Development of a Model on Pavement Condition Index

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Ramireddy Sushmitha,

Abstract

India has one of the most extensive road networks globally, comprising various categories such as
National Highways, State Highways, District Roads, and Village Roads. Each category of road plays
an impressive role in economic development and connectivity among different important roads. But,
the condition of these roads varies effectively due to difference in traffic intensity, construction quality,
maintenance practices and climatic condition. The present research work conducted pavement
condition surveys and mapping of common failures across several road categories was done. Several
pavement distresses such as rutting, alligator cracking, longitudinal and transverse cracking, ravelling,
potholes, patch work, depression and settlement were identified and their deterioration patterns were
understood. The study results showed that percentage alligator cracking, longitudinal and transverse
cracking, ravelling, potholes, patch work, depression and settlement, were found to affect Pavement
Condition Index.

Keywords: Pavement Condition Index, Potholes, Regression Model, Rutting, Cracks.

How to cite this article:
Ramireddy Sushmitha. Development of a Model on Pavement Condition Index. Journal of Industrial Safety Engineering. 2026; 13(01):-.
How to cite this URL:
Ramireddy Sushmitha. Development of a Model on Pavement Condition Index. Journal of Industrial Safety Engineering. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joise/article=2026/view=239052


References

1. Afridi, M.A., Erlingsson, S., Sjogren, L., Englund, C. (2025). “Predicting Pavement Condition
Index Using an ML Approach for a Municipal Street Network”. Journal of Transportation
Engineering, Volume 151, issue 2, PP: 1-13. https://doi.org/10.1061/JPEODX.PVENG-1568
2. Ali, A.A., Milad, A., Hussein, A., Yusoff, N.I.M., Heneash, U. (2023). “Predicting Pavement
Condition Index Based on the Utilization of Machine Learning Techniques: A Case Study”. Journal
of Road Engineering, Volume 3, Issue 3, pp: 266-278. https://doi.org/10.1016/j.jreng.2023.04.002
3. Ali, A.A., Milad, A., Hussein, A., Heneash, U.,Yusoff, N.I.M. (2022). “Predicting Pavement
Condition Index using Fuzzy Logic Technique”. Infrastructure, Volume 7, Issue 7,
https://doi.org/10.3390/infrastructures7070091.
4. Majidifard, H., Gyamfi, Y.A., Buttlar, W.G. (2020). “Deep Machine Learning Approach to Develop
a New Asphalt Pavement Condition Index”. Construction and Building Materials, Volume 247.
https://doi.org/10.1016/j.conbuildmat.2020.118513
5. Tawalare, A., Vasudeva Raju, K. (2016). “Pavement Performance Index for Indian rural roads”.
Perspective science, Volume 8, PP: 447-451. https://doi.org/10.1016/j.pisc.2016.04.101
Development of a Model on Pavement Condition Index Sushmitha et al.
6. Setyawan, A., Nainggolan, J., Budiarto, A. (2015). “Predicting the remaining service life of road
using pavement condition index”. Procedia Engineering, Volume 125, PP: 417-423.
7. Tare, V., Goliya, H.S., Bhatore, A., and Meashram, K. (2013). “Pavement Deterioration Modeling
for Low Volume Roads”, IRC.
8. Habib, S., Mohammad, A., Tutunchian., Mehdi, M., and Amir, A.A. (2012). “Application of Soft
Computing for Prediction of Pavement Condition Index”. Journal of transportation engineering.


Ahead of Print Subscription Original Research
Volume 13
01
Received 28/01/2026
Accepted 28/02/2026
Published 05/03/2026
Publication Time 36 Days


Login


My IP

PlumX Metrics