GPS-Monitoring and Assessment of Mansoura Railway Steel-Bridge Based on Filter and Wavelet Methods

Abstract

This study presents Mansoura steel railway bridge, Egypt, movement analysis in response to passing trains. This bridge was used for two types of traffics which are trains on the middle and one vehicle lane on each side of the bridge. A monitoring system is designed based on the Real Time Kinematic Global Positioning System (RTK-GPS) to monitor and assess the bridge behaviour and movements under the effect of trains' loads. Two methods are used to de-noise the GPS data, which are moving average filter (FM) and Wavelet Transform (WT). In addition; the wavelet decomposition is used to classify the noise effects. The time and frequency domain GPS observation analysis results are discussed and the results showed that the bridge is safe under affected train loads in time and frequency domain. Also, it can be concluded that the WT can be used to filter the GPS monitoring data but can't be used to extract the dynamic frequencies. Otherwise, the multiple filters are more suitable method to extract dynamic frequencies for the low sampling GPS frequency instruments.

Review of Bridge Structural Health Monitoring Based on GNSS: From Displacement Monitoring to Dynamic Characteristic Identification

“…Cao et al [280] applied SSA on the time series of the pylons of Sutong Bridge monitored by GNSS and effectively extracted the vibration frequency of the pylons via wavelet analysis. Elbeltagi et al [281] filtered GNSS monitoring data via moving average filtering and wavelet transform and extracted low-frequency bridge vibration information by using FT. Han et al [282] conducted wavelet and FFT analysis on the data sequence obtained by the GNSS-based monitoring equipment on the Wuhan Yangtze River Second Bridge under typhoon load. The obtained main frequency of vibration of the bridge was 0.172 Hz.…”