620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
In future, additional and more detailed data are needed about the current conditions of bridges for preventive maintenance management. Monitoring procedures are not merely able to provide key performance indicators for a specific point in time, but also over a period. These KPIs must be selected in such a way as to permit substantiated statements about the present and future condition of bridges. For this reason, greater efforts must be made to define the significant KPIs for the various types of bridges, and show how these figures can be reliably determined. Both the COST Action TU1402, and TU1406 offer important approaches which, properly combined, can deliver substantial added value to the calculation and description of the condition of bridges in the interest of proactive maintenance management.
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.