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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Bibliographic Details
Published in:Journal of Building Engineering Vol. 111; p. 113197
Main Authors: Xing, Lili, Gardoni, Paolo, Yu, Juanya, Zhou, Ying, Zhang, Peng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2025
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ISSN:2352-7102, 2352-7102
Online Access:Get full text
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Summary: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.
ISSN:2352-7102
2352-7102
DOI:10.1016/j.jobe.2025.113197