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Research Papers, Lincoln University

On 4 September 2010, a 7.1 magnitude earthquake struck near Darfield, 40 kilometres west of Christchurch, New Zealand. The quake caused significant damage to land and buildings nearby, with damage extending to Christchurch city. On 22 February 2011, a 6.3 magnitude earthquake struck Christchurch, causing extensive and significant damage across the city and with the loss of 185 lives. Years on from these events, occasional large aftershocks continue to shake the region. Two main entomological collections were situated within close proximity to the 2010/11 Canterbury earthquakes. The Lincoln University Entomology Research Collection, which is housed on the 5th floor of a 7 storey building, was 27.5 km from the 2010 Darfield earthquake epicentre. The Canterbury Museum Entomology Collection, which is housed in the basement of a multi-storeyed heritage building, was 10 km from the 2011 Christchurch earthquake epicentre. We discuss the impacts of the earthquakes on these collections, the causes of the damage to the specimens and facilities, and subsequent efforts that were made to prevent further damage in the event of future seismic events. We also discuss the wider need for preparedness against the risks posed by natural disasters and other catastrophic events.

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.