GNSS-RTK data denoising and displacement-based blind modal analysis of a long-span bridge

• Published in: GPS solutions 2025, Vol.29 (1), p.43
• Main Authors: Niu, Yanbo, Xiong, Chunbao, Li, Zhi
• Format: Article
• Language: English
• Subjects: Atmospheric Sciences; Automotive Engineering; Background noise; Cable-stayed bridges; Chebyshev approximation; Cluster analysis; Clustering; Earth and Environmental Science; Earth Sciences; Electrical Engineering; Field tests; Geophysics/Geodesy; Global navigation satellite system; Kinematics; Mathematical models; Modal analysis; Monitoring; Noise sensitivity; Numerical models; Original Article; Parameter estimation; Parameter identification; Parameter robustness; Parameter sensitivity; Real time; Robustness; Signal to noise ratio; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Swarm intelligence; Vector quantization; Vibration analysis
• ISSN: 1080-5370; 1521-1886
• DOI: 10.1007/s10291-024-01774-9

Displacement-based modal analysis has been proven to yield more robust and reliable modal parameter identification results compared to acceleration-based modal analysis. Global navigation satellite systems (GNSS) under real-time kinematic (RTK) mode is a widely used dynamic displacement monitoring technique. Notably, the monitoring accuracy of GNSS is limited due to the existence of multiple error sources such as multipath effect and satellite shielding effect. Particularly, blind source separation (BSS) can determine structural modal parameters from output-only responses. This method is advantageous compared with conventional modal analysis method because it does not require any prior knowledge of the structure. However, common BSS methodologies are susceptible to the local minima problem and are sensitive to low signal-to-noise ratio (SNR) signals. To address the aforementioned problems, this study first presents a combination filter strategy of Chebyshev and wavelet threshold (WT) to estimate the structural dynamic displacement based on GNSS RTK measurement. Then, a swarm-enhanced blind identification approach is proposed to determine structural modal parameters from the estimated displacement. The core of this approach is to develop a robust K-means clustering approach with swarm intelligence optimization to estimate the mixing matrix (i.e., mode shape matrix). Finally, the developed approach is verified in a four-degree-of-freedom numerical model and then implemented to a field test of a long-span cable-stayed bridge in engineering practice. The results illustrate that the designed combination filter can effectively weaken the influence of GNSS-RTK background noise while retaining the components related to structural dynamic vibration. Meanwhile, comparing with the conventional BSS approach (i.e., sparse component analysis), the developed swarm-enhanced blind identification approach exhibits higher robustness and convergence accuracy in determining structural modal parameters.