THE EFFICIENCY EMPLOYMENT OF CLOSE-RANGE PHOTOGRAMMETRY TO MEASURE AND MODEL POTHOLES IN ASPHALT PAVEMENT

  • AHMED RAMZY JAMEE Dept. of Civil Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
  • RAAD AWAD KATTAN Dept. ofSurveying Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
Keywords: Distress, Close range photogrammetry, 3-D Modelling, GCPs, Agisoft PhotoScan.

Abstract

The objective of this study is to measure and model potholes distress in the pavement road surface,
which form a major type among several distresses exist in road networks. In this research close-range
photogrammetry is used to represent potholes distress, this approach allows converting images of the
studied potholes into a 3-D model. Ground control points (GCPs) have to be distributed uniformly to
cover the whole measured area helping to solve analytically for the unknown coordinates of the measured
area point cloud and acquiring a georeferenced model with known coordinates.
Agisoft PhotoScan was used for potholes modelling, which is a user-friendly software that allows for 3-
D modelling, producing digital elevation models, point clouds, and orthomosaic. The Agisoft PhotoScan
initial assessment test on a model similar in shape to a real pothole reviles that the best accuracy acquired
using 8 GCPs distributed regularly to cover the shape of the pothole have a maximum discrepancy of -
2.16cm in Z coordinates of one point. The obtained root mean square errors RMS were ±0.79 ±0.78
±0.67cm in X, Y, and Z coordinates respectively.
In a real pothole modelling tests on two sites and comparing the model check points coordinates with
the ground coordinates, the RMS obtained were, ±0.85, ±0.58, ±0.44 cm and ±0.32, ±0.27, ±0.39cm in X, Y
and Z coordinates respectively. Both potholes can be classified as high level potholes as their depths are
more than 50mm in accordance with known specifications. In volume test carried out on the first model,
the Agisoft PhotoScan generated volume was 8413 cm3
confirmed the volume measured with the
standard soil test known as sand replacement method which gave a volume of 8311 cm3
.

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References

Adlinge, S. S., & Gupta, A. K. (2013). Pavement
deterioration and its causes. International
Journal of Innovative Research and
Development, 2(4), 437–450.
Chai, L.T., 2005. Evaluation of cracks and
disintegration using Close Range Digital
Photogrammetry and Image Processing
Technique (Doctoral dissertation, M. Sc.
Thesis, Department of Civil Engineering,
College of Engineering, University technology
Malaysia).
Clifton Associates Ltd. (2012). Pothole Identification,
Assessment and Repair GuidelinesNo Title.
Communities of Tomorrow Leveraged
Municipal Innovation Fund.
https://suma.org/img/uploads/documents/com
munities_of_tomorrow/Pothole Guidelines.pdf
Jebur, A., Abed, F., & Mohammed, M. (2018).
Assessing the performance of commercial
Agisoft PhotoScan software to deliver reliable
data for accurate3D modelling. MATEC Web
of Conferences, 162, 3022.
Mustaffara, M., Lingb, T. C., & Puanb, O. C. (2008).
Automated pavement imaging program (APIP)
for pavement cracks classification and
quantification-a photogrammetric approach.
The International Archives of the
Photogrammetry, Remote Sensing and Spatial
Information Sciences, 37(B4), 367–372.
Oh, J., 2011. Novel approach to epipolar resampling
of HRSI and satellite stereo imagery-based
georeferencing of aerial images (Doctoral
dissertation, The Ohio State University).
Paige-Green, P., Maharaj, A., & Komba, J. (2010).
Potholes: Technical guide to their causes,
identification and repair. CSIR Built
Environment, Stellenbosch, South Africa.
Perera, R.W., Kohn, S.D. and Tayabji, S.D.,
2005. Achieving a high level of smoothness in
concrete pavements without sacrificing long-
term performance (No. FHWA-HRT-05-068).
United States. Federal Highway Administration. Office of Infrastructure
Research and Development.
quan Li, X., an Chen, Z., ting Zhang, L., & Jia, D.
(2016). Construction and accuracy test of a 3D
model of non-metric camera images using
Agisoft PhotoScan. Procedia Environmental
Sciences, 36, 184–190.
Rutty, G. N. (2008). Essentials of autopsy practice:
new advances, trends and developments.
Springer Science & Business Media.
Sarsam, S. I., Daham, A. M., & Ali, A. M. (2015).
Implementation of Close Range
Photogrammetry to Evaluate Distresses at
Asphalt Pavement Surface. International
Journal of Transportation Engineering and
Traffic System, 1(1), 1–14.
Shahin, M. Y. (2009). Paver distress identification
manual, asphalt surfaced airfields. US Army
Corps of Engineers ERDC-CERL, Champaign,
IL.
Tan, Y., & Li, Y. (2019). UAV Photogrammetry-
Based 3D Road Distress Detection. ISPRS
International Journal of Geo-Information,
8(9), 409.
Tiong, P. L. Y., Mustaffar, M., & Hainin, M. R.
(2012). Road surface assessment of pothole
severity by close range digital
photogrammetry method. World Applied
Sciences Journal, 19(6), 867–873.
Wang, D., Falchetto, A. C., Goeke, M., Wang, W.,
Li, T., & Wistuba, M. P. (2017). Influence of
computation algorithm on the accuracy of rut
depth measurement. Journal of Traffic and
Transportation Engineering (English Edition),
4(2), 156–164.
Wolf, P. R., & Dewitt, B. A. (2000). Elements of
photogrammetry: with applications in GIS
(Vol. 3). McGraw-Hill New York.
Published
2020-12-13
How to Cite
JAMEE, A. R., & KATTAN, R. A. (2020). THE EFFICIENCY EMPLOYMENT OF CLOSE-RANGE PHOTOGRAMMETRY TO MEASURE AND MODEL POTHOLES IN ASPHALT PAVEMENT. Journal of Duhok University, 23(2), 179-197. https://doi.org/10.26682/sjuod.2020.23.2.19
Section
Pure and Engineering Sciences