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<p>This study presents a comprehensive comparative analysis of two stochastic system identification methods—SSI_data and SSI_cov—for extracting flutter derivatives (FDs) of a trussed deck suspension bridge section under turbulent wind conditions. Numerical simulations and wind tunnel experiments were conducted to evaluate each method's robustness to noise, identification accuracy, and computational efficiency. Results show that while both methods reliably estimate natural frequencies, SSI_data achieves greater precision in damping ratio identification and offers a significant computational advantage—reducing runtime compared to SSI_cov. A detailed signal-to-noise ratio (SNR) analysis reveals that SSI_data excels in vertical mode identification, while SSI_cov is more resilient in estimating torsional damping under low SNR conditions. Experimental validation confirms that the critical flutter wind speeds estimated using both methods under turbulent flow conditions closely match the results observed in wind tunnel tests. These findings provide practical guidance for selecting suitable identification strategies and underscore the potential of SSI methods—particularly SSI_data—for real-time aeroelastic analysis of long-span bridges operating in complex, turbulence-prone wind environments.</p>

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Comparative study of data-based and covariance-based methods for flutter derivative identification of a trussed deck suspension bridge model with wind tunnel validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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  • Hoang Trong Lam,
  • Phan Duc Tam,
  • Vo Duy Hung

摘要

This study presents a comprehensive comparative analysis of two stochastic system identification methods—SSI_data and SSI_cov—for extracting flutter derivatives (FDs) of a trussed deck suspension bridge section under turbulent wind conditions. Numerical simulations and wind tunnel experiments were conducted to evaluate each method's robustness to noise, identification accuracy, and computational efficiency. Results show that while both methods reliably estimate natural frequencies, SSI_data achieves greater precision in damping ratio identification and offers a significant computational advantage—reducing runtime compared to SSI_cov. A detailed signal-to-noise ratio (SNR) analysis reveals that SSI_data excels in vertical mode identification, while SSI_cov is more resilient in estimating torsional damping under low SNR conditions. Experimental validation confirms that the critical flutter wind speeds estimated using both methods under turbulent flow conditions closely match the results observed in wind tunnel tests. These findings provide practical guidance for selecting suitable identification strategies and underscore the potential of SSI methods—particularly SSI_data—for real-time aeroelastic analysis of long-span bridges operating in complex, turbulence-prone wind environments.