Integrating heat and seismic risk
A multi-objective decision-making approach for facade retrofit design
Kyujin Kim (TU Delft - Building Design & Technology)
Simone D’Amore (Sapienza University of Rome)
Alessandra Luna-Navarro (TU Delft - Building Design & Technology)
Thaleia Konstantinou (TU Delft - Building Design & Technology)
Mauro Overend (TU Delft - Architectural Engineering +Technology)
Stefano Pampanin (Sapienza University of Rome)
Simona Bianchi (TU Delft - Structures & Materials)
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Abstract
Current multi-hazard risk approaches in seismic engineering primarily focus on structural performance under hazards such as earthquakes, floods, and wind. Despite the distinct risk due to their direct impact on human health, heatwaves receive limited consideration. This unbalanced and fragmented approach is particularly noticeable in facade retrofit design, which has a significant influence on both structural vulnerability during earthquakes and indoor thermal conditions during heatwaves. In this case, integrating seismic and heat risk considerations would help balance performance trade-offs across both domains and assist designers in the selection and combination of technologies that are effective under seismic and heatwave conditions. This study therefore proposes a simulation-based multi-objective methodology for facade retrofit decision making. The suggested approach is demonstrated through a case study: a reinforced concrete building retrofitted using a timber rocking-dissipative external exoskeleton and precast concrete sandwich facade panels. Key facade design parameters-component capacity and dimensioning-were varied to generate a multivariate response for both seismic and thermal performance. The simulation results revealed two challenges for optimization: a limited sample size and nonlinear relationships between design inputs and performance outcomes. To address both, a multivariate regression was applied within segmented performance ranges, defined by breakpoints where the relationship between parameters and performance shifted. The resulting segmented multivariate model enabled the identification of optimal technology combinations within specific performance ranges and the generation of multiple Pareto fronts. This broadened the viable solution space and better supported project-specific trade-off decisions.