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.

翻译：本文利用美国南卡罗来纳州主干道与州际公路系统的数据，建立了路面性能评估模型。从全州范围内选取了20个路面路段，收集了这些路段的历史路面性能数据。基于回归技术共开发了8个模型，其中4个适用于沥青混凝土路面，4个适用于普通混凝土路面。统计分析中将四种不同性能指标作为响应变量：现时服务能力指数、路面病害指数、路面质量指数和国际平整度指数。预测变量包括年平均日交通量、自由流速度、降水量、温度以及土壤类型（来自蓝岭与皮德蒙特地区的A型土，以及来自沿海平原与沉积区的B型土）。结果表明，对于普通混凝土路面和沥青混凝土路面，年平均日交通量、自由流速度和降水量对现时服务能力指数和国际平整度指数均有统计显著影响。温度仅对沥青混凝土路面的路面病害指数和路面质量指数（p < 0.01）具有显著影响。在土壤类型方面，对于沥青混凝土路面，B型土相比A型土产生的路面病害指数和路面质量指数更高（p < 0.01）；而对于普通混凝土路面，B型土相比A型土产生的国际平整度指数和现时服务能力指数更高（p < 0.001）。利用所开发模型，地方交通部门可预估未来养护与维修等纠正措施需求，以及路面未来性能表现。