Development of Pavement Performance Prediction Models for Low-Volume Roads Using Functional Characteristics

Pavement evaluations are done to determine the functional and structural conditions of the pavement. The combined action of age, traffic, climate and environmental factors usually affects the surface course and causes functional deterioration of the pavement. This will adversely affect the riding quality as well as the vehicle operating cost. The present study aims in developing pavement performance prediction models for low-volume roads in Calicut district of Kerala state, India. The roads considered for the study have an age varying from 1 to 7 years. The data determining the present conditions of the pavement such as pavement distress data, roughness, skid resistance, texture depth, traffic data and geometric details were collected. Since the pavement condition also depends on the subgrade conditions, California Bearing Ratio (CBR) and maximum dry density of subgrade were also collected. The Pavement Condition Index (PCI) and International Roughness Index (IRI) were calculated from the distress data and roughness data, respectively. Three different models were developed to predict the PCI, IRI and Skid Number (SN) of the road sections. Multiple regression models developed correlates PCI, IRI and SN with different factors such as age, Average Daily Traffic (ADT), texture depth and CBR. The performance of each model developed was evaluated using selected performance criteria. The models so developed help the concerned authorities in making decisions on the maintenance strategies as well as the allocation of funds.