Prognosmodell för spårutveckling orsakad av tung trafik: delmoment för nybyggnation
2007 (Swedish)Report (Other academic)
VTI has, on commission by the Swedish Road Administration, monitored road data from 66 sites and more than 600 test sections since 1987 and the monitoring is still in progress. All data is stored in a Microsoft Access LTPP (Long Time Pavement Performance) data base. Some of this data has been used in this project with the aim to develop a model to predict rut depth caused by heavy traffic. This project covers only the development of rut depth on new constructed roads. The next phase is to develop a similar model for predicting rut depth after a maintenance measure. The basic data which has been used in the development of the model is response data from Falling Weight Deflection (FWD), rut depth data from a laser equipped high speed measuring vehicle developed at VTI and traffic data describing the amount and configuration of the heavy traffic. The required input data for developing the model was: - The date of the opening of the road for traffic - FWD data one year after the opening of the road - Rut depth data collected every year - The equivalent number of standard axles passed on the roads co-ordinated with the monitoring of the rut depth. In the data base there is data from 66 sites with different types of pavement structures. For the purpose of this project data from 15 sites with 121 sections with the length of 100 metres were used. During the model development work the most significant factor of describing the structural capacity was the Surface Curvature Index (SCI300) which is the difference in deflection between the centre deflection and the deflection at the distance of 300 mm from the centre of the loading plate in the FWD testing.
Place, publisher, year, edition, pages
Linköping: VTI., VTI notat 2-2007 , 2007. , 36 p.
Rutting, Depth, Lorry, Prediction, Mathematical model, Input data, Bearing capacity, Axle load, Flexible pavement
Research subject Road: Highway design, Road: Surfacing
IdentifiersURN: urn:nbn:se:vti:diva-1634OAI: oai:DiVA.org:vti-1634DiVA: diva2:670346