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WALL-CRACK

Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection.