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Unreinforced masonry buildings are vulnerable to earthquakes. This is a rising concern due to recent human-induced seismic activity in Groningen since the majority of buildings are made of unreinforced masonry and were not designed to withstand earthquakes. These buildings have structural weaknesses, making their assessment crucial for safety. The mentioned issue has prompted engineering companies in the region to research seismic assessments of existing structures. In order to quickly and accurately assess existing structures and identify progressive damage, state-of-the-art technologies, such as innovative sensors and deep learning techniques, can be used for crack detection on masonry surfaces. Hence, an experimental campaign took place at FieldLab BuildinG at Hanze University of Applied Sciences to assess the efficiency of an innovative crack sensor and vision-based crack detection on masonry structures. A total of two full-scale masonry walls were built and tested on the in-plane test setup with two different configurations, including two different surface properties. Four sensors were placed on each wall to monitor crack development in each direction. The sensors are based on wireless crack monitoring, which is very easy to place and generates data based on wall movement. Regarding vision-based crack detection, five different cameras were used to take pictures of the walls to have a wide range of different qualities of pictures from the wall with the purpose of validating the AI algorithm. The validated product of the experiment is also used to detect future cracks in the monument of Fraeylemaborg. Hence, the proposed project here is a complementary effort to enrich the validation of the sensor for structural health monitoring in a laboratory environment.
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.