A new method for three-dimensional evaluation of asphalt pavement texture

Document Type : Research Article

Authors

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

2 Dep. of Civil Engineering, Amirkabir University of Technology

3 RESEARCHER /AUT

Abstract

The surface texture depth is an important indicator for evaluating the skid resistance capabilities and providing suitable surface drainage conditions for asphalt pavement, which has a direct effect on the safety and comfort of road users. Texture evaluation is one of the main goals of the pavement management system. Three-dimensional evaluation is the most accurate method of measuring pavement texture characteristics. In this paper, a three-dimensional automatic device based on a hybrid imaging technique is presented. In this device, a system consisting of two-dimensional digital cameras is designed to provide the required image data. The proposed system has a tool for reducing surface friction. This system also makes it possible to evaluate the texture in rainy conditions. Based on the two-dimensional images taken from the pavement surface, the three-dimensional models of the pavement texture are presented. A method for calculating the mean texture depth is presented. Measuring the depth of pavement texture in different directions is a capability of this method, which makes it possible to uniformly evaluate the depth of the texture in different directions and find the critical direction for texture drainage capacity. The proposed system has been used to measure the depth of texture in different sections of pavement and the results have been compared with the results of the standard sand patch method. This comparison shows a high correlation between the results of the proposed method and a standard pavement texture evaluation method.

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[1] 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.
[2] J. Peng, L. Chu, T. Wang, T. Fwa, Analysis of vehicle skidding potential on horizontal curves, Accident Analysis & Prevention, 152 (2021) 105960.
[3] S. Ling, F. Yu, D. Sun, G. Sun, L. Xu, A comprehensive review of tire-pavement noise: Generation mechanism, measurement methods, and quiet asphalt pavement, Journal of Cleaner Production, 287 (2021) 125056.
[4] X. Zhao, L. Xue, F. Xu, Asphalt pavement paving segregation detection method using more efficiency and quality texture features extract algorithm, Construction and Building Materials, 277 (2021) 122302.
[5] S. Ranjbar, F.M. Nejad, H. Zakeri, An image-based system for asphalt pavement bleeding inspection, International Journal of Pavement Engineering,  (2021) 1-17.
[6] B. Mataei, F.M. Nejad, M. Zahedi, H. Zakeri, Evaluation of pavement surface drainage using an automated image acquisition and processing system, Automation in Construction, 86 (2018) 240-255.
[7] International Organization for Standardization. ISO 13473-1: 2019 Characterisation of pavement texture by the use of surface profiles – Part 1: determination of mean profile depth. Geneva, Switzerland: International Organization for Standardization, (2019).
[8] D. Zhang, Q. Zou, H. Lin, X. Xu, L. He, R. Gui, Q. Li, Automatic pavement defect detection using 3D laser profiling technology, Automation in Construction, 96 (2018) 350-365.
[9] W. Luo, L. Liu, L. Li, Measuring rutting dimension and lateral position using 3D line scanning laser and inertial measuring unit, Automation in Construction, 111 (2020) 103056.
[10] S. Dong, S. Han, Q. Zhang, X. Han, Z. Zhang, T. Yao, Three-dimensional evaluation method for asphalt pavement texture characteristics, Construction and Building Materials, 287 (2021) 122966.
[11] N. Dong, J.A. Prozzi, F. Ni, Reconstruction of 3D pavement texture on handling dropouts and spikes using multiple data processing methods, Sensors, 19(2) (2019) 278.
[12] M. Ran, S. Xiao, X. Zhou, W. Xiao, Asphalt pavement texture 3D reconstruction based on binocular vision system with SIFT algorithm, in:  2017 International Conference on Smart Grid and Electrical Automation (ICSGEA), IEEE, 2017, pp. 213-218.
[13] R.B. Kogbara, E.A. Masad, D. Woodward, P. Millar, Relating surface texture parameters from close range photogrammetry to Grip-Tester pavement friction measurements, Construction and Building Materials, 166 (2018) 227-240.
[14] M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds, ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 179 (2012) 300-314.
[15] S. Zancajo-Blazquez, D. Gonzalez-Aguilera, H. Gonzalez-Jorge, D. Hernandez-Lopez, An automatic image-based modelling method applied to forensic infography, PloS one, 10(3) (2015) e0118719.
[16] A. Ahmed, M. Ashfaque, M.U. Ulhaq, S. Mathavan, K. Kamal, M. Rahman, Pothole 3d reconstruction with a novel imaging system and structure from motion techniques, IEEE Transactions on Intelligent Transportation Systems,  (2021).
[17] D. Lowe, BObject recognition from local scale-invariant features,[in Proc. 7th Int. Conf, Computer Vision, Kerkyra, Greece,  (1999) 1150-1157.
[18] M.A. Fischler, R.C. Bolles, ‘Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography’, Commun, in, ACM, (1981).
[19] ASTM E965-15. "Standard test method for measuring pavement macrotexture depth using a volumetric technique." Annual book of American society for testing materials. ASTM standards (2015).