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A Computationally Efficient Framework for Inelastic Multi-objective Performance-Based Seismic Design Using Surrogate Linear Dynamic Models

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-19页

作者： Mokarram, Vahid Banan, Mohammad Reza Department of Mechanical Engineering Faculty of Engineering Jahrom University Fars Jahrom 74131-88941 Iran Department of Civil and Environmental Engineering School of Engineering Shiraz University Fars Shiraz 71348-51156 Iran

This study presents a robust framework for automatic multi-objective performance-based seismic design (PBSD) of buildings, balancing computational efficiency and optimal design outcomes. PBSD aims to elevate structural resilience against seismic events by judiciously balancing safety and economic considerations. The overarching objective of PBSD is ideally addressed within a multi-objective optimization (MOO) framework, utilizing inelastic time-history analysis as the evaluation tool. Although the computational demands of such an approach are significant, prior research indicates that relying on approximate pushover analyses in the MOO process can lead to unreliable solutions. This paper introduces the extended fast converging multi-objective particle swarm optimization (FC-MOPSO) algorithm, which offers four alternative MOO procedures—namely, the Direct Nonlinear Dynamic Procedure (NDP), the Direct Linear Dynamic Procedure (LDP), the Surrogate LDP, and the Surrogate Nonlinear Static Procedure (NSP) models—to achieve reliable inelastic time-history-based optimal Pareto fronts within the PBSD framework. The effectiveness of the proposed models is demonstrated through the MOO design of three steel moment-resisting frames (MRFs) with setbacks. The results showcase substantial reductions in computational costs for the Direct and Surrogate LDP models compared to the Direct NDP model, thereby making them practical for engineering practitioners. Furthermore, the findings reveal that when the optimal design is selected through a Pareto front based on the two concurrent objectives—minimizing both the initial cost and the life-cycle cost—the Direct and Surrogate LDP models outperform the Surrogate NSP model. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： costly optimization problems Inelastic time-history analysis Irregular buildings Multi-objective structural optimization Performance-based seismic design

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