Research on Denoising of Bridge Dynamic Load Signal Based on Hippopotamus Optimization Algorithm–Variational Mode Decomposition–Singular Spectrum Analysis Method

Bridge dynamic load test signals are readily contaminated by environmental noise. This reduces the accuracy of bridge structure state assessment. To address this issue, this research proposes a denoising method that combines the hippopotamus optimization algorithm (HOA), variational mode decomposition (VMD), and singular spectrum analysis (SSA). The methodology follows three key phases: First, the HOA optimizes the critical parameters of VMD. Then, the optimized VMD decomposes raw signals into several intrinsic mode components (IMFs). The IMFs below the threshold are removed by calculating the correlation coefficient between each IMF and the original signal. Finally, SSA is introduced for secondary denoising, which helps reorganize bridge signals and eliminate local low-frequency oscillations. The simulation results show that compared with other methods, the root mean square error (RMSE), signal-to-noise ratio (SNR), mean square error (MSE), and mean absolute error (MAE) of the denoised signals achieve on average 16.22% reduction, 2.51% improvement, 62.02% diminution, and 43.74% decrease, respectively, across varying noise levels. Practical validation reveals superior performance metrics: a mean 12.81% lower normalization Shannon entropy ratio (NSER) and a mean 8.44% higher noise suppression ratio (NSR) compared to other techniques. This comprehensive approach effectively addresses noise components in bridge dynamic load test signals.