Computer Tomography has been used for many years in construction materials technology in order to gain knowledge, using small format samples, of the joint structure of construction materials and how they change under different conditions of exposure. However, there is lack of appropriately configured equipment that would enable by means of X-ray technology a better understanding of bearing and deformation behaviour of entire building parts. Hence, in line with the DFG offer to tender, a completely new type of Tomography Portal (TOP) has been conceived; one that can scan building components of real life dimensions under increasing load with high resolution accuracy. It makes use of a high radiation energy, which is indispensable in order for example to illuminate bar-like reinforced concrete components using cross-section dimensions of at least 30 x 30 cm, and imaging of cracks of 0.1 mm width. In addition, a second radiating source has been integrated, which in the case of smaller dimensions or less demanding materials permits higher resolutions. The design has been understood as an interdisciplinary work. The guiding factors in the design were: the needs of the construction industry, machine, X-ray and measuring technology, calibration procedure, mathematical reconstruction of raw data, with their visual preparation and further processing on high performance computers as well as the availability of the research results.

Project partner:

Professor Dr.-Ing. Jürgen Schnell

Professor Dr.-Ing. Manfred Curbach

Professor Dr.-Ing. Josef Hegger

Professorin Dr. Heike Leitte

Professor Dr.-Ing. Harald S. Müller

Professor Dr.-Ing. Ralf Müller

Professor Dr.-Ing. Matthias Pahn

Professor Dr.-Ing. Jörg Seewig

Professor Dr.-Ing. Christos Vrettos

The aim of the research project is to develop methods for the detection of anomalies in large image data. This could be e.g. micro cracks in concrete beams, material densification in textile web goods or local fiber misalignments in components made of fiber-reinforced plastic. For this purpose, methods of machine learning, modeling of structures and imaging as well as statistical methods for the detection of abnormalities can be combined. In all this cases mentioned, the structure varies greatly. Methods and measures must therefore be developed in order to decide objectively, robustly and repeatably what an anomaly, i.e. a significant deviation is. The anomalies to be detected rarely occur both statistically and spatially. For this reason, there is a lack of training data for purely machine learning (ML) based solutions, which annotation is also very difficult. One way out is training based on realistic, simulated data, since in this case enough data can be generated and the basic truth is immediately available. An alternative approach is to adapt statistical methods for structure break detection in time series for the detection of spatial anomalies. The spatial component of the image data gives rise to new tasks, such as recognizing complex anomaly regions, which will also be solved in the project. The planned research work focuses on the detection of cracks in concrete components, since this question is of great practical relevance, but also particularly difficult in a methodological sense. ML and statistical methods for solving this specific problem are expected to be transferable to the other use cases mentioned above. Models for concrete and crack structures, on the other hand, are problem-specific, but are already a major challenge in this thematic limitation. 

Project partner:

Universität Ulm

Otto von Guericke-Universität Magdeburg

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.

BORAPA Ingenieurgesellschaft mbH

MKT Metall-Kunststoff-Technik GmbH & Co KG

C-Con GmbH & Co KG

Dr. Ketterer Ingenieurgesellschaft mbH

Heinze CobiaxDeutschland GmbH

Bekaert GmbH