Pavement condition index explained

The pavement condition index (PCI) is a numerical index between 0 and 100, which is used to indicate the general condition of a pavement section. The PCI is widely used in transportation civil engineering[1] and asset management, and many municipalities use it to measure the performance of their road infrastructure and their levels of service.[2] It is a statistical measure and requires manual survey of the pavement. This index was originally developed by the United States Army Corps of Engineers as an airfield pavement rating system,[3] but later modified for roadway pavements[4] and standardized by the ASTM.[5] The surveying processes and calculation methods have been documented and standardized by ASTM for both roads and airport pavements:

Calculation

The method is based on a visual survey of the number and types of distresses in a pavement. First, the type and extent of existing distresses, their severity level is collected. Next, distress density is calculated for each type of distress. The density values are translated into deduct value (DV) and corrected deduct value (CDV) using a set of curves proposed by the ASTM. The ASTM does not include the formulae of these curves, but they are recalculated by researchers. Finally, the value of the PCI is calculated in an iterative process. The result of the analysis is a numerical value between 0 and 100, with 100 representing the best possible condition and 0 representing the worst possible condition.

Pavement distress types for asphalt pavements include:

For relatively small pavement systems, the entire system may be surveyed. For large pavement systems, the process may involve surveying a random or representative sample of the entire system with the following steps:

This condition index can give a good indication of the pavement condition of a network.[8] However, trained personnel are required to complete the complicated survey procedure.[9]

Categorization

The ASTM divides the PCI into seven classes as follows, but in practice a PCI lower than 40 is almost impassable.[10]

!PCI range!Class
86-100Good
71-85Satisfactory
56-70Fair
41-55Poor
26-40Very Poor
11-25Serious
0-10Failed

Relationship with roughness

PCI is correlated with the performance indicators measuring roughness such as international roughness index (IRI).[11] Generally, a road with a high PCI has a low IRI, and a road with a high IRI has a low PCI. However, this is not always the case. For example, two roads with the same PCI can have significantly different IRI values as a result of having different types of distresses or grades.

See also

Notes and References

  1. Web site: Using Data Analytics for Cost-Effective Prediction of Road Conditions: Case of The Pavement Condition Index:[summary report]]. Piryonesi. S. M.. El-Diraby. T.. 2018. FHWA-HRT-18-065. United States. Federal Highway Administration. Office of Research, Development, and Technology.. National Transportation Library Repository & Open Science Access Portal. dead. https://web.archive.org/web/20190202153647/https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/18065/index.cfm. 2019-02-02.
  2. The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads . Piryonesi . S. M. . 2019 . PhD dissertation . University of Toronto .
  3. Web site: Development of a Pavement Maintenance Management System, Vol. I. Airfield Pavement Condition Rating . https://web.archive.org/web/20210603164822/https://apps.dtic.mil/sti/citations/ADA048884. live. June 3, 2021. Shahin. M. Y.. Darter. M. I.. Kohn. S. D.. 1977. Construction Engineering Research Laboratory.
  4. Web site: Research and Innovative Technology Administration (RITA), National Transportation Library. Development Of A Pavement Condition Index For Roads And Streets. Tris Online Record. dead. https://web.archive.org/web/20120208054414/http://ntlsearch.bts.gov/repository/record/tris/00183146.html. 2012-02-08.
  5. 10.1061/(ASCE)IS.1943-555X.0000512. Piryonesi. S. M.. El-Diraby. T. E.. 2020. Published online: December 21, 2019. Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index. Journal of Infrastructure Systems. 26. 1. 04019036. 213782055 .
  6. Web site: Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys . ASTM International.
  7. Web site: Standard Test Method for Airport Pavement Condition Index Surveys . ASTM International.
  8. Web site: William. Yardley. Jan 26, 2008. Building Costs Deal Blow to Local Budgets. New York Times.
  9. Web site: Main Menu (List of Modules) . 2010-06-19 . dead . https://web.archive.org/web/20100211000552/http://training.ce.washington.edu/WSDOT/. 2010-02-11 . Washington State DOT pavement guide.
  10. Bryce. J.. Boadi. R.. Groeger. J.. 2019. Relating Pavement Condition Index and Present Serviceability Rating for Asphalt-Surfaced Pavements. Transportation Research Record: Journal of the Transportation Research Board. 2673. 3. 308–312. 10.1177/0361198119833671. 116809787 .
  11. 2021-01-01. Examining the relationship between two road performance indicators: Pavement condition index and international roughness index. Transportation Geotechnics. en. 26. 100441. 10.1016/j.trgeo.2020.100441. 2214-3912. Piryonesi. S. Madeh. El-Diraby. Tamer E.. 225253229 .

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