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Research papers, University of Canterbury Library

Abstract This study provides a simplified methodology for pre-event data collection to support a faster and more accurate seismic loss estimation. Existing pre-event data collection frameworks are reviewed. Data gathered after the Canterbury earthquake sequences are analysed to evaluate the relative importance of different sources of building damage. Conclusions drawns are used to explore new approaches to conduct pre-event building assessment.

Research papers, University of Canterbury Library

On 15 August 1868, a great earthquake struck off the coast of the Chile-Peru border generating a tsunami that travelled across the Pacific. Wharekauri-Rekohu-Chatham Islands, located 800 km east of Christchurch, Aotearoa-New Zealand (A-NZ) was one of the worst affected locations in A-NZ. Tsunami waves, including three over 6 metres high, injured and killed people, destroyed buildings and infrastructure, and impacted the environment, economy and communities. While experience of disasters, and advancements in disaster risk reduction systems and technology have all significantly advanced A-NZ’s capacity to be ready for and respond to future earthquakes and tsunami, social memory of this event and other tsunamis during our history has diminished. In 2018, a team of scientists, emergency managers and communication specialists collaborated to organise a memorial event on the Chatham Islands and co-ordinate a multi-agency media campaign to commemorate the 150th anniversary of the 1868 Arica tsunami. The purpose was to raise awareness of the disaster and to encourage preparedness for future tsunami. Press releases and science stories were distributed widely by different media outlets and many attended the memorial event indicating public interest for commemorating historical disasters. We highlight the importance of commemorating disaster anniversaries through memorial events, to raise awareness of historical disasters and increase community preparedness for future events – “lest we forget and let us learn.”

Research papers, University of Canterbury Library

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.