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Pavement macrotexture measurement using tire/road noise

  • Yiying ZhangEmail author
  • J. Gregory McDaniel
  • Ming L. Wang
Original Paper

Abstract

A method is developed to estimate pavement macrotexture depth (MTD), using measurements from a microphone mounted underneath a moving vehicle. The acoustic energy is assumed to have positive linear correlation with MTD of the pavement. However, the acoustic measurements will include tire-generated sound that carries information about the road features as well as noise generated by the environment and vehicle. The variations in frequency of the noise are assumed to be small compared to the variations in frequency of the signal related to road features, which allows principal component analysis (PCA) to filter noise from microphone data prior to estimating its energy over an optimally selected bandwidth. The acoustic energy computed from the first principal component (PC) is termed as PCA energy, which is an important variable for MTD prediction. The frequency band most relative to pavement macrotexture was determined to be 140–700 Hz. Then, an MTD prediction model was built based on a Taylor series expansion with two variables, PCA energy and driving speed. The model parameters were obtained from an engineered track (interstate highway) with known MTD and then applied to urban roads for the feasibility test. The predicted MTD extends its range from 0.4–1.5 mm of the engineered track to 0.2–3 mm, which is the typical range of MTD. In addition, the excellent repeatability of the MTD prediction is demonstrated by the urban road test. Moreover, the potential to use the predicted MTD for pavement condition assessment is discussed. Therefore, the PCA Energy Method is a reliable, efficient, and cost-effective approach to predict equivalent MTD for engineering applications as an important index for pavement condition assessment.

Keywords

Macrotexture Mean texture depth (MTD) Tire-generated sound Principal component analysis (PCA) Pavement condition index (PCI) 

References

  1. 1.
    ISO 13473–1 (1997) Characterization of pavement texture by use of surface profiles—part 1: determination of mean profile depth, 1st edn. American National Standards Institute, Washington, DCGoogle Scholar
  2. 2.
    Stroup-Gardiner M, Brown ER (2000) Segregation in hot-mix asphalt pavements. Report no. 441, Transportation Research BoardGoogle Scholar
  3. 3.
    Henry JJ (2000) Evaluation of pavement friction characteristics, vol 291. Transportation Research BoardGoogle Scholar
  4. 4.
    Flintsch GW, de Leon E, McGhee K, Al-Qadi I (2003) Pavement surface macrotexture measurement and application. Transp Res Rec J Transp Res Board 1860(1):168–177CrossRefGoogle Scholar
  5. 5.
    ASTM (2004) Standard test method for measuring pavement macrotexture depth using a volumetric technique. ASTM E965, West ConshohockenGoogle Scholar
  6. 6.
    ASTM (2009) Standard practice for calculating pavement macrotexture mean profile depth. ASTM E1845, West ConshohockenGoogle Scholar
  7. 7.
    Veres RE, Henry JJ, Lawther JM (1975) Use of tire noise as a measure of pavement macrotexture. In: Rose JG (ed) Symposium named Surface texture versus skidding: measurements, frictional aspects, and safety features of tire-pavement interactions. ASTM, West Conshohocken, pp 18–28Google Scholar
  8. 8.
    Sandberg U, Ejsmont JA (2002) Tyre/road noise reference book. Informex, KisaGoogle Scholar
  9. 9.
    Saykin V, Zhang Y, Cao Y, Wang ML, McDaniel JG (2013) Pavement macrotexture monitoring through the sound generated by tire-pavement interaction. J Eng Mech 139(3):264–271CrossRefGoogle Scholar
  10. 10.
    Zhang Y, Ma X, McDaniel JG, Wang ML (2012) Statistical analysis of acoustic measurements for assessing pavement surface condition. In: Proceedings of SPIE smart structures and materials + nondestructive evaluation and health monitoring. International Society for Optics and Photonics, Bellingham, p 83471FGoogle Scholar
  11. 11.
    Zhang Y, McDaniel J, Gregory, Wang ML (2014) Estimation of pavement macrotexture by principal component analysis of acoustic measurements. J Transp Eng 140(2):04013004-1–04013004-11Google Scholar
  12. 12.
    Sandberg U (2003) The multi-coincidence peak around 1000 Hz in tyre/road noise spectra. In: Euronoise Conference, paper ID, vol 498Google Scholar
  13. 13.
    KMS & Associates, Inc. (2007) Micro PAVER: Pavement Management System. http://www.city.pittsburgh.pa.us/district8/assets/07_pavement_mgt_system.pdf. Accessed 8 May 2013
  14. 14.
    Metro Nashville (2006) Metro Nashville long range paving plan, Chapter 3 pavement management data. http://mpw.nashville.gov/IMS/Paving/Documents/Chapter_3.pdf. Accessed 12 May 2013
  15. 15.
    Wang HQ, Song ZH, Wang H (2002) Statistical process monitoring using improved PCA with optimized sensor locations. J Process Control 12(6):735–744CrossRefGoogle Scholar
  16. 16.
    Pearson K (1901) On lines and planes of closest fit to systems of points is space. Philos Mag 6(23):559–572CrossRefGoogle Scholar
  17. 17.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417–441Google Scholar
  18. 18.
    Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  19. 19.
    Greenberg MD (1998) Advanced engineering mathematics, 2nd edn. Pearson Education, India, pp 1236–1242Google Scholar
  20. 20.
    Dienes P (1957) The Taylor series: an introduction to the theory of functions of a complex variable. Dover Publications, New YorkzbMATHGoogle Scholar
  21. 21.
    Roe PG, Webster DC, West G (1991) The relation between the surface texture of roads and accidents. Research report 296, Transport and Road Research Laboratory, Wokingham, UK, TRL. http://www.roadsafetyobservatory.com/Evidence/Details/10569. Accessed 6 Mar 2013
  22. 22.
    Wang ML, Birken R Shamsabadi SS (2014) Framework and implementation of a continuous network-wide health monitoring system for roadways. In: Proceedings of the SPIE 9063, nondestructive characterization for composite materials, aerospace engineering, civil infrastructure, and homeland security, Vol. 9063. California, pp 1–12Google Scholar
  23. 23.
    WesTrack (2001) Superpave mixture design guide. WesTrack Forensic Team consensus report. Federal Highway Administration, Washington, DCGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yiying Zhang
    • 1
    Email author
  • J. Gregory McDaniel
    • 2
  • Ming L. Wang
    • 3
  1. 1.Pavement and Road Maintenance Technology InstituteChina Merchants Chongqing Communications Technology Research and Design Institute Co. LtdChongqingPeople’s Republic of China
  2. 2.Department of Mechanical EngineeringBoston UniversityBostonUSA
  3. 3.Department of Civil and Environmental EngineeringNortheastern UniversityBostonUSA

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