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Videos, UC QuakeStudies

A video of an interview with John Haynes, about his experiences during the 22 February 2011 earthquake. Haynes was in the Forsyth Barr building when the earthquake struck. Using his skills as trained mountain guide, Haynes belayed fourteen people down three and a half floors to safety.

Research papers, University of Canterbury Library

Existing unreinforced masonry (URM) buildings are often composed of traditional construction techniques, with poor connections between walls and diaphragms that results in poor performance when subjected to seismic actions. In these cases the application of the common equivalent static procedure is not applicable because it is not possible to assure “box like” behaviour of the structure. In such conditions the ultimate strength of the structure relies on the behaviour of the macro-elements that compose the deformation mechanisms of the whole structure. These macroelements are a single or combination of structural elements of the structure which are bonded one to each other. The Canterbury earthquake sequence was taken as a reference to estimate the most commonly occurring collapse mechanisms found in New Zealand URM buildings in order to define the most appropriate macroelements.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.

Audio, Radio New Zealand

Why is the Royal Commission into Building Collapses in the Christchurch Earthquake not apportioning blame? Christchurch lawyer Grant Cameron represented the victims of the Cave Creek Tragedy.

Research papers, University of Canterbury Library

Over 6.3 million waste tyres are produced annually in New Zealand (Tyrewise, 2021), leading to socioeconomic and environmental concerns. The 2010-11 Canterbury Earthquake Sequence inflicted extensive damage to ~6,000 residential buildings, highlighting the need to improve the seismic resilience of the residential housing sector. A cost-effective and sustainable eco-rubber geotechnical seismic isolation (ERGSI) foundation system for new low-rise buildings was developed by the authors. The ERGSI system integrates a horizontal geotechnical seismic isolation (GSI) layer i.e., a deformable seismic energy dissipative filter made of granulated tyre rubber (GTR) and gravel (G) – and a flexible rubberised concrete raft footing. Geotechnical experimental and numerical investigations demonstrated the effectiveness of the ERGSI system in reducing the seismic demand at the foundation level (i.e., reduced peak ground acceleration) (Hernandez et al., 2019; Tasalloti et al., 2021). However, it is essential to ensure that the ERGSI system has minimal leaching attributes and does not result in long-term negative impacts on the environment.

Images, UC QuakeStudies

Members of the USAID Disaster Assistance Response Team (DART) and the New Zealand Urban Search and Rescue, breaking through the floor of a building which was severely damaged during the 22 February 2011 earthquake.

Images, Alexander Turnbull Library

Two people stare at a demolition scene. The man thinks there must have been an earthquake but the woman advises him that it was the city council. Refers to plans to demolish three buildings in Wellington's Willis Street without public consultation. The buildings due for demolition are owned by Singaporean Grand Complex Properties, which plans eventually to build a multimillion-dollar high-rise on the site, reports stuff.co.nz. The Canterbury earthquake happened 4th September and as a result there has been a lot of discussion about the need to preserve historic buildings if at all possible. Quantity: 1 digital cartoon(s).