Presentation of a new surface drainage assessment method based on image processing

Document Type : Research Article


1 Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.



Improvement of the pavement surface texture characteristics and drainage quality is an important issue in the field of increasing roads safety and reducing the rate of accidents, especially in rainy weather conditions. The assessment of the pavement surface features and their relation with the accident rate is a common research topic, but no extensive research has been carried out on the evaluation of the pavement surface drainage. In this research, a system has been developed to assess the surface drainage of the pavements. For this purpose, hardware is designed which can saturate the surface and capture the drainage process under constant conditions without the effects of environmental factors. The basis of the presented system is based on digital image processing techniques. Using image processing methods, three time-related indexes including Entropy, Energy and pixels proportion have been determined for the assessment of surface drainage quality of the pavements. Providing a proper combination of the indexes, the pavements are classified as appropriate, normal and inappropriate in terms of surface drainage performance using C5.0 data mining algorithm. The validation of the results of the proposed system shows that this system can evaluate the surface drainage situation with 95.7% accuracy. The presented system results can be used in the pavement management systems at project and network levels as a suitable measure for the evaluation of pavement safety in the rainy conditions as well as improving roads safety.


Main Subjects

[1] J. Hall, K.L. Smith, L. Titus-Glover, J.C. Wambold, T.J. Yager, Z. Rado, Guide for pavement friction, Final Report for NCHRP Project, 1 (2009) 43.
[2] S. Li, K. Zhu, S. Noureldin, D. Harris, Identifying friction variations with the standard smooth tire for network pavement inventory friction testing, Transportation research record, 1905(1) (2005) 157-165.
[3] G.R. Dewey, A.C. Robords, B.T. Armour, R.Muethel, Aggregate wear and pavement friction, in:  Transportation Research Board, Annual Meeting CDROM, 17p, 2001.
[4] D.A. Noyce, H.U. Bahia, J.M. Yambo, G. Kim, Incorporating road safety into pavement management: maximizing asphalt pavement surface friction for road safety improvements, Draft Literature Review and State Surveys, Midwest Regional University Transportation Center (UMTRI), Madison, Wisconsin,  (2005).
[5] E. Masad, A. Rezaei, A. Chowdhury, P. Harris, Predicting asphalt mixture skid resistance based on aggregate characteristics, Texas. Dept. of Transportation. Research and Technology Implementation Office, 2008.
[6] P.M. Gandhi, B. Colucci, S.P. Gandhi, Polishing of aggregates and wet-weather accident rates for flexible pavements, Transportation Research Record, (1300) (1991).
[7] C.-G. Wallman, H. Åström, Friction measurement methods and the correlation between road friction and traffic safety: A literature review, Statens väg-och transportforskningsinstitut, 2001.
[8] B. Sengoz, A. Topal, S. Tanyel, Comparison of pavement surface texture determination by sand patch test and 3D laser scanning, Periodica Polytechnica Civil Engineering, 56(1) (2012) 73-78.
[9]A. El Gendy, A. Shalaby, M. Saleh, G.W. Flintsch, Stereo-vision applications to reconstruct the 3D texture of pavement surface, International Journal of Pavement Engineering, 12(03) (2011) 263-273.
[10] E. Masad, A. Rezaei, A. Chowdhury, T.J. Freeman, Field evaluation of asphalt mixture skid resistance and its relationship to aggregate characteristics, Texas Transportation Institute, 2010.
[11]J.N. Meegoda, S. Gao, S. Liu, N.C. Gephart, Pavement texture from high-speed laser for pavement management system, International Journal of Pavement Engineering, 14(8) (2013) 697-705.
[12] H. Zelelew, A. Papagiannakis, E. de León Izeppi, Pavement macro-texture analysis using wavelets, International Journal of Pavement Engineering, 14(8) (2013) 725-735.
[13]   W. Wang, X. Yan, H. Huang, X. Chu, M. Abdel-Aty, Design and verification of a laser based device for pavement macrotexture measurement, Transportation Research Part C: Emerging Technologies, 19(4) (2011) 682-694.
[14]   B. Mataei, H. Zakeri, M. Zahedi, F.M. Nejad, Pavement friction and skid resistance measurement methods: A literature review, Open Journal of Civil Engineering, 6(04) (2016) 537.
[15]   R. Elunai, V. Chandran, P. Mabukwa, Digital image processing techniques for pavement macro-texture analysis, in:  Proceedings of the 24th ARRB Conference: Building on 50 Years of Road and Transport Research:, ARRB Group, 2010, pp. 1-5.
[16]   R. Elunai, V. Chandran, E. Gallagher, Asphalt concrete surfaces macrotexture determination from still images, IEEE transactions on intelligent transportation systems, 12(3) (2011) 857-869.
[17]   A. Cigada, F. Mancosu, S. Manzoni, E. Zappa, Lasertriangulation device for in-line measurement of road texture at medium and high speed, Mechanical Systems and Signal Processing, 24(7) (2010) 2225-2234.
[18]   E.D. de Leon Izeppi, G.W. Flintsch, M. Saleh, K.K. McGhee, Area-based macrotexture measurements: stereo vision approach, 2009.
[19]   S.N. Goodman, Y. Hassan, O. Abd El Halim, Digital Sand Patch Test: Use of Digital Image Analysis for Measurement of Pavement Macrotexture, 2010.
[20]   B. Mataei, H. Zakeri, and F.M. Nejad, Automatic, Evaluation of pavement surface drainage using image processing, 10th bitumen, asphalt and Machinery Conference, (2017) in Persian.
[21]   R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital image processing using MATLAB, Pearson Education India, 2004.