Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.
This paper proposes a bridge damage detection method using direct rotation measurements. Initially, numerical analyses are carried out on a 1-D simply supported beam model loaded with a single moving point load to investigate the sensitivity of rotation as a main parameter for damage identification. As a result of this study, the difference in rotation measurements due to a single moving point load obtained for healthy and damaged states is proposed as a damage indicator. A relatively simple laboratory experiment is conducted on a 3 m long simply supported beam structure to validate the results obtained from the numerical analysis. The case of multi-axle vehicles is investigated through numerical analyses of a 1-D bridge model and a theoretical basis for damage detection is presented. Finally, a sophisticated 3-D dynamic Finite Element model of a 20 m long simply supported bridge structure is developed by an independent team of researchers and used to test the robustness of the proposed damage detection methodology in a series of blind tests. Rotations from an extensive range of damage scenarios were provided to the main team who applied their methods without prior knowledge of the extent or location of the damage. Results from the blind test simulations demonstrate that the proposed methodology provides a reasonable indication of the bridge condition for all test scenarios.
In an effort to find more cost‐effective and proactive ways to keep bridges in good condition, the use of instrumented vehicles has gained great interest in the last decade. Two bridge components that can wear rapidly are the bearings and the road surface. However, past research on drive‐by monitoring has placed focus mostly on detecting losses of bending stiffness in the bridge deck, while assuming ideal support conditions that may differ from real cases significantly, and ignoring the characterization of the road profile. Even further, the need for specialized vehicles equipped with high‐tech instrumentation, low speeds, or very good road profiles has been a major obstacle preventing its practical implementation. This paper investigates the use of axle accelerations from an ordinary two‐axle vehicle crossing the bridge to quantify the rotational stiffness of the supports and the height of the road irregularities while overcoming the limitations exposed above. In contrast to previous research where the response of the contact point has been derived from other vehicular locations based on complex differential equations of motion, transfer functions are employed here. The key advantage of transfer functions is their simple algebraic form that can be easily calibrated on the field. The road profile is then obtained by subtracting the displacement of the bridge under each axle from the displacement of the contact point. There is one prediction of the road profile per axle but only a unique value of rotational stiffness at each support that will yield the same prediction by both axles. The algorithm is successfully tested with a half‐car traveling at 5, 10, 15, and 20 m/s, over a 15‐m bridge beam model with ISO road classes “A,” “B,” and “C,” for boundary conditions ranging from simply supported to fixed. The solution's robustness to modeling inaccuracies and noisy data is also investigated.
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