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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, University of Canterbury Library

Rising disaster losses, growth in global migration, migrant labour trends, and increasingly diverse populations have serious implications for disaster resilience around the world. These issues are of particular concern in New Zealand, which is highly exposed to disaster risk and has the highest proportion of migrant workers to national population in the OECD. Since there has been no research conducted into this issue in New Zealand to date, greater understanding of the social capital used by migrant workers in specific New Zealand contexts is needed to inform more targeted and inclusive disaster risk management approaches. A New Zealand case study is used to investigate the extent and types of social capital and levels of disaster risk awareness reported by members of three Filipino migrant workers organisations catering to dairy farm, construction and aged care workers in different urban and rural Canterbury districts. Findings from (3) semi-structured interviews and (3) focus groups include consistently high reliance on bonding capital and low levels of bridging capital across all three organisations and industry sectors, and in both urban and rural contexts. The transitory, precarious residential status conveyed by temporary work visas, and the difficulty of building bridging capital with host communities has contributed to this heavy reliance on bonding capital. Social media was essential to connect workers with family and friends in other countries, while Filipino migrant workers organisations provided members with valuable access to industry and district-specific networks of other Filipino migrant workers. Linking capital varied between the three organisations, with members of the organisation set up to advocate for dairy farm workers reporting the highest levels of linking capital. Factors influencing the capacity of workers organisations to develop linking capital appeared to include motivation (establishment objectives), length of time since establishment, support from government and industry groups, urban-rural context, income levels and gender. Although aware of publicity around earthquake and tsunami risk in the Canterbury region, participants were less aware of flood risk, and expressed fatalistic attitudes to disaster risk. Workers organisations offer a valuable potential interface between CDEM Group activities and migrant worker communities, since organisation leaders were interested in accessing government support to participate (with and on behalf of members) in disaster risk planning at district and regional level. With the potential to increase disaster resilience among these vulnerable, hard to reach communities, such participation could also help to build capacity across workers organisations (within Canterbury and across the country) to develop linking capital at national, as well as regional level. However, these links will also depend on greater government and industry commitment to providing more targeted and appropriate support for migrant workers, including consideration of the cultural qualifications of staff tasked with liaising with this community.