Multi-objective optimization of high-rise buildings with outrigger systems subject to seismic loads
Achieving highly accurate global optimization of high-rise buildings to balance safety and economy while addressing uncertainties is critical in structural design. This paper introduces a comprehensive multi-objective optimization method for high-rise buildings with single outrigger systems subjecte...
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| Veröffentlicht in: | Journal of Building Engineering Jg. 111; S. 113197 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.10.2025
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| Schlagworte: | |
| ISSN: | 2352-7102, 2352-7102 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Achieving highly accurate global optimization of high-rise buildings to balance safety and economy while addressing uncertainties is critical in structural design. This paper introduces a comprehensive multi-objective optimization method for high-rise buildings with single outrigger systems subjected to seismic loads, utilizing the NSGA-II algorithm. The optimization addresses two computing objectives: minimizing structural weight and maximizing either structural seismic performance or reliability. Initially, updated probabilistic demand models incorporating additional structural features are developed using a database derived from 3D nonlinear time-history analyses for three distinct single-outrigger system configurations. These models consider input feature uncertainties, enabling predictive fragility assessments. Subsequently, multi-objective global optimization is executed using these enhanced probabilistic demand models in combination with the NSGA-II algorithm. The optimized designs, balancing structural weight and reliability, exhibit improved robustness due to comprehensive incorporation of parameter uncertainties into the fragility assessments.
•A design process for the multi-objective optimization of high-rise buildings.•Improved probabilistic demand models with structural features for high-rise buildings.•An augmented sample-based approach for efficient evaluation of global sensitivity.•An elitism non-dominated sorting GA (NSGA-Ⅱ) for the multi-objective optimization. |
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| ISSN: | 2352-7102 2352-7102 |
| DOI: | 10.1016/j.jobe.2025.113197 |