Abstract
The greatest challenges for the maintenance of road infrastructure are the planning and implementation of new construction and maintenance measures in line with requirements and the effective use of financial and material resources. This requires the most accurate possible knowledge of the condition of all layers of the road structure. As part of the mFUND project "Data-based assessment of the resilience of municipal road infrastructure - DaRkSeit" funded by the German Federal Ministry for Digital and Transport, Weigh-in-motion and temperature sensors were installed in a major municipal road for the first time in order to be able to calculate the damage and thus the condition development of the asphalt layers as accurately as possible.
Up to six temperature sensors were installed at different depths of the asphalt road in the various urban climate zones of the city of Münster. Their measurement data serve as support points for calculating the vertical and temporal temperature curves and thus the material conditions. When a vehicle passes over the Weigh-in-motion sensors, the load applied to the road surface can then be combined with the material conditions existing at that time and the resulting damage can be calculated. This damage in the asphalt layers is accumulated and then forms the basis for the condition and remaining service life prediction.
The results to date have already provided important insights into the difference in the increase in fatigue of the asphalt layers on predominantly shaded and predominantly sunlit road sections. This difference amounted to around 25 % for the year 2023 and almost 100 % at the maximum, within one summer week. This clearly shows the influence of the energy absorption property and thus the selection of the asphalt surface course material on the durability of the entire road.
With the help of these and other findings and the results from other similar measuring stations, the adjustment factors in the German regulations are to be adapted in the future after the occurrence of real damage in the respective roads, thus improving the quality of the forecast. In addition, it is planned to use the results for the search for more cost-effective methods for determining traffic load and temperature conditions in order to be able to apply the condition forecast to roads where no complex sensor technology can be installed. The aim is to extend the procedure to the entire road network inexpensive and effectively in the future.