Ingår i: EPAM 2012: Malmö, Sweden, 5–7 September: 4th European pavement and asset management conference

A Nordic research project has developed a set of performance models for prediction of rutting, roughness, cracking and bearing capacity of flexible pavements at network level. Fully implemented in Pavement Management Systems it can contribute to a better platform for decision-making when the maintenance budget is allocated to specific projects. A considerable strength of the new system of models is that it bases predictions on thorough information on pavement condition and construction. The precondition for the model system was that data should be acquired by modern traffic speed survey techniques. The reason for this being efficiency and traffic safety. The required input data; rutting, roughness, amount of cracking, centre deflection and asphalt layer thickness can all be registered by condition surveys performed at traffic speed and as such fulfil the requirements. Among a choice of model systems, the well-known and respected HDM-4 model was selected as a basis for the new suggested Nordic model. The HDM-4 model has been calibrated to Nordic (mainly Swedish) climate, pavement materials and construction traditions using historical data sets. As a result the system of models provides predictions of the selected pavement parameters, showing good consistency with measured values. This paper will shortly describe the model selection and calibration, and show examples of predictions. Furthermore, we will illustrate the use of the computer software, which makes implementation in existing Pavement Management Systems a fairly easy task. The research project was financed and executed under the NordFoU umbrella, which is a pooled fund scheme for research projects seen as important to the Nordic National Road Authorities. “NordFoU Pavement Performance Models” was funded and executed by Norway, Sweden, Iceland and Denmark. Parallel to developing a model for network level, the project also looked at models for project level performance prediction.