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Structural condition assessment of highway bridges is traditionally performed by visual inspections or nondestructive
evaluation techniques, which are either slow, unreliable or detects only local flaws. Instrumentation of bridges with
accelerometers and other sensors, however, can provide real-time data useful for monitoring the global structural
conditions of the bridges due to ambient and forced excitations. Traditionally, videos are used for surveillance purposes
and environmental monitoring of civil structures. In this paper the potential for the utilization of videos in an integrated
structural health monitoring of highway bridges beyond the mentioned traditional applications are reported. Results
obtained from the field tests, which were carried out on a short-span instrumented bridge, are presented. Videos of
vehicles passing by, together with signals from laser beam sensors placed on the side of the bridge, were captured, and
synchronized with data recordings from the accelerometers. For short-span highway bridges, vibration is predominantly
due to traffic excitation. A stochastic model of traffic excitation on bridges is developed assuming that vehicles
traversing a bridge (modeled as an elastic beam) form a sequence of Poisson process moving forces and that the contact
force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements.
Basic information of vehicle types, arrival times and speeds are extracted from the video images to develop a physics-based
simulation model of the traffic excitation. This modeling approach aims at circumventing a difficulty in the
system identification of bridge structural parameters. Current practice of system identification of bridge parameters is
often based on the measured response (or system output) only, and knowledge of the input (traffic excitation) is either
unknown or assumed, making it difficult to obtain an accurate assessment of the state of the bridge structures. The
effectiveness and viability of this video-assisted approach are demonstrated by the field results. Finally, a technique on
how to integrate the weights of vehicles in the image processing algorithm is proposed.
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