Genetic algorithm framework for environmentally sustainable and cost-efficient seismic retrofitting design of reinforced concrete structures

Abstract

Seismic retrofitting of reinforced concrete structures is often associated with high costs, invasiveness, and prolonged downtime, while the environmental impact remains largely overlooked. Over the past year, computational intelligence has proven effective in optimizing retrofit costs; however, economically optimal solutions do not necessarily minimize carbon emissions. This study introduces a genetic algorithm-based framework to reduce the embodied carbon of retrofitting materials, using Global Warming Potential as a key metric. Applied to a non-seismically designed RC frame structure, the framework can easily assess different techniques, it has been assessed with concrete jacketing, identifying solutions that optimize sustainability, seismic performance, and economic feasibility