A Multi-Source Fusion Deformation Monitoring Method for Super High-Rise Buildings Based on WOA-VMD and Adaptive Robust Kalman Filtering

Super high-rise buildings are increasingly equipped with structural monitoring systems to track deformation responses during construction and operation, thereby supporting structural condition assessment and engineering management. To address key monitoring challenges, including GNSS multipath interference, insufficient vertical accuracy, accelerometer integration drift, and high-frequency noise, this study proposes a GNSS/accelerometer fusion monitoring method based on whale optimization algorithm–optimized variational mode decomposition (WOA-VMD) and adaptive robust Kalman filtering (ARKF). Continuous three-hour GNSS and accelerometer observations collected from a super high-rise building under construction are used for fusion validation. The results show that WOA-VMD effectively separates noise from deformation-related signals and outperforms conventional EMD and standard VMD in denoising performance. Compared with the raw observations, the fused east, north, and vertical displacement RMSEs are reduced by 68.84%, 75.97%, and 60.22%, respectively; the SNRs increase to 22.03 dB, 21.38 dB, and 16.74 dB, respectively; the STDs decrease by 72.58%, 75.62%, and 68.39%, respectively; and the PSDs increase to 9.47 dB, 9.02 dB, and 8.31 dB, respectively. The proposed framework exhibits sub-centimeter-level displacement monitoring performance in the horizontal directions and significantly enhances the monitoring capability of the vertical component. The field validation results demonstrate the feasibility and effectiveness of the proposed framework for short-term deformation monitoring of super high-rise buildings under practical monitoring conditions and indicate its potential for structural health monitoring applications.