Long-Term Health Monitoring Data Processing on Post-Tensioned Concrete Box-Girder Bridge by Wavelet-Based

Abstract

The concrete box-girder bridge is designed to have a long service life of around 100 years. To ensure safety and performance degradation during long service life, a Structural Health Monitoring System (SHMS) has been implemented in the box-girder bridge. SHMS can reliably assess structural response due to real-time applied loads, detect anomaly activities and locate the structural damage in the structure. Several sensors have been implemented in the bridge to continuously record the behavior of the bridge in all environmental conditions. Due to real-time natural conditions, false alarms occur frequently in SHM due to the disruption of noises and lead to misunderstanding of who is evaluating. Nevertheless, numerous SHM data that have been collected make it complicated to determine the anomaly of the structures. Therefore, it required signal processing to maximize the potentialities of the massive SHM data, as well as the efficiency of the time work. In this study, wavelet transformation, a rapid and unsupervised signal processing approach, was used to analyze the huge signal data by removing noise, and separating different signal sources as well. Further, with time-frequency analysis and multi-resolution capabilities, the transformation of wavelet is a promising tool for analyzing long-term SHM data. The suggested approach is shown by using long-term strain data from a 40 m concrete box-girder bridge in 24h. The results showed that after the denoising process, the highest discrepancy between the reconstructed and original strain signal is 2.73 μƐ and lost their energy less than 1%. Hence, the strain gauge sensor was successfully able to eliminate the noise through wavelet technology.