Abstract
AbstractStochastic subspace identification (SSI) stands as one of the most extensively employed algorithms for modal parameter identification within the domain of bridge structural health monitoring. However, when confronted with nonstationary signals, it often generates numerous false modes in the stability graph, consequently impeding the accuracy of modal parameter identification. To address this challenge, an algorithm combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and covariance‐driven SSI (COV‐SSI) has been proposed in this research, referred to as the CEEMDAN‐SSI algorithm. The CEEMDAN‐SSI algorithm first decomposes the structural vibration acceleration into intrinsic mode functions (IMFs) and then selects the pertinent IMF component for signal reconstruction using the Pearson correlation coefficient. Subsequently, the reconstructed signal undergoes analysis using the COV‐SSI algorithm, effectively mitigating the occurrence of false modes. Furthermore, the research focuses on a large‐span continuous rigid frame bridge with elevated piers situated in Toutunhe, Xinjiang Province, currently under construction. Modal parameters of the rigid frame bridge under various wind speed conditions are compared and analyzed using both COV‐SSI and CEEMDAN‐SSI algorithms. The findings reveal that the CEEMDAN‐SSI algorithm markedly diminishes false modes while enhancing the strength of stability axes for each mode, thus affirming the feasibility and robustness of the CEEMDAN‐SSI algorithm.
Published Version
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