Enhancing Multi-Objective Seismic Retrofitting Design Through Optimisation

Most residential buildings in European seismic-prone regions were constructed prior to modern seismic design standards. Retrofitting these buildings to address structural vulnerabilities is crucial for meeting societal needs and enhancing community resilience. Recent earthquakes have underscored the need to consider not only structural safety but also economic losses and downtime resulting from seismic events in retrofitting design. Furthermore, with the increasing emphasis on sustainability, the environmental impact of retrofitting becomes an ever more crucial aspect in decision-making, alongside installation costs. In contrast to existing studies that consider a limited set of retrofitting options, this study employs an optimisation algorithm to explore a broader range of solutions. A Non-dominated Sorting Genetic Algorithm (NSGA), incorporating a novel Stochastic Iterative Retrofitting Algorithm (SIRA) for generating initial populations, is employed to explore various retrofitting schemes for reinforced concrete (RC) jacketing. While a RC infilled building was selected as a case study, the methodology was designed to be broadly applicable across various building layouts and retrofitting strategies. The retrofitting solutions were evaluated, targeting multiple objectives based on different variables, including installation cost, environmental impact, expected annual loss, and downtime, while ensuring compliance with baseline code requirements. The results demonstrate the potential of optimisation algorithms in helping practitioners, or decision-makers in general, identify the most effective retrofitting solutions.