International roughness index

Roughness progression for a road in Texas, US. Blue dots show the times of maintenance.

The international roughness index (IRI) is the roughness index most commonly obtained from measured longitudinal road profiles. It is calculated using a quarter-car vehicle math model, whose response is accumulated to yield a roughness index with units of slope (in/mi, m/km, etc.).[1][2] Although a universal term, IRI is calculated per wheelpath, but can be expanded to a Mean Roughness Index (MRI) when both wheelpath profiles are collected. This performance measure has less stochasticity and subjectivity in comparison to other pavement performance indicators, such as PCI, but it is not completely devoid of randomness.[3] The sources of variability in IRI data include the difference among the readings of different runs of the test vehicle and the difference between the readings of the right and left wheel paths.[4][5] Despite these facts, since its introduction in 1986,[6][7][8] the IRI has become the road roughness index most commonly used worldwide for evaluating and managing road systems.

The measurement of IRI is required for data provided to the United States Federal Highway Administration,[1][9] and is covered in several standards from ASTM International: ASTM E1926 - 08,[10] ASTM E1364 - 95(2005),[11] and others. IRI is also used to evaluate new pavement construction, to determine penalties or bonus payments based on smoothness.

  1. ^ a b Piryonesi, S. Madeh; El-Diraby, Tamer E. (2021-02-01). "Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling". Journal of Infrastructure Systems. 27 (2): 04021005. doi:10.1061/(ASCE)IS.1943-555X.0000602. ISSN 1076-0342. S2CID 233550030.
  2. ^ Sayers, M.W.; Karamihas, S.M. (1998). "Little Book of Profiling" (PDF). University of Michigan Transportation Research Institute. Archived from the original (PDF) on 2018-05-17. Retrieved 2010-03-07.
  3. ^ Piryonesi, S. Madeh; El-Diraby, Tamer E. (2021-02-01). "Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling". Journal of Infrastructure Systems. 27 (2): 04021005. doi:10.1061/(ASCE)IS.1943-555X.0000602. ISSN 1076-0342. S2CID 233550030.
  4. ^ Piryonesi, S. M. (2019). The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (PhD dissertation). University of Toronto.
  5. ^ Piryonesi, S. Madeh; El-Diraby, Tamer E. (2020-09-11). "Examining the Relationship Between Two Road Performance Indicators: Pavement Condition Index and International Roughness Index". Transportation Geotechnics. 26: 100441. doi:10.1016/j.trgeo.2020.100441. S2CID 225253229 – via Elsevier Science Direct.
  6. ^ Sayers, M.W., Gillespie, T. D., and Paterson, W.D. Guidelines for the Conduct and Calibration of Road Roughness Measurements, World Bank Technical Paper No. 46, The World Bank, Washington DC, 1986.
  7. ^ Sayers, M. (1984). Guidelines for the conduct and calibration of road roughness measurements. University of Michigan, Highway Safety Research Institute. OCLC 173314520.
  8. ^ Sayers, M. W. (Michael W.) (1986). International road roughness experiment : establishing methods for correlation and a calibration standard for measurements. World Bank Technical Paper No. 45. Washington, D.C.: World Bank. ISBN 0-8213-0589-1. OCLC 1006487409.
  9. ^ "National Performance Management Measures; Assessing Pavement Condition for the National Highway Performance Program and Bridge Condition for the National Highway Performance Program". Federal Register. 2017-01-18. Retrieved 2021-02-25.
  10. ^ "ASTM E1926 - 08(2015) Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements". www.astm.org. Retrieved 2019-12-19.
  11. ^ "ASTM E1926 - 08(2015) Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements". www.astm.org. Retrieved 2019-12-19.