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Sensors 2018, 18(8), 2713; https://doi.org/10.3390/s18082713

Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data

1
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USA
3
Fujian Provincial Expressway Technology Consulting Co., Ltd. Fuzhou 350002, China
*
Authors to whom correspondence should be addressed.
Received: 10 July 2018 / Revised: 15 August 2018 / Accepted: 16 August 2018 / Published: 17 August 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Grooving is widely used to improve airport runway pavement skid resistance during wet weather. However, runway grooves deteriorate over time due to the combined effects of traffic loading, climate, and weather, which brings about a potential safety risk at the time of the aircraft takeoff and landing. Accordingly, periodic measurement and evaluation of groove performance are critical for runways to maintain adequate skid resistance. Nevertheless, such evaluation is difficult to implement due to the lack of sufficient technologies to identify shallow or worn grooves and slab joints. This paper proposes a new strategy to automatically identify airport runway grooves and slab joints using high resolution laser profiling data. First, K-means clustering based filter and moving window traversal algorithm are developed to locate the deepest point of the potential dips (including noises, true grooves, and slab joints). Subsequently the improved moving average filter and traversal algorithms are used to determine the left and right endpoint positions of each identified dip. Finally, the modified heuristic method is used to separate out slab joints from the identified dips, and then the polynomial support vector machine is introduced to distinguish out noises from the candidate grooves (including noises and true grooves), so that PCC slab-based runway safety evaluation can be performed. The performance of the proposed strategy is compared with that of the other two methods, and findings indicate that the new method is more powerful in runway groove and joint identification, with the F-measure score of 0.98. This study would be beneficial in airport runway groove safety evaluation and the subsequent maintenance and rehabilitation of airport runway. View Full-Text
Keywords: airport runway; K-means clustering; groove dimension; Naïve Bayes Classifier; Support Vector Machine; point laser; profiling data airport runway; K-means clustering; groove dimension; Naïve Bayes Classifier; Support Vector Machine; point laser; profiling data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Li, L.; Luo, W.; Wang, K.C.P.; Liu, G.; Zhang, C. Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data. Sensors 2018, 18, 2713.

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