Development of a Model on Pavement Condition Index

Year : 2026 | Volume : 13 | Issue : 01 | Page : 1 7
    By

    Ramireddy Sushmitha,

  • Raj Kumar Naik,

  • Mounika,

  • Shashank,

  • Madhav,

  1. Assistant Professor, Department, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, india
  2. B. Tech Student, Civil Engineering Department, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andrapradesh, India
  3. student, Civil Engineering Department, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra pradesh, India
  4. student, Civil Engineering Department, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, india
  5. student, Civil Engineering Department, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra pradesh, india

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.

[This article belongs to Journal of Industrial Safety Engineering ]

How to cite this article:
Ramireddy Sushmitha, Raj Kumar Naik, Mounika, Shashank, Madhav. Development of a Model on Pavement Condition Index. Journal of Industrial Safety Engineering. 2026; 13(01):1-7.
How to cite this URL:
Ramireddy Sushmitha, Raj Kumar Naik, Mounika, Shashank, Madhav. Development of a Model on Pavement Condition Index. Journal of Industrial Safety Engineering. 2026; 13(01):1-7. Available from: https://journals.stmjournals.com/joise/article=2026/view=239052


References

  1. Afridi MA, Erlingsson S, Sjögren L, Englund C. Predicting pavement condition index using an ML approach for a municipal street network. J Transp Eng Part B Pavements. 2025;151(2):1-13. doi:10.1061/JPEODX.PVENG-1568.
  2. Ali AA, Milad A, Hussein A, Md Yusoff NIM, Heneash U. Predicting pavement condition index based on the utilization of machine learning techniques: A case study. J Road Eng. 2023;3(3):266-78. doi:10.1016/j.jreng.2023.04.002.
  3. Ali A, Heneash U, Hussein A, Eskebi M. Predicting pavement condition index using fuzzy logic technique. Infrastructures. 2022;7(7):91. doi:10.3390/infrastructures7070091.
  4. Majidifard H, Adu-Gyamfi YA, Buttlar WG. Deep machine learning approach to develop a new asphalt pavement condition index. Constr Build Mater. 2020;247:118513. doi:10.1016/j.conbuildmat.2020.118513.
  5. Tawalare A, Vasudeva Raju K. Pavement performance index for Indian rural roads. Perspect Sci. 2016;8:447-51. doi:10.1016/j.pisc.2016.04.101.
  6. Setyawan A, Nainggolan J, Budiarto A. Predicting the remaining service life of road using pavement condition index. Procedia Eng. 2015;125:417-23. doi:10.1016/j.proeng.2015.11.108.
  7. Tare V, Goliya HS, Bhatore A, Meashram K. Pavement deterioration modeling for low volume roads. New Delhi: Indian Roads Congress; 2013.
  8. Shahnazari H, Tutunchian MA, Mashayekhi M, Amini AA. Application of soft computing for prediction of pavement condition index. J Transp Eng. 2012;138(12):1495-506. doi:10.1061/(ASCE)TE.1943-5436.0000454.
  9. Oktopianto Y, Antonius RA, Rochim A. An artificial neural network approach for predicting pavement distress: A case study toward sustainable road maintenance. Adv Sustain Sci Eng Technol. 2025;7(3):02503019. doi:10.26877/asset.v7i3.2133.
  10. Sholevar N, Golroo A, Esfahani SR. Machine learning techniques for pavement condition evaluation. Autom Constr. 2022;136:104190. doi:10.1016/j.autcon.2022.104190.

Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 28/01/2026
Accepted 28/02/2026
Published 05/03/2026
Publication Time 36 Days


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