cost minimization, safety improvement, and reduction of expected losses, with consideration of payback time and embodied carbon at the end of the optimization procedure to guide the selection of the final retrofitting scheme. Application to a case study highlights the capability of this approach to determine the optimal typology, location, and size of each retrofitting intervention by exploring common strengthening techniques, such as fiber-reinforced polymer wrapping, steel jacketing, steel braces, or combinations thereof. The seismic performance of the optimized retrofitted structure is finally evaluated via nonlinear dynamic analyses.