| Pavement management systems (PMS) provide a methodology for synthesizing all the activities related to the pavement network. These include all the activities involved in the planning and programming, design, construction, maintenance, and rehabilitation (1).;The effective implementation and utilization of PMS requires a systematic approach involving condition assessment and performance modelling. A very important part of such approach is the use of pavement performance prediction models to predict the future condition of pavements based on present condition.;This study presents a methodology used to develop network level pavement performance prediction models for the pavement network in the state of Arkansas. Regression analysis is proposed to be employed to forecast the pavement condition. In this method pavements are grouped into families each possessing the same characteristics in terms of its surface type and age.;Four pavement families are identified for the model development: asphalt over asphalt and asphalt pavements, asphalt over concrete pavements, jointed reinforced concrete pavements (JRCP), and jointed plain concrete pavements (JPCP). Models for each family are developed for predicting average Rutting depth , average IRI (International Roughness Index), average Cracking Rating (Universal Cracking Index, "UCI"), and average Pavement Condition Index (PCI).;The successful implementation of PMS depends upon how well future pavement condition, as predicted by the prediction models, agrees with observed values of the road network. To further assess the models' accuracy, their predictions are compared against the corresponding observed values. |
