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Research papers, University of Canterbury Library

Natural hazard reviews reveal increases in disaster impacts nowhere more pronounced than in coastal settlements. Despite efforts to enhance hazard resilience, the common trend remains to keep producing disaster prone places. This paper explicitly explores hazard versus multi-hazard concepts to illustrate how different conceptualizations can enhance or reduce settlement resilience. Understandings gained were combined with onthe-ground lessons from earthquake and flooding experiences to develop of a novel ‘first cut’ approach for analyzing key multi-hazard interconnections, and to evaluate resilience enhancing opportunities. Traditional disaster resilience efforts often consider different hazard types discretely. However, recent events in Christchurch, a New Zealand city that is part of the 100 Resilient Cities network, highlight the need to analyze the interrelated nature of different hazards, especially for enhancing lifelines system resilience. Our overview of the Christchurch case study demonstrates that seismic, hydrological, shallow-earth, and coastal hazards can be fundamentally interconnected, with catastrophic results where such interconnections go unrecognized. In response, we have begun to develop a simple approach for use by different stakeholders to support resilience planning, pre and post disaster, by: drawing attention to natural and built environment multi-hazard links in general; illustrating a ‘first cut’ tool for uncovering earthquake-flooding multi-hazard links in particular; and providing a basis for reviewing resilience strategy effectiveness in multi-hazard prone environments. This framework has particular application to tectonically active areas exposed to climate-change issues.

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.