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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.

Research Papers, Lincoln University

Oarai is a coastal town in Ibaraki Prefecture, Japan, affected by the Great East Japan Earthquake in 2011. The disaster severely damaged local industries, and the local tourism sector faced a sharp decline followed the event. To overcome the conundrum, the local tourism businesses have taken the opportunity to collaborate with an anime called Girls und Panzer, which has been developed by an external animation production studio. This collaboration has resulted in huge success, and the drop in the local tourism industry had been largely reversed, but has resulted in a significant change to the tourism system. This thesis explores the activities and outcomes of this tourism industry reimagining. A mixed-method approach was used to investigate the perception of local tourism businesses to the current Oarai tourism system, and examine the transformative effect of the disaster and its aftermath. Perceptions of disaster impact and anime tourism development were analysed through surveys (n=73) and interviews (n=2) which focused on tourism business operators, while participant observation was conducted to create the image of anime tourism operation in Oarai. Results show that the development of anime tourism in Oarai successfully helped the local tourism businesses to recover from the disaster. As new agencies and organisations joined the anime tourism network, anime tourism increased communication between stakeholders, and improved the resilience of the community. The new tourism development has transformed the local tourism industry, to some extent, however. the future trajectory of anime tourism in Oarai is difficult to forecast, and there is scope for longitudinal research of this tourism system.