Abstract
Bridges are critical to the functioning of any transport network and the failure of a bridge can have devastating effects, not only in relation to fatalities or damage associated directly with the collapse, but also to the functioning of the transport network in the aftermath. Traditional methods for inspection and monitoring of bridges are labor-intensive and time consuming and cannot feasibly be applied to monitor all of the bridges on a large transport network. This paper proposes an approach which utilizes vibration measurements from a vehicle driving across a bridge, to monitor changes in its condition over time. In-vehicle vibrations have successfully been used in the past to identify the dynamic properties of the bridge; however, the vehicle speed and the pavement conditions have a big influence on the measured response and can often mask any changes which might occur due to damage in the bridge. This paper demonstrates an approach which combines these in-vehicle vibration measurements with an Artificial Neural Network to identify bridge damage for varying vehicle speeds. The algorithm is shown to be successful in identifying cracking in the deck and changes in the boundary conditions due to seized bearings. The impact of pre-existing damage on the ability of the algorithm to detect further deterioration in bridge condition is also examined. The results show that the sensitivity to further increases in the existing damage is slightly reduced, however the detection of other types of damage is not negatively impacted. The results of the simulations performed in this paper provide a good indication that the application of machine learning to drive-by bridge monitoring represents a promising step towards making large-scale monitoring of bridges feasible.
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Corbally, R., Malekjafarian, A. (2023). Drive-By Detection of Midspan Cracking and Changing Boundary Conditions in Bridges. In: Wu, Z., Nagayama, T., Dang, J., Astroza, R. (eds) Experimental Vibration Analysis for Civil Engineering Structures. Lecture Notes in Civil Engineering, vol 224. Springer, Cham. https://doi.org/10.1007/978-3-030-93236-7_50
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DOI: https://doi.org/10.1007/978-3-030-93236-7_50
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