Recommended Citation
Cheng, P., Krogmeier, J. V., Bell, M. R., Wang, K., Li, J., & Yang, G. (2021). Detection and classification of concrete patches by integrating GPR and surface imaging (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2021/18). West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284317320
DOI
10.5703/1288284317320
Abstract
This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
Report Number
FHWA/IN/JTRP-2021/18
Keywords
pavement, pavement patching, ground penetrating radar, three-dimensional imaging, patching table
SPR Number
4213
Performing Organization
Joint Transportation Research Program
Sponsoring Organization
Indiana Department of Transportation
Publisher Place
West Lafayette, IN
Date of this Version
2021