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Research papers, University of Canterbury Library

New Zealand has a long tradition of using light timber frame for construction of its domestic dwellings. After the most recent earthquakes (e.g. Canterbury earthquakes sequence), wooden residential houses showed satisfactory life safety performance which aligns with New Zealand design codes requirements. However, poor performance was reported in terms of their seismic resilience that can be generally associated with community demands. Future expectations of the seismic performance of wooden-framed houses by homeowners were assessed in this research. Homeowners in the Wellington region were asked in a survey about the levels of safety and expected possible damage in their houses after a seismic event. Findings bring questions about whether New Zealand code requirements are good enough to satisfy community demands. Also, questions whether available information of strengthening techniques to structurally prepare wooden-framed houses to face future major earthquakes can help to make homeowners feel safer at home during major seismic events.

Research papers, University of Canterbury Library

After a high-intensity seismic event, inspections of structural damages need to be carried out as soon as possible in order to optimize the emergency management, as well as improving the recovery time. In the current practice, damage inspections are performed by an experienced engineer, who physically inspect the structures. This way of doing not only requires a significant amount of time and high skilled human resources, but also raises the concern about the inspector’s safety. A promising alternative is represented using new technologies, such as drones and artificial intelligence, which can perform part of the damage classification task. In fact, drones can safely access high hazard components of the structures: for instance, bridge piers or abutments, and perform the reconnaissance by using highresolution cameras. Furthermore, images can be automatically processed by machine learning algorithms, and damages detected. In this paper, the possibility of applying such technologies for inspecting New Zealand bridges is explored. Firstly, a machine-learning model for damage detection by performing image analysis is presented. Specifically, the algorithm was trained to recognize cracks in concrete members. A sensitivity analysis was carried out to evaluate the algorithm accuracy by using database images. Depending on the confidence level desired,i.e. by allowing a manual classification where the alghortim confidence is below a specific tolerance, the accuracy was found reaching up to 84.7%. In the second part, the model is applied to detect the damage observed on the Anzac Bridge (GPS coordinates -43.500865, 172.701138) in Christchurch by performing a drone reconnaissance. Reults show that the accuracy of the damage detection was equal to 88% and 63% for cracking and spalling, respectively.