The German highway network hast o face new challenges in the near future, e.g. increasing traffic density and loads, climate change effects and new quality requirements regarding sustainability. It is necessary to come up with foresighted concepts in the present to be prepared for these challenges. Therefore it is important to adapt and enhance innovative attempts, which take changing impacts into account. One goal of these efforts is the development of adaptive systems for the provision of information and a holistic evaluation in real time. The paper describes the recent research and developments on a system for information and holistic evaluation in real time, taking into account sensor networks, evaluation procedures and their implementation in existing maintenance and inspection strategies.
The German highway network is facing new challenges in the near future. The structures have to deal with increasing traffic loads, climate change effects and new requirements regarding sustainability while they are getting older and budget cuts can be expected. To guarantee a reliable highway network, it will be vital to adapt and enhance innovative approaches. Current bridge management relies on the results of conventional bridge inspections and thus has certain limitations when it comes to insufficient load bearing capacity and other systematic weaknesses. Therefore, new approaches for real time condition assessment of critical road infrastructure elements are to be developed.
In Germany, expenditure for the construction of new and maintenance of existing federal highways is currently at a record level of EUR 8 billion per year. In connection with the planned infrastructure policy reforms it is necessary to further develop the planning tools for dimensioning and substance assessment of road structures in order to increase the efficiency of construction measures. The stress caused by traffic is of central importance here. Since unevenness in the road surface has a significant influence on the dynamic part of the wheel load, dynamic effects must be explicitly taken into account. As a result, increasing unevenness can lead to higher dynamic loads and, in the context of a corresponding number of wheel rollovers, to disproportionate damage to the road structure. In general, a shock factor is taken into account during dimensioning, which is to be considered as a function of vehicle suspension, load, speed and evenness. This approach is not sufficient for concrete road structures executed as slabs. In the normal case, only the periodically occurring individual event of a transverse contraction joint, superimposed by irreversible and/or temporary slab deformations, can lead to a significant increase in the dynamic wheel load. In addition, the existing slab deformations are tied to many boundary conditions and can therefore vary greatly in their characteristics. For the further development of methods for dimensioning and residual substance assessment with regard to their accuracy, a three-dimensional slab-specific view of the road surface is therefore appropriate. In this paper, a suitable measuring method for three-dimensional surface laser scanning and an algorithm for the classification of slab deformations are presented.
Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.
The Federal Highway Network of Germany represents considerable fixed assets. Construction and maintenance activities not only require a high budget, but also influence the traffic infrastructure and, thus, the economy and society as a whole. The present safety of the network has to be ensured under consideration of environmental aspects. At the same time the network owner has to make sure that the civil works are carried out in the most efficient way. Considering the fact that financial resources are restricted, the costs have to be spent in a way to obtain the greatest possible benefit. This task is supported by the application of a comprehensive Asset Management, which is subdivided into operational and controlling tasks respectively. The paper describes the current management procedures.