Optimal Pavement Maintenance Strategy Based on the Relationship between Pavement Condition Index and Roughness Index

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Pavement maintenance is crucial for ensuring road safety, reducing congestion, and minimizing repair costs. However, determining the optimal timing and strategy for pavement maintenance remains a challenge. This study investigated the correlation between the Pavement Condition Index (PCI) and Roughness Index (RI) to develop a numerical model for describing relationship of the two indices for pavement maintenance decision-making. Using statistical analysis and data visualization techniques, a significant correlation was found between PCI and RI. The study revealed a moderate correlation between PCI and IRI (R² = 0.47), indicating that 47% of PCI variations can be explained by IRI. While this suggests that the model is capturing a significant amount of the relationship between PCI and IRI, there is still room for improvement, as about 53% of the variance in PCI is not explained by the model. Since the PCI is a measure of road pavement conditions (on a scale typically ranging from 0 to 100), an RMSE of 7.77 means that the model's predictions for PCI are, on average, about 7 to 8 PCI units off from the actual value. The study established a clear relationship between pavement condition and surface roughness, enabling the development of a model to guide maintenance decisions. The study recommends prioritizing roads with PCI ≥ 50.3 and RI ≤ 5.12 m/km, alongside regular monitoring to ensure timely, cost-effective maintenance. Regular monitoring of PCI and RI values is also recommended to ensure timely maintenance and prevent costly repairs.

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Engineering Headway (Volume 33)

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347-363

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February 2026

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© 2026 Trans Tech Publications Ltd. All Rights Reserved

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[1] Keegan K, Wolff H. Modern pavement condition assesment methods for periodic maintenance and rehabilitation planning on a network level-a United States approach with possible application opportunities in South África. InConference on Asphalt Pavements for Southern Africa (CAPSA15) 2015 Aug.

Google Scholar

[2] Rijal P. H. & Medis, S. S. Study of Pavement Condition Index Relationship with International Roughness Index (IRI) on Flexible Pavement. MATEC Web of Conferences, (2019). Issue 258, pp.1-6.

DOI: 10.1051/matecconf/201925803019

Google Scholar

[3] Stephen A. Arhin, Lakeasha N. Williams, Asteway Ribbiso, & Melissa F. Anderson Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area. Journal of Civil Engineering Research (2015). 5 (1): 10-17, ISSN: 2163-2316 e-ISSN: 2163-2340.

Google Scholar

[4] Hui Wang, Zhang Chen, and Lijun Sun Pavement Roughness Evaluation for Urban Road Management. ASCE Library, ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation (2013).

DOI: 10.1061/9780784413159.394

Google Scholar

[5] Sayers Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements (English). World Bank technical paper; no. WTP 45 Washington, DC: The World Bank (1986).. http://documents.worldbank.org/curated/en/326081468740204115.

Google Scholar

[6] Arati P., (2018). Predicting the Future Service Life of Road using Pavement Condition Index. International Journal for Research in Applied Science and Engineering Technology, 6.

DOI: 10.22214/IJRASET.2018.3495

Google Scholar

[7] E. Macioszek and A. Kurek, "Road Traffic Distribution on Public Holidays and Workdays on Selected Road Transport Network Elements," Transp. Probl., vol. 16, no. 1, p.127–138, 2021.

DOI: 10.21307/tp-2021-011

Google Scholar

[8] McQueen, J.M., & Timm, D.H. Part 2: Pavement Monitoring, Evaluation, and Data Storage: Statistical Analysis of Automated Versus Manual Pavement Condition Surveys (2005). Transportation Research Record, 1940, 53-62.

DOI: 10.1177/0361198105194000107

Google Scholar

[9] Satkar S., Khadka, R. Assessment of Relationship between Road Roughness and Pavement Surface Condition(2021).

DOI: 10.3126/jacem.v6i0.38357

Google Scholar

[10] S. Abbas, A. A. Khalil, M. S. Ali, S. Sultana, and S. H. H. Shah, "Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks," Eur. J. Appl. Sci. Eng. Technol., vol. 2, no. 2, p.224–232, 2024.

DOI: 10.59324/ejaset.2024.2(2).15

Google Scholar

[11] F.A.R. Temimi, A. Hadi M. Ali, and A. H. F. Obaidi, "The Pavement Condition Index (PCI) Method for Evaluating Pavement Distresses of The Roads in Iraq- A Case Study in Al- Nasiriyah City," Univ. Thi-Qar J. Eng. Sci., vol. 11, no. 2, p.17–23, 2021.

DOI: 10.31663/tqujes.11.2.394(2021)

Google Scholar

[12] Amr A. Elhadidy, El-Badawy S., Elbeltagi E., (2019). A simplified pavement condition index regression model for pavement evaluation. International Journal of Pavement Engineering, 22.

DOI: 10.1080/10298436.2019.1633579

Google Scholar

[13] Amer, M.A. Study a Relationship between Manual and Function Pavement Condition (2015).

Google Scholar

[14] Ahmed, B. Q. Developing of Pavement Management System (PMS) for EMU Campus Pavement in GIS Environment (Doctoral dissertation, Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)) (2013).

Google Scholar

[15] Betkier, I., & Macioszek, E. (2022, November 25). Characteristics of Parking Lots Located along Main Roads in Terms of Cargo Truck Requirements: A Case Study of Poland. Sustainability, 14(23), 15720.

DOI: 10.3390/su142315720

Google Scholar

[16] El-Raof, H.S., El-Hakim, R.T., El-Badawy, S.M., & Afify, H.A. (2019). General Procedure for Pavement Maintenance/Rehabilitation Decisions Based on Structural and Functional Indices. Recent Developments in Pavement Engineering.

DOI: 10.1007/978-3-030-34196-1_2

Google Scholar

[17] Grivas, D.A., Schultz, B.C., & Waite, C.A. (1992). Determination Of Pavement Distress Index for Pavement Management. Transportation Research Record. Hui Wang, Zhang Chen, Lijun Sun (2013). Pavement Roughness Evaluation for Urban Road Management.

DOI: 10.1061/9780784413159.394

Google Scholar

[18] Muji R., Setyawan A., Handayani F. S., Dipta A.A. (2023). Evaluation of functional and structural conditions on flexible pavements using pavement condition index (PCI) and international roughness index (IRI) methods. https://doi.org/10.1051/e3sconf/202342905011 Ong, Ghim Ping, Noureldin, S., and Kumares C. S., "Methodology to evaluate quality of pavement surface distress data collected by automated techniques." Transportation research record 2093, no. 1 (2009): 3-11.

DOI: 10.3141/2093-01

Google Scholar

[19] Papageorgiou, G.P. (2019). Appraisal of Road Pavement Evaluation Methods. Journal of Engineering Science and Technology Review, 12, 158-166.

Google Scholar

[20] S. Arhin, E. C. Noel (2014). Predicting Pavement Condition Index from International Roughness Index in Washington, DC. Arhin, S., Williams, L., Asteway, R., Melissa F. A., (2015). Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area. Journal of Civil Engineering Research.

DOI: 10.31026/j.eng.2020.12.05

Google Scholar

[21] Xiaoyang J, Baoshan H, Di Zhu, Qiao Dong, M. Woods (2018). Influence of Measurement Variability of International Roughness Index on Uncertainty of Network-Level Pavement Evaluation

DOI: 10.1061/JPEODX.0000034

Google Scholar

[22] ASTM, 2007. ASTM D 6433-07, "Standard Practice for Roads and Parking Lots PavementCondition Index Surveys.

Google Scholar