Pavement Deterioration Rate and Maintenance Cost for Low-Volume Roads

Al-Suleiman, Turki I; Bazlamit, Subhi M; Azzama, Mahmoud; Ahmad, Hesham S · 2020 · Crossref

DOI: 10.1051/matecconf/202031206002

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Summary

This study addresses the challenge of optimizing maintenance budgets for low-volume road networks, where limited funding often leads to delayed maintenance and excessive costs. The authors aim to develop predictive formulas that relate maintenance costs to pavement deterioration rates, specifically for roads where weather-related factors, rather than traffic loading, are the primary causes of distress. By establishing these relationships, the research seeks to improve Pavement Maintenance Management Systems (PMMS) by enabling more accurate cost estimation and prioritization of Maintenance and Rehabilitation (M&R) projects. The methodology involved a case study of the road network at Al-Zaytoonah University of Jordan (ZUJ), focusing on a specific branch called the Ring Road, which comprised 46 sections. The researchers conducted two cycles of field inspections one year apart (2017 and 2018) to evaluate pavement conditions using the Pavement Condition Index (PCI), calculated via the PAVER system software. Between the inspections, M&R projects were implemented on sections rated as poor, very poor, serious, or failed. The study classified these maintained sections into three groups based on their pre-maintenance condition. Regression analysis was then performed to determine the relationship between the change in PCI ($\Delta$PCI) and the maintenance cost per square meter ($Mc$), testing linear, polynomial, and logarithmic models. The results demonstrated that maintenance cost is strongly dependent on the initial pavement condition. For sections initially rated as "Poor," the logarithmic model provided the highest reliability ($R^2 = 0.9557$), while for "Failed & Serious" sections, the polynomial model was most accurate ($R^2 = 0.9955$). Linear regression also proved effective for the "Poor" category ($R^2 = 0.8728$). In contrast, models for "Very Poor" sections showed low reliability ($R^2 < 0.17$). The study found that sections without maintenance experienced a decline in PCI, whereas maintained sections showed significant improvements, validating the efficacy of timely intervention. The derived equations allow for the prediction of maintenance costs based on deterioration rates and pavement area. The significance of this work lies in providing a practical prediction model for estimating maintenance costs in low-volume road networks, aiding engineering managers in efficient decision-making and budget allocation. The high $R^2$ values for specific condition categories indicate that the equations are valid tools for cost forecasting. The authors conclude that while the current models are effective, future studies should expand the dataset to include longer time periods and broader areas to account for variables like traffic volume and pavement structure. Additionally, they recommend integrating modern evaluation techniques, such as image processing and remote sensing, to further enhance the robustness of pavement condition assessments.

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