Tsunami events including the 2004 Indian Ocean Tsunami and the 2011 Tohoku Earthquake and Tsunami confirmed the need for Pacific-wide comprehensive risk mitigation and effective tsunami evacuation planning. New Zealand is highly exposed to tsunamis and continues to invest in tsunami risk awareness, readiness and response across the emergency management and science sectors. Evacuation is a vital risk reduction strategy for preventing tsunami casualties. Understanding how people respond to warnings and natural cues is an important element to improving evacuation modelling techniques. The relative rarity of tsunami events locally in Canterbury and also globally, means there is limited knowledge on tsunami evacuation behaviour, and tsunami evacuation planning has been largely informed by hurricane evacuations. This research aims to address this gap by analysing evacuation behaviour and movements of Kaikōura and Southshore/New Brighton (coastal suburb of Christchurch) residents following the 2016 Kaikōura earthquake. Stage 1 of the research is engaging with both these communities and relevant hazard management agencies, using a survey and community workshops to understand real-event evacuation behaviour during the 2016 Kaikōura earthquake and subsequent tsunami evacuations. The second stage is using the findings from stage 1 to inform an agent-based tsunami evacuation model, which is an approach that simulates of the movement of people during an evacuation response. This method improves on other evacuation modelling approaches to estimate evacuation times due to better representation of local population characteristics. The information provided by the communities will inform rules and interactions such as traffic congestion, evacuation delay times and routes taken to develop realistic tsunami evacuation models. This will allow emergency managers to more effectively prepare communities for future tsunami events, and will highlight recommended actions to increase the safety and efficiency of future tsunami evacuations.
A natural disaster will inevitably strike New Zealand in the coming years, damaging educational facilities. Delays in building quality replacement facilities will lead to short-term disruption of education, risking long-term inequalities for the affected students. The Christchurch earthquake demonstrated the issues arising from a lack of school planning and support. This research proposes a system that can effectively provide rapid, prefabricated, primary schools in post-disaster environments. The aim is to continue education for children in the short term, while using construction that is suitable until the total replacement of the given school is completed. The expandable prefabricated architecture meets the strength, time, and transport requirements to deliver a robust, rapid relief temporary construction. It is also adaptable to any area within New Zealand. This design solution supports personal well-being and mitigates the risk of educational gaps, PTSD linked with anxiety and depression, and many other mental health disorders that can impact students and teachers after a natural disaster.
Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
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. However, poor performance was reported in terms of their seismic resilience. Although numerous innovative methods to mitigate damage have been introduced to the New Zealand community in order to improve wooden house performance, these retrofit options have not been readily taken up. The low number of retrofitted wooden-framed houses leads to questions about whether homeowners are aware of the necessity of seismic retrofitting their houses to achieve a satisfactory seismic performance. This study aims to explore different retrofit technologies that can be applied to wooden-framed houses in Wellington, taking into account the need of homeowners to understand the risk, likelihood and extent of damage expected after an event. A survey will be conducted in Wellington about perceptions of homeowners towards the expected performance of their wooden-framed houses. The survey questions were designed to gain an understanding of homeowners' levels of safety and awareness of possible damage after a seismic event. Afterwards, a structural review of a sample of the houses will be undertaken to identify common features and detail potential seismic concerns. The findings will break down barriers to making improvements in the performance of wooden-framed houses and lead to enhancements in the confidence of homeowners in the event of future seismic activity. This will result in increased understanding and contribute towards an accessible knowledge base, which will possibly increase significantly the use of these technologies and avoid unnecessary economic and social costs after a seismic event.
