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. This paper reports a video-assisted approach for structural health monitoring of highway bridges, with results from field tests and subsequent offline parameter identification. The field tests were performed on a short-span instrumented bridge. Videos of vehicles passing by were captured, 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 loads 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 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. Our model reveals that traffic excitation on bridges is spatially correlated, an important feature that is usually incorrectly ignored in most output-only methods. A recursive Bayesian filtering is formulated to monitor the evolution of the state of the bridge. The effectiveness and viability of this video-assisted approach are demonstrated by the field results.
Structural condition assessment of highway bridges has long been relying on visual inspection, which, however, involves subjective judgment of the inspector and detects only local flaws. Local flaws might not affect the global performance of the bridge. By instrumenting bridges with accelerometers and other sensors, one is able to monitor ambient or forced vibration of the bridge and assess its global structural condition. Ambient vibration measurement outwits forced vibration measurement in that it requires no special test arrangement, such as traffic control or a heavy shaker. As a result, it can be continuously executed while the bridge is under its normal serving condition. For short-to mid-span highway bridges, ambient vibration is predominantly due to traffic excitation, inducing the bridge to vibrate mainly in vertical direction. Based on its physical nature, traffic excitation is modeled as moving loads from the passing vehicles whose arrivals and speeds are extracted from digital video. Traffic-induced vibration provides valuable information for assessing the health of super-structure, but is less sensitive to possible seismic damage in the sub-structure. During earthquakes, bridges are excited in all directions by short-duration un-stationary ground motion, and are expected to better reveal their sub-structure integrity. Therefore, traffic-induced and ground-motion-induced ambient vibration data are treated separately in this paper for different assessment objectives, because of the different characteristics and measurability of the excitation. By continuously monitoring the ambient vibration of the instrumented bridge, its global structural conditions of both super- and sub-structures can be evaluated with possible damage locations identified, which will aid local non-destructive evaluation or visual inspection to further localize and access the damage.
For long-term bridge health monitoring, structural dynamic properties are usually obtained by system identification based only on the system output (bridge vibration responses), because the system input (traffic excitation) is difficult to measure. To facilitate such identification the excitation is commonly assumed as spatially uncorrelated white noise. However, when physically modeling it as a stationary stream of moving forces traversing the bridge, whose arrivals at the bridge are in accordance with a Poisson process, the traffic excitation is found to be spatially correlated. In this paper a procedure for formulating the traffic excitation model based on its physics is proposed, which involves first converting the moving forces into equivalent nodal excitation time-histories by the dynamic nodal loading approach, and then applying the Campbell’s theorem for the filtered Poisson processes. By this procedure, a non-diagonal frequency-variant excitation spectrum density matrix (SDM) is obtained. This does not conform to the conventional white noise excitation model. One of the output-only identification techniques based on the conventional excitation model, the frequency domain decomposition technique is implemented to demonstrate that direct application of the technique to traffic-induced vibrations can lead to misleading results. The proposed procedure for formulating the traffic excitation SDM provides a way to describe primary knowledge of the traffic excitation in frequency domain even for complicated bridges, which will potentially enable improvement in output-only identification techniques with unknown but spatially correlated excitation.