Talkha steel highway bridge monitoring and movement identification using RTK-GPS technique

• RTK-GPS measurements are used to monitor and model the movement of bridge deck.
• Moving average filter increased the accuracy by 15%.
• Multi filter of short-period can reveal the dynamic displacement of the bridge deck movement.
• The movement output of the Neural Network Auto-Regressive model is highly confirmed with the GPS measurements.

Monitoring the bridge deformation is the vital task in bridge maintenance and management. Talkha highway steel bridge is one of the two oldest steel bridges in Mansoura city. Nowadays, the Real Time Kinematic-Global Positioning System (RTK-GPS) is capable of providing fast and accurate measurements of bridge oscillations. Also, the movement and damage severity can be identified using the dynamic bridge characteristics obtained from GPS. The aim of the present work is to demonstrate the use of RTK-GPS (1 Hz) to provide data for use in the assessment of existing structures. The moving average filter is used to de-noising the GPS observations. Finite Impulse Response (FIR) with moving average are used to extract the dynamic response and frequency domain of the bridge and Neural Network Auto-Regressive (NNAR) model is used to identify the bridge movement. The results indicate that: (1) the moving average filter is simple and suitable to smooth high noises and errors of GPS observation signals; (2) the multi-filter of short-period can reveal the dynamic displacement of bridge deck movement; (3) the low-frequency movements of the bridge could not be completed and the observation time should be increased to complete it and (4) the movement output of the NNAR is highly conformed with the observation filter.