Chapter
Feb 6, 2024

Determination of Mean Texture Depth of Pavement Using 3D Digital Image

Publication: International Conference on Road and Airfield Pavement Technology 2023

ABSTRACT

Mean texture depth (MTD) of pavement is a 3D texture parameter that has been found by pavement engineers and researchers to be useful for providing an assessment of a pavement’s performance in a number of aspects, including (i) the ability of pavement to drain surface runoff effectively during rainfall, (ii) the ability to maintain an adequate skid resistance for safe vehicle operations in wet weather, and (iii) the ability to keep tire-pavement noise caused by traffic within an acceptable limit. Hence, it is important to maintain the MTD of pavements at a sufficiently high level at the road network level. Currently it is not possible to determine MTD continuously at traffic speed for network level monitoring. Instead, mean profile depth (MPD) has been measured for the purpose of estimating MTD. Unfortunately, MPD being a 2D texture parameter along a linear profile, cannot provide a perfect representation of MTD. Studies have found that the relationship between MPD and MTD vary from one mixture type to another. This presents a major problem for network level monitoring of MTD. To overcome this limitation, direct determination of MTD from 3D digital images is preferred. This paper describes the limitations of currently available method for numerical MTD determination based on 3D digital images, and proposes possible approaches to overcome those limitations. Through comparison of the different methods, recommendations are given for the preferred approach and method for direct MTD determination at road network level.

Get full access to this article

View all available purchase options and get full access to this chapter.

REFERENCES

EN 13036-1, Road and airfield surface characteristics–Test methods–Part 1: Measurement of pavement surface macrotexture depth using a volumetric patch technique.
Afonso, M. L., M. Dinis-Almeida and C. S. Fael (2019). Characterization of the skid resistance and mean texture depth in a permeable asphalt pavement. IOP Conference Series: Materials Science and Engineering, IOP Publishing.
Alamdarlo, M. N. and S. Hesami (2018). “Optimization of the photometric stereo method for measuring pavement texture properties.” Measurement 127: 406-413.
Angst, C., F. Beltzung, D. Bosshardt, H. Grolimund and H. Pestalozzi (2008). Low-noise road surfaces in urban areas. Ittigen, Switzerland, Federal Roads Office.
ASTM (2015a). ASTM E965-15 Standard test method for measuring pavement macrotexture depth using a volumetric technique. West Conshohocken, PA., ASTM International.
ASTM (2015b). ASTM E1845-15 Standard practice for calculating pavement macrotexture mean profile depth. West Conshohocken, PA, ASTM International.
Chen, B., C. Xiong, W. Li, J. He and X. Zhang (2021). “Assessing Surface Texture Features of Asphalt Pavement Based on Three-Dimensional Laser Scanning Technology.” Buildings 11(12).
Chen, J., X. Huang, B. Zheng, R. Zhao, X. Liu, Q. Cao and S. Zhu (2019). “Real-time identification system of asphalt pavement texture based on the close-range photogrammetry.” Construction and Building Materials 226: 910-919.
Chu, L. and T. Fwa (2018). “Pavement skid resistance consideration in rain-related wet-weather speed limits determination.” Road materials and pavement design 19(2): 334-352.
Dan, H.-C., G.-W. Bai, Z.-H. Zhu, X. Liu and W. Cao (2022). “An improved computation method for asphalt pavement texture depth based on multiocular vision 3D reconstruction technology.” Construction and Building Materials 321: 126427.
Ding S., Y. Zhan, E. Yang and C. Wang (2020). “MTD measurement of asphalt pavement based on high precision laser section elevation.” Journal of Southeast University (Natural Science Edition).
Falconer, K. (2004). Fractal geometry: Mathematical foundations and applications, John Wiley & Sons.
Fisco, N. R. (2009). Comparison of Macrotexture Measurement Methods, The Ohio State University.
Flintsch, G. W., E. de León, K. K. McGhee and I. L. Ai-Qadi (2003). “Pavement Surface Macrotexture Measurement and Applications.” Transportation Research Record 1860(1): 168-177.
Fwa, T. F. and L. Chu (2021). “The concept of pavement skid resistance state.” Road Materials and Pavement Design 22(1): 101-120.
Gilbert, G. K. (1877). Report on the geology of the Henry Mountains. Monograph. Washington, D.C., 212.
ISO (2019). International Standard ISO 13473-1:2019(E). Characterization of pavement texture by use of surface profiles. Part 1: Determination of mean profile depth. Geneva, Switzerland, International Organization for Standardization.
J, H. J. (2000). Evaluation of pavement friction characteristics–A synthesis of highway practice. Washington DC, NCHRP Synthesis 291, National Cooperative Highway Research Program.
Kargah-Ostadi, N. and A. Howard (2015). “Monitoring Pavement Surface Macrotexture and Friction: Case Study.” Transportation Research Record 2525(1): 111-117.
Liang, J., X. Gu, Y. Chen, F. Ni and T. Zhang (2020). “A novel pavement mean texture depth evaluation strategy based on three-dimensional pavement data filtered by a new filtering approach.” Measurement 166: 108265.
Lim, M.-H. and P. C. Yuen (2015). “Entropy measurement for biometric verification systems.” IEEE Transactions on Cybernetics 46(5): 1065-1077.
Losa, M. and P. Leandri (2011). “The reliability of tests and data processing procedures for pavement macrotexture evaluation.” International Journal of Pavement Engineering 12(1): 59-73.
Lu, Q. and J. T. Harvey (2011). “Laboratory Evaluation of Open-Graded Asphalt Mixes with Small Aggregates and Various Binders and Additives.” Transportation Research Record 2209(2209): 61-69.
Plati, C., M. Pomoni and T. Stergiou (2017). “Development of a mean profile depth to mean texture depth shift factor for asphalt pavements.” Transportation Research Record 2641(1): 156-163.
Pomoni, M., C. Plati, A. Loizos and G. Yannis (2022). “Investigation of pavement skid resistance and macrotexture on a long-term basis.” International Journal of Pavement Engineering 23(4): 1060-1069.
Praticò, F., R. Vaiana and T. Iuele (2013). Acoustic absorption and surface texture: An experimental investigation.
Praticò, F. G., R. Vaiana and R. Fedele (2014). “A study on the dependence of PEMs acoustic properties on incidence angle.” International Journal of Pavement Engineering 16(7): 632-645.
Sandberg, U., and J. A. Ejsmont (2002). Tyre/road noise reference book. Kisa, Sweden, Informex.
Saykin, V. V. (2011). Pavement macrotexture monitoring through sound generated by the tire-pavement interaction, Northeastern University.
Schwarzenberger, R. (1990). “Fractal geometry: Mathematical foundations and applications, by Kenneth Falconer. 1990. ISBN 0-471-92287-0 (Wiley).” The Mathematical Gazette 74(469): 316-317.
Sezen, H. and N. Fisco (2013). “Evaluation and comparison of surface macrotexture and friction measurement methods.” Journal of Civil Engineering and Management 19(3): 387-399.
Tang, Y., L. Li, C. Wang, M. Chen, W. Feng, X. Zou and K. Huang (2019). “Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision.” Robotics and Computer-Integrated Manufacturing 59: 36-46.
Wambold, J. (1995). “International PIARC experiment to compare and harmonize texture and skid resistance measures.”
Wang, T., L. Chu and T. Fwa (2022). “Improved numerical method for determination of pavement mean texture depth from 3-dimensional digital image.” Construction and Building Materials 358: 129447.
Xin, Q., Z. Qian, Y. Miao, L. Meng and L. Wang (2017). “Three-dimensional characterisation of asphalt pavement macrotexture using laser scanner and micro element.” Road Materials and Pavement Design 18(sup3): 190-199.
Zhang, H., I. Ernst, S. Zuev, A. Börner, M. Knoche and R. Klette (2018). Visual Odometry and 3D Point Clouds Under Low-Light Conditions. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE.
Zuniga-Garcia, N. and J. A. Prozzi (2019). “High-definition field texture measurements for predicting pavement friction.” Transportation Research Record 2673(1): 246-260.

Information & Authors

Information

Published In

Go to International Conference on Road and Airfield Pavement Technology 2023
International Conference on Road and Airfield Pavement Technology 2023
Pages: 839 - 848

History

Published online: Feb 6, 2024

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Tangjie Wang
Chang’an Univ., China
Chang’an Univ., China (corresponding author). Email: [email protected]
T. F. Fwa
Chang’an Univ., China; National Univ. of Singapore, Singapore

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$158.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$158.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share