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.
Our poster will present on-going QuakeCoRE-founded work on strong motion seismology for Dunedin-Mosgiel area, focusing on ground motion simulations for Dunedin Central Business District (CBD). Source modelling and ground motion simulations are being carried out using the SCEC (Southern California Earthquakes Center) Broad Band simulation Platform (BBP). The platform computes broadband (0-10 Hz) seismograms for earthquakes and was first implemented at the University of Otago in 2016. As large earthquakes has not been experienced in Dunedin in the time of period of instrumental recording, user-specified scenario simulations are of great value. The Akatore Fault, the most active fault in Otago and closest major fault to Dunedin, is the source focused on in the present study. Simulations for various Akatore Fault source scenarios are run and presented. Path and site effects are key components considered in the simulation process. A 1D shear wave velocity profile is required by SCEC BBP, and this is being generated to represent the Akatore-to-CBD path and site within the BBP. A 3D shear velocity model, with high resolution within Dunedin CBD, is being developed in parallel with this study (see Sangster et al. poster). This model will be the basis for developing a 3D shear wave velocity model for greater Dunedin-Mosgiel area for future ground motion simulations, using Canterbury software (currently under development).
This study is a qualitative investigation into the decision-making behaviour of commercial property owners (investors and developers) who are rebuilding in a city centre after a major disaster. In 2010/2011, Christchurch, the largest city in the South Island of New Zealand, was a site of numerous earthquakes. The stronger earthquakes destroyed many buildings and public infrastructure in the commercial inner city. As a result, affected property owners lost all or most of their buildings, a significant proportion of which were old and in the last phase of their life span. They had to negotiate pay-outs with insurance companies and decide, once paid out, whether they should rebuild in Christchurch or sell up and invest elsewhere. The clear majority of those who decided to reinvest in and rebuild the city are ‘locals’, almost all of whom had no prior experience of property development. Thus, in a post-disaster environment, most of these property owners have transitioned from being just being passive investors to active property developers. Their experience was interpreted using primary data gathered from in-depth and semi-structured interviews with twenty-one “informed property people” who included commercial property owners; property agents or consultants; representatives of public-sector agencies and financial institutions. The study findings showed that the decision-making behaviour of property investors and developers rebuilding after a major disaster did not necessarily follow a strict financial or profit motive as prescribed in the mainstream or neo-classical economics property literature. Rather, their decision-making behaviour has been largely shaped by emotional connections and external factors associated with their immediate environment. The theoretical proposition emerging from this study is that after a major disaster, local urban property owners are faced with two choices “to stay” or “to go”. Those who decide to stay and rebuild are typically very committed individuals who have a feeling of ownership, belonging and attachment to the city in which they live and work. These are people who will often take the lead in commercial property development, proactively making decisions and seeking positive investment outcomes for themselves which in turn result in revitalised commercial urban precincts.
