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Pavement performance evaluation models for South Carolina

  • Highway Engineering
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Abstract

This paper develops pavement performance evaluation models using data from primary and interstate highway systems in the state of South Carolina, USA. Twenty pavement sections are selected from across the state, and historical pavement performance data of those sections are collected. A total of 8 models were developed based on regression techniques, which include 4 for Asphalt Concrete (AC) pavements and 4 for Jointed Plain Concrete Pavements (JPCP). Four different performance indicators are considered as response variables in the statistical analysis: Present Serviceability Index (PSI), Pavement Distress Index (PDI), Pavement Quality Index (PQI), and International Roughness Index (IRI). Annual Average Daily Traffic (AADT), Free Flow Speed (FFS), precipitation, temperature, and soil type (soil Type A from Blue Ridge and Piedmont Region, and soil Type B from Coastal Plain and Sediment Region) are considered as predictor variables. Results showed that AADT, FFS, and precipitation have statistically significant effects on PSI and IRI for both JPCP and AC pavements. Temperature showed significant effect only on PDI and PQI (p < 0.01) for AC pavements. Considering soil type, Type B soil produced statistically higher PDI and PQI (p < 0.01) compared to Type A soil on AC pavements; whereas, Type B soil produced statistically higher IRI and PSI (p < 0.001) compared to Type A soil on JPCP pavements. Using the developed models, local transportation agencies could estimate future corrective actions, such as maintenance and rehabilitation, as well as future pavement performances.

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Authors and Affiliations

  1. Dept. of Civil and Environmental Engineering, University of South Carolina, 300 Main St, Columbia, SC, 29208, USA

    Md Mostaqur Rahman, M. Majbah Uddin & Sarah L. Gassman

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  1. Md Mostaqur Rahman
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  2. M. Majbah Uddin
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  3. Sarah L. Gassman
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Correspondence to Md Mostaqur Rahman.

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Rahman, M.M., Uddin, M.M. & Gassman, S.L. Pavement performance evaluation models for South Carolina. KSCE J Civ Eng 21, 2695–2706 (2017). https://doi.org/10.1007/s12205-017-0544-7

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  • DOI: https://doi.org/10.1007/s12205-017-0544-7

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