MATEC Web Conf.
Volume 271, 20192019 Tran-SET Annual Conference
|Number of page(s)||5|
|Published online||09 April 2019|
Automated Road Damage Recognition based on the Sparse Coding Analysis of Vehicle Vibrations
Department of Construction Science, Texas A&M University, College Station, TX 77843
2 Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843
3 Bert S. Turner Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803
* Corresponding author: firstname.lastname@example.org
Road pavement damage inspection is a critical yet challenging task. At present, road pavement damage inspection is usually done by DOTs using a manual process. Another emerging method of inspection is via the use of sensors, such as the use of LiDAR. This study proposes an automated road damage recognition method via the Sparse Coding analysis of vehicle vibrations. Sparse Coding is a class of unsupervised methods that learn data patterns based on extracted overcomplete bases. Unlike frequency domain-based analysis, e.g. Spectral Analysis, Sparse Coding analysis preserves the temporal information of the vehicle vibration that contains important patterns related to road pavement damage. A preliminary study was performed with vehicle vibration data collected in College Station, Texas. Results confirm the feasibility of the proposed method in automated road pavement damage recognition. More data points should be collected in the future to further benchmark the effectiveness of the proposed method.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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