Detection of Anomaly in a Pretensioned Bolted Beam-To-Column Connection Node Using Digital Image Correlation and Neural Networks

Abstract

Bolted connections, commonly applied in civil engineering structures, have many advantages. According to current trends, bolted connections in steel structures are designed as prestressed ones. Unfortunately, precise control of the prestressing forces is difficult, while the loosening (due to, e.g., dynamic interactions) may be dangerous for the entire structure. There are many control methods applied in the determination of the tightening level, among which are vision-based methods. The methods described so far enable—thanks to image processing—damage detection in connections with visible connectors. The level of the considered loosening was significant—in many cases, changes in connectors were visible with the naked eye, whereas the procedure presented here enables the detection of very small changes, impossible to detect without manual inspection of every single connector. It is not necessary to observe the connectors directly, but the near surrounding of the node should be visible. As a measurement technique, Digital Image Correlation (DIC) was used. The applied measurement method and the high sensitivity of the presented procedure makes the presented research original. The currently presented procedure, employing Artificial Neural Networks, based on the laboratory examination of an example of one selected beam-to-column connection of a two-story steel portal frame, was perfect in the detection of a change and in the determination of the number of loosened rows, 95%, and their location, 94%, with the number of false alarms below 1%.