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.
Farming and urban regions are impacted by earthquake disasters in different ways, and feature a range of often different recovery requirements. In New Zealand, and elsewhere, most earthquake impact and recovery research is urban focused. This creates a research deficit that can lead to the application of well-researched urban recovery strategies in rural areas to suboptimal effect. To begin to reduce this deficit, in-depth case studies of the earthquake impacts and recovery of three New Zealand farms severely impacted by the 14th November 2016, M7.8 Hurunui-Kaikōura earthquake were conducted. The initial earthquake, its aftershocks and coseismic hazards (e.g., landslides, liquefaction, surface rupture) affected much of North Canterbury, Marlborough and the Wellington area. The three case study farms were chosen to broadly represent the main types of farming and topography in the Hurunui District in North Canterbury. The farms were directly and indirectly impacted by earthquakes and related hazards. On-farm infrastructure (e.g., woolsheds, homesteads) and essential services (e.g., water, power), frequently sourced from distributed networks, were severely impacted. The earthquake occurred after two years of regional drought had already stressed farm systems and farmers to restructuring or breaking point. Cascading interlinked hazards stemming from the earthquakes and coseismic hazards continued to disrupt earthquake recovery over a year after the initial earthquake. Semi-structured interviews with the farmers were conducted nine and fourteen months after the initial earthquake to capture the timeline of on-going impacts and recovery. Analysis of both geological hazard data and interview data resulted in the identification of key factors influencing farm level earthquake impact and recovery. These include pre-existing conditions (e.g., drought); farm-specific variations in recovery timelines; and resilience strategies for farm recovery resources. The earthquake recovery process presented all three farms with opportunities to change their business plans and adapt to mitigate on-going and future risk.
