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Evaluation of thermography-based automated delamination and cavity detection in concrete bridges

: Merkle, Dominik; Reiterer, Alexander

Fulltext urn:nbn:de:0011-n-6364273 (18 MByte PDF)
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Created on: 6.7.2021

Beyerer, Jürgen (Hrsg.); Heizmann, Michael (Hrsg.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Automated Visual Inspection and Machine Vision IV : 21-26 June 2021, Online Only, Germany
Bellingham, WA: SPIE, 2021 (Proceedings of SPIE 11787)
Paper 1178706, 12 pp.
Conference "Automated Visual Inspection and Machine Vision" <4, 2021, Online>
Conference Paper, Electronic Publication
Fraunhofer IPM ()
thermography; infrared camera; bridge inspection; remote sensing; damage detection

The stability, durability, and safety of concrete bridges are not only endangered by visible damages on the surface such as cracks or spalling, but also internal damages such as cavities, delamination, or material changes due to carbonation or moisture penetration. In Germany, the inspection is regulated by the norm DIN 1076. Accordingly, the inspectors with an experienced hearing still interpret the sound of a hammer on the entire underside of the bridge. Therefore, to inspect the underside of a bridge with its underlying infrastructures like traffic, water, or railroads and its usually complex and difficult to reach structures, including girders, a fast and reliable remote sensing approach is required to increase both efficiency and level of automation. This paper summarizes recent work using both passive and active thermography for bridge inspection. We compare three different sensor systems including the Parrot Anafi Thermal, the FLIR Vue Pro R, and the FLIR SC640 which could be used in a UAV, UGV, or human based inspection. To test and verify our concepts, we apply passive thermography on real concrete bridges in Freiburg (Germany) and derive 2D and 3D data using a photogrammetric approach. We analyse spatial and thermal resolution dependent on camera selection, working distance, weather conditions, and the bridges' surface properties. For damage detection, we use high-pass or lowpass filtering dependent on the ambient temperature gradient. Moreover, we discuss further damage detection methods that could be integrated into the proposed processing workflow.