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<p>Accurate identification of spillway radial gate (SRG) modal parameters is essential for understanding their dynamics. Given their massive structure, artificial excitation is impractical, making natural excitation the primary input. Effective online monitoring also demands adaptive algorithms to efficiently process short-term data. This study proposes a two-stage adaptive modal identification method combining NExT-IERA and IDBSCAN clustering. First, NExT-IERA extracts modal parameters under natural excitation, enabling modal order determination and stability diagram construction. Then, DBSCAN with optimized GA-HIMDSPSO filters out spurious modes. A four-degree-of-freedom (4-DOF) system validates the method, with a deviation in natural frequencies below 0.03 %. Application to an actual SRG in Southwest China shows a deviation in natural frequencies under 5 % from numerical simulation. Results confirm the method’s accuracy in identifying modal parameters under stochastic excitation while effectively eliminating spurious modes. Compared to conventional approaches, it offers superior accuracy, adaptability, and efficiency for real-time structural health monitoring.</p>

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A study on a two-stage adaptive modal identification method for spillway radial gate

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Chen Wang,
  • Yakun Liu,
  • Di Zhang,
  • Ze Cao

摘要

Accurate identification of spillway radial gate (SRG) modal parameters is essential for understanding their dynamics. Given their massive structure, artificial excitation is impractical, making natural excitation the primary input. Effective online monitoring also demands adaptive algorithms to efficiently process short-term data. This study proposes a two-stage adaptive modal identification method combining NExT-IERA and IDBSCAN clustering. First, NExT-IERA extracts modal parameters under natural excitation, enabling modal order determination and stability diagram construction. Then, DBSCAN with optimized GA-HIMDSPSO filters out spurious modes. A four-degree-of-freedom (4-DOF) system validates the method, with a deviation in natural frequencies below 0.03 %. Application to an actual SRG in Southwest China shows a deviation in natural frequencies under 5 % from numerical simulation. Results confirm the method’s accuracy in identifying modal parameters under stochastic excitation while effectively eliminating spurious modes. Compared to conventional approaches, it offers superior accuracy, adaptability, and efficiency for real-time structural health monitoring.