Establishment of Full-Field, Full-Order Dynamic Model of Cable Vibration by Video Motion Manipulations

Abstract

In-service cables such as stay cables and suspenders of cable-stayed bridges and suspension bridges, are subjected to dynamic loads (e.g., the vehicle loads and wind excitation). Performing vibration measurements and subsequently identifying the dynamic properties and establishing a dynamic model of cable vibration are essential for their dynamic analysis, condition assessment, and performance prediction. For example, based on the taut-string theory, the cable tension, as a critical indicator of cable performance and health state, can be computed using its frequency that can be identified from the measured cable vibration responses. Traditional contact-type wired or wireless sensors, such as accelerometers and strain gauge sensors, require physically attaching to the structure for vibration measurements, which could induce the mass effect. In addition, installing these sensors on structures is costly, time-consuming, and allows instrumentations at a limited number of places. On the other hand, digital video cameras have emerged as a cost effective and agile non-contact vibration measurement method, offering high-resolution, simultaneous, measurements. Recently, digital video camera measurements processed by advanced computer vision and machine learning algorithms have been successfully used for experimental and operational full-field vibration measurement and modal analysis. This study develops a video measurement and processing based technique that can autonomously and blindly extract the full-field dynamic parameters of cable vibration from the video measurements. In addition, by exploiting the taut string theory, full-order (as many modes as possible) dynamic parameters are also extracted. Therefore, a full-field, full-order dynamic (modal) model of cable vibration is established. Laboratory experiments are conducted to validate the developed approach.