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Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

Author

Listed:
  • Mostafa M. Radwan

    (Faculty of Engineering, Al-Maaqal University, Al-Maaqal, Basra 61014, Iraq
    Faculty of Engineering, Nahda University, Nahda University Road, Beni Suif 52611, Egypt)

  • Ahmad Mousa

    (Department of Civil Engineering, Faculty of Science and Engineering, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China
    Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Subang Jaya 47500, Malaysia)

  • Elsaid Mamdouh Mahmoud Zahran

    (Department of Civil Engineering, Faculty of Science and Engineering, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China)

Abstract

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Suggested Citation

  • Mostafa M. Radwan & Ahmad Mousa & Elsaid Mamdouh Mahmoud Zahran, 2024. "Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3158-:d:1373095
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    References listed on IDEAS

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    1. Sullivan, Joe H. & Warkentin, Merrill & Wallace, Linda, 2021. "So many ways for assessing outliers: What really works and does it matter?," Journal of Business Research, Elsevier, vol. 132(C), pages 530-543.
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