  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.
