Abstract
Infrastructure spending is a major component of railroad annual budgets and therefore must be prioritized to ensure both safety and line capacity at all times. Current inspection priorities for railroads include early detection of structural deterioration, including concrete and timber, caused by the recent increase of car load capacities. Railroad bridge inspectors conduct tap testing to detect the deterioration of concrete and timber acoustically. However, measuring railroad bridge condition in the field is a challenging task and, in general, is solely based on the inspector’s experience. The results of this research describe the development, validation, and testing of a remote tap testing device that can be deployed by an aerial robot. The new tap testing device can remotely impact the surface, record the sounds of those impacts, and post-process the data to perform replacement prioritization. Tapping acoustics were analyzed both in the time and frequency domains. Principal component analysis of the data enabled the clustering of the different sets of data collected from different concrete and timber surfaces. The results quantify for the first time structural tap testing data collected with a tap testing mechanism built for deployment by an aerial robot. The goal of this work is to enable cost-effective, safer and sustainable upgrade prioritization of railroad bridge inventories.
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Acknowledgements
This research was performed at the Los Alamos National Laboratory Engineering Institute. Support from the following sources is gratefully acknowledged: The University of New Mexico (UNM) structures laboratory of the Civil Engineering Department; The New Mexico Consortium (NMC), who provided financial support for this research; and the Canadian National Railway (CN), who provided inputs for the feasibility of this research for railroad bridge replacement prioritization in the United States.
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Moreu, F., Ayorinde, E., Mason, J. et al. Remote railroad bridge structural tap testing using aerial robots. Int J Intell Robot Appl 2, 67–80 (2018). https://doi.org/10.1007/s41315-017-0041-7
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DOI: https://doi.org/10.1007/s41315-017-0041-7