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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.

Articles, UC QuakeStudies

A PDF copy of the FESTA 2012 programme. The programme includes a site map of FESTA events and projects, and key information about each one. It is designed to fold out into a poster.

Research papers, University of Canterbury Library

This study explicitly investigates uncertainties in physics-based ground motion simulation validation for earthquakes in the Canterbury region. The simulations utilise the Graves and Pitarka (2015) hybrid methodology, with separately quantified parametric uncertainties in the comprehensive physics and simplified physics components of the model. The study is limited to the simulation of 148 small magnitude (Mw 3.5 – 5) earthquakes, with a point source approximation for the source rupture representations, which also enables a focus on a small number of relevant uncertainties. The parametric uncertainties under consideration were selected through sensitivity analysis, and specifically include: magnitude, Brune stress parameter and high frequency rupture velocity. Twenty Monte Carlo realisations were used to sample parameter uncertainties for each of the 148 events. Residuals associated with the following intensity measures: spectral acceleration, peak ground velocity, arias intensity and significant duration, were ascertained. Using these residuals, validation was performed through assessment of systematic biases in site and source terms from mixed-effects regression. Based on the results to date, initial standard deviation recommendations for parameter uncertainties, based on the Canterbury simulations have been obtained. This work ultimately provides an initial step toward explicit incorporation of modelling uncertainty in simulated ground motion predictions for future events, which will improve the use of simulation models in seismic hazard analysis. We plan to subsequently assess uncertainties for larger magnitude events with more complex ruptures, and events across a larger geographic region, as well as uncertainties due to path attenuation, site effects, and more general model epistemic uncertainties.

Research papers, University of Canterbury Library

Research indicates that aside from the disaster itself, the next major source of adverse outcomes during such events, is from errors by either the response leader or organisation. Yet, despite their frequency, challenge, complexity, and the risks involved; situations of extreme context remain one of the least researched areas in the leadership field. This is perhaps surprising. In the 2010 and 2011 (Christchurch) earthquakes alone, 185 people died and rebuild costs are estimated to have been $40b. Add to this the damage and losses annually around the globe arising from natural disasters, major business catastrophes, and military conflict; there is certainly a lot at stake (lives, way of life, and our well-being). While over the years, much has been written on leadership, there is a much smaller subset of articles on leadership in extreme contexts, with the majority of these focusing on the event rather than leadership itself. Where leadership has been the focus, the spotlight has shone on the actions and capabilities of one person - the leader. Leadership, however, is not simply one person, it is a chain or network of people, delivering outcomes with the support of others, guided by a governance structure, contextualised by the environment, and operating on a continuum across time (before, during, and after an event). This particular research is intended to examine the following: • What are the leadership capabilities and systems necessary to deliver more successful outcomes during situations of extreme context; • How does leadership in these circumstances differ from leadership during business as usual conditions; • Lastly, through effective leadership, can we leverage these unfortunate events to thrive, rather than merely survive?