Liquefaction-induced lateral spreading during earthquakes poses a significant hazard to the built environment, as observed in Christchurch during the 2010 to 2011 Canterbury Earthquake Sequence (CES). It is critical that geotechnical earthquake engineers are able to adequately predict both the spatial extent of lateral spreads and magnitudes of associated ground movements for design purposes. Published empirical and semi-empirical models for predicting lateral spread displacements have been shown to vary by a factor of <0.5 to >2 from those measured in parts of Christchurch during CES. Comprehensive post- CES lateral spreading studies have clearly indicated that the spatial distribution of the horizontal displacements and extent of lateral spreading along the Avon River in eastern Christchurch were strongly influenced by geologic, stratigraphic and topographic features.
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.
The lived reality of the 2010-2011 Canterbury earthquakes and its implications for the Waimakariri District, a small but rapidly growing district (third tier of government in New Zealand) north of Christchurch, can illustrate how community well-being, community resilience, and community capitals interrelate in practice generating paradoxical results out of what can otherwise be conceived as a textbook ‘best practice’ case of earthquake recovery. The Waimakariri District Council’s integrated community based recovery framework designed and implemented post-earthquakes in the District was built upon strong political, social, and moral capital elements such as: inter-institutional integration and communication, participation, local knowledge, and social justice. This approach enabled very positive community outputs such as artistic community interventions of the urban environment and communal food forests amongst others. Yet, interests responding to broader economic and political processes (continuous central government interventions, insurance and reinsurance processes, changing socio-cultural patterns) produced a significant loss of community capitals (E.g.: social fragmentation, participation exhaustion, economic leakage, etc.) which simultaneously, despite local Council and community efforts, hindered community well-being in the long term. The story of the Waimakariri District helps understand how resilience governance operates in practice where multi-scalar, non-linear, paradoxical, dynamic, and uncertain outcomes appear to be the norm that underpins the construction of equitable, transformative, and sustainable pathways towards the future.
