Structural modal parameter identification based on BeiDou satellite signals and an enhanced stochastic subspace method

Abstract

The BeiDou System Navigation Satellite (BDS) has been extensively applied in navigation and disaster monitoring. BDS is primarily utilized for displacement and deformation monitoring in bridge structures. To further explore the modal feature information of bridge structures embedded in the BeiDou monitoring data, an innovative stochastic subspace identification (SSI) method is proposed to identify the modal parameters using the BDS signals. Firstly, the wavelet transform technique is employed to process the fine frequency bands of BDS signals. Then, the wavelet threshold denoising technique eliminates the noise interference in each frequency band. Secondly, the data-driven SSI-data-driven is employed to identify the structural modal parameters from the processed or purified BDS signals. Subsequently, numerical simulations with noise interference thoroughly verify the feasibility and effectiveness of the proposed method. Finally, the results of modal frequency identification based on BDS and acceleration signals are compared and analyzed using Shenzhen North Bridge (CFST arch bridge) as a practical application. The results indicate that the joint application of wavelet transform and threshold denoising technique can effectively remove the noise components in BDS displacement signals. The proposed SSI-data-driven for BDS signals can accurately identify the multi-order modal frequencies and modal shapes of Shenzhen North Bridge. Compared with the SSI-data-driven based on acceleration data, the results show high consistency, with the error controlled within 7%, and the Modal Assurance Criterion (MAC) value of the modal shapes exceeds 0.65, which shows good consistency.