Bridge Dynamic Displacement Monitoring Using Adaptive Data Fusion of GNSS and Accelerometer Measurements

The monitoring of bridge dynamic displacement under normal operation conditions has been a vital need for the assessment of the serviceability of bridges. Traditionally, it was mostly accomplished by the Global Navigation Satellite System (GNSS). However, the poor measurement accuracy and low sampling rate of the GNSS limit the use of monitored displacement signals. In this paper, we propose a novel adaptive multirate Rauch-Tung-Striebel (RTS) smoother that fuses the measurement signals of GNSS and accelerometers to improve both accuracy and sampling rate. The proposed algorithm distinguishes itself from previous studies by adaptively estimating the unknown time-varying transition and GNSS measurement noise variances using the variational Bayes (VB) technique, making it more accurate and less human-involved. The proposed algorithm was validated on a field test conducted on Chishuihe Hongjun Bridge in China, which has the second longest main span and the second highest main towers among suspension bridges over valleys in the world. The algorithm estimated the dynamic displacement at an accuracy of 2.09 mm, which is 21.4% better than the result of the previous algorithm, with both pseudo-static and several low-order main vibration mode components recovered.