Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN-Adaptive Threshold Wavelet Method

Area of Science:

• Geotechnical Engineering
• Structural Health Monitoring
• Signal Processing

Background:

• BDS bridge displacement monitoring is susceptible to background noise.
• Traditional wavelet filtering methods often use fixed thresholds dependent on data length, limiting adaptability.

Purpose of the Study:

• To propose an advanced data processing method for BDS bridge displacement monitoring.
• To overcome limitations of fixed threshold wavelet filtering in noisy environments.

Main Methods:

• Utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose displacement data into intrinsic mode functions (IMFs).
• Applied adaptive threshold wavelet denoising based on the mean and variance of wavelet coefficients to noisy IMFs.
• Employed correlation coefficients to differentiate between noisy and effective components.

Main Results:

• The proposed CEEMDAN-wavelet method effectively suppressed random and multipath noise in BDS displacement monitoring data.
• Successfully reconstructed the true bridge displacement response, validated through simulation and real-world data from Nanmao Bridge.

Conclusions:

• The integrated CEEMDAN and adaptive wavelet denoising approach offers a robust solution for accurate bridge displacement monitoring.
• This method enhances the reliability of structural health monitoring systems by providing cleaner displacement data.