Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.
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.
