PSI - Issue 78

Besim Yukselen et al. / Procedia Structural Integrity 78 (2026) 1943–1950

1944

1. Introduction Recent earthquakes have highlighted the risks associated with buildings constructed before the implementation of modern seismic codes, making retrofitting efforts urgently essential. This is vital for both safety and sustainability. Furthermore, research indicates that evaluating downtime is critical for ensuring functionality after an earthquake, which supports broader resilience objectives. Contemporary seismic regulations require that retrofitting designs satisfy multiple performance criteria, thereby limiting design options. Decision-makers may strive to balance intervention costs, environmental impact, construction timelines, labour requirements, aesthetic factors, and long term economic repercussions, including anticipated annual losses and downtime in the retrofitting design process (Couto et al. 2024a; 2024b). Interest in optimisation techniques for retrofitting has grown over the past decade due to improvements in computational resources, and different studies have employed optimisation algorithms for seismic retrofitting design. For example, Papavasileiou and Charmpis (2016) minimised material costs in multi-storey steel-concrete buildings using evolution strategies, while Akin and Saka (2015) optimised RC frame design with harmony search. Seo et al. (2018) used ant colony optimisation for retrofitting column placement in schools. Mahdavi et al. (2019) applied genetic algorithms and particle swarm optimisation for material reduction, and Falcone et al. (2019) focused on cost optimisation in RC buildings. Other studies explored the Active-Set algorithm by Braga et al. (2019) for bracing systems, cost minimisation using genetic algorithm (GA) by Di Trapani et al. (2022), GA-based optimisation for buckling-restrained braces by Minafò and Camarda (2022), and the use of Non-dominated sorting genetic algorithm III (NSGA-III) by Park et al. (2024) for FRP retrofitting in masonry-infilled RC frames. Most recently, Yukselen et al. (2025a) evaluated genetic algorithm (GA) and particle swarm optimisation (PSO) for optimal RC jacketing schemes for a masonry-infilled RC building from the late 1960s (pre-seismic design code). To address large search spaces and code compliance, the Stochastic Iterative Retrofitting Algorithm (SIRA) was introduced to enhance initial population generation using engineering judgment. This improves feasibility, flexibility, and generalisability while reducing computational effort. This study adopts SIRA with Non-dominated sorting genetic algorithm II (NSGA-II) to optimise the seismic retrofitting scheme of a residential RC building with masonry infills. The optimisation addresses intervention cost, environmental impact, expected annual loss, and downtime, subject to Eurocode 8 (CEN 2005) seismic design compliance. 2. Methodology This study introduces a multi-objective seismic retrofitting design framework that aims to minimise four critical objectives: total intervention cost, environmental impact of the intervention installation, expected annual loss (EAL), and downtime following a seismic event. While all objectives are formulated for minimisation, the optimisation is subject to the constraint of compliance with Eurocode 8 performance criteria. To identify optimal solutions, the NSGA-II (2002) is employed within this constrained multi-objective environment. A key factor in the optimisation process is the computational burden, which necessitates careful selection of design variables and an effective strategy for generating the initial population. To address this, the recently proposed stochastic iterative retrofitting algorithm (SIRA) by Yukselen et al. (2025a) is adopted to generate an initial population set. The effectiveness of the proposed methodology is demonstrated through a case study reinforced concrete (RC) building with unreinforced masonry (URM) infills, representative of Portuguese buildings constructed in the 1960s. For what concerns the retrofitting technique, RC jacketing is adopted. 2.1. Problem formulation and objective evaluation The retrofitting task is formulated as a multi-objective optimisation problem, aiming to identify optimal solutions that minimise four competing objectives — intervention cost, environmental impact, expected annual loss (EAL), and downtime, under seismic design code performance constraints. The problem is expressed through the equation (1): Minimise: ( ̅)=[ 1 ( ̅), 2 ( ̅), 3 ( ̅), 4 ( ̅)], Subject to: ( ̅) = 0, ̅ ∈ Ω (1)