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<p>Multiple tuned mass dampers (MTMDs) reduce dynamic response with multiple specified frequencies of building structures. Many optimization algorithms for placement design exist, though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity. This study proposes an inverse element exchange method (IEEM) with multi-level programming and compares it to a single tuned mass damper (STMD) and uniform distribution of multiple tuned mass dampers in the frequency and time domains. A ten-story shear building is used for the numerical case study. The results show that the proposed method can offer improvement over the STMD, uniform distribution of multiple tuned mass dampers, and distribution optimized by genetic algorithms (GA) with regard to minimizing the interstory drift ratio (<i>IDR</i>) in both the frequency and time domains and the time consumption for optimization.</p>

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Optimizing distribution of multiple tuned mass dampers for building structures with inverse element exchange method

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Peng-Tai Chan

摘要

Multiple tuned mass dampers (MTMDs) reduce dynamic response with multiple specified frequencies of building structures. Many optimization algorithms for placement design exist, though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity. This study proposes an inverse element exchange method (IEEM) with multi-level programming and compares it to a single tuned mass damper (STMD) and uniform distribution of multiple tuned mass dampers in the frequency and time domains. A ten-story shear building is used for the numerical case study. The results show that the proposed method can offer improvement over the STMD, uniform distribution of multiple tuned mass dampers, and distribution optimized by genetic algorithms (GA) with regard to minimizing the interstory drift ratio (IDR) in both the frequency and time domains and the time consumption for optimization.