The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
Designing a structure for higher- than-code seismic performance can result in significant economic and environmental benefits. This higher performance can be achieved using the principles of Performance-Based Design, in which engineers design structures to minimize the probabilistic lifecycle seismic impacts on a building. Although the concept of Performance-Based Design is not particularly new, the initial capital costs associated with designing structures for higher performance have historically hindered the widespread adoption of performance-based design practices. To overcome this roadblock, this research is focused on providing policy makers and stakeholders with evidence-based environmental incentives for designing structures in New Zealand for higher seismic performance. In the first phase of the research, the environmental impacts of demolitions in Christchurch following the Canterbury Earthquakes were quantified to demonstrate the environmental consequences of demolitions following seismic events. That is the focus here. A building data set consisting of 142 concrete buildings that were demolished following the earthquake was used to quantify the environmental impacts of the demolitions in terms of the embodied carbon and energy in the building materials. A reduced set of buildings was used to develop a material takeoff model to estimate material quantities in the entire building set, and a lifecycle assessment tool was used to calculate the embodied carbon and energy in the materials. The results revealed staggering impacts in terms of the embodied carbon and energy in the materials in the demolished buildings. Ongoing work is focused developing an environmental impact framework that incorporates all the complex factors (e.g. construction methodologies, repair methodologies (if applicable), demolition methodologies (if applicable), and waste management) that contribute to the environmental impacts of building repair and demolition following earthquakes.
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.
