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In the coming years, the city of Essen will rely on artificial intelligence (AI) for the damage assessment of roads. Named TWIN4ROAD, the Office for Geoinformation, Surveying and Cadastre together with Point Cloud Technology, HPI Potsdam and Straßen.NRW launched a three-year research project.
The German road network has a length of over 830,000 kilometres. The management and maintenance of the road network pose fundamental challenges to the economy, the state and society. The collection of area-wide and constantly updated data on road condition and road space inventory as well as their evaluations and change analyses is the central problem of road authorities due to the enormous amount of data and the lack of procedures for automated evaluation. These data enable the creation of digital twins for reliable and data-oriented decision-making as basis for road maintenance and investment planning as well as infrastructure monitoring.
In fact, however, municipal roads and especially the roads in Essen are a network that has grown over 100 years, with different ages, conditions and road structures that have been disturbed in different ways e.g. by roadsworks for supply lines. Predicting where damage will most likely occur in the near future, for example, is much more complex under these conditions than on kilometres of homogeneously asphalted motorway sections. Moreover, a comprehensive inventory of road infrastructure in all material respects (i.e. a digital twin) is still…
With the help of ground penetrating radar (GPR) technology, 3D data and image recordings, an extensive database on road infrastructure and the assessment of the road condition has to be set up. For this purpose, a digital twin of Essen's road network is created using car-based mobile mapping and data collection. This includes mobile laserscanning measurements, extensive acquisition of road space imagery and continuous GPR-measurements. The latter ones will be enriched by analysis of drilling cores displaying the road’s material substruction, it’s chemistry and regularities. By using this data, with the aid of AI it is envisaged to be able to derive robust and stable assessments on road damages, needs for renovation and potential future potholes solely by measurements and data analysis. Furthermore, the digital twin provides an inventory which can be accessed in context of most other sorts of municipal tasks in the public road space. The amount of subsequent analysis is potentially manifold.
