THE VERIFICATION FOR AI EXTRACTING THE DATA OVER BRIDGE FROM VEHICLE VIBRATION DATA BASED ON GPS DEVICE CORRELATION DISTANCE AND THE FEASIBILITY STUDY OF THE IMPROVEMENT OF PREDICTION BY REANNOTATION BASED ON SIGNAL PROCESSING.

Abstract

Construction of bridge digital twins using sensors is considered to be expensive for small and mediumspan bridges. This research focuses on drive-by inspection and bridge screening that estimate bridge vibration from vehicle vibration without installing expensive sensors on the bridge. Vehicle vibrations on bridges need to be extracted from continuous data, In this research, the data is extracted from the relative distance between the bridge edge and GPS devices installed on the vehicle, and it is verified that AI leraned them can correcte the position estimation error. In this experiment, the measurement on 4 bridges (3 PC bridges, 1 steel bridge) are carried out and the prediction results of learned AI is validated. For models that were difficult to learn, the improvement of the accuracy rate by post-processing using signal proccesing technology are comfirmed. Therefore, the possibility of constructing and updating bridge digital twins by data accumulation and re-learning was verified using actual data.