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Images, UC QuakeStudies

The new staff room at Avonside Girls High School with the new classrooms in the background. The photograph has been captioned by BeckerFraserPhotos, "Avonside Girls High School staff room. We spoke to two Science teachers who talked enthusiastically about their new Science labs and how great it was to return to their own campus again".

Research Papers, Lincoln University

Tree mortality is a fundamental process governing forest dynamics, but understanding tree mortality patterns is challenging because large, long-term datasets are required. Describing size-specific mortality patterns can be especially difficult, due to few trees in larger size classes. We used permanent plot data from Nothofagus solandri var. cliffortioides (mountain beech) forest on the eastern slopes of the Southern Alps, New Zealand, where the fates of trees on 250 plots of 0.04 ha were followed, to examine: (1) patterns of size-specific mortality over three consecutive periods spanning 30 years, each characterised by different disturbance, and (2) the strength and direction of neighbourhood crowding effects on sizespecific mortality rates. We found that the size-specific mortality function was U-shaped over the 30-year period as well as within two shorter periods characterised by small-scale pinhole beetle and windthrow disturbance. During a third period, characterised by earthquake disturbance, tree mortality was less size dependent. Small trees (,20 cm in diameter) were more likely to die, in all three periods, if surrounded by a high basal area of larger neighbours, suggesting that sizeasymmetric competition for light was a major cause of mortality. In contrast, large trees ($20 cm in diameter) were more likely to die in the first period if they had few neighbours, indicating that positive crowding effects were sometimes important for survival of large trees. Overall our results suggest that temporal variability in size-specific mortality patterns, and positive interactions between large trees, may sometimes need to be incorporated into models of forest dynamics.

Research papers, The University of Auckland Library

Mechanistic and scientific approaches to resilience assume that there is a “tipping point” at which a system can no longer absorb adversity; after this point, it is liable to collapse. Some of these perspectives, particularly those stemming from ecology and psychology, recognise that individuals and communities cannot be perpetually resilient without limits. While the resilience paradigm has been imported into the social sciences, the limits to resilience have often been disregarded. This leads to an overestimation of “human resourcefulness” within the resilience paradigm. In policy discourse, practice, and research, resilience seems to be treated as a “limitless” and human quality in which individuals and communities can effectively cope with any hazard at any time, for as long as they want and with any people. We critique these assumptions with reference to the recovery case in Ōtautahi Christchurch, Aotearoa New Zealand following the 2010-11 Canterbury earthquake sequence. We discuss the limits to resilience and reconceptualise resilience thinking for disaster risk reduction and sustainable recovery and development.

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.