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Research papers, University of Canterbury Library

By closely examining the performance of a 22-storey steel framed building in Christchurch subject to various earthquakes over the past seven years, it is shown that a number of lessons can be learnt regarding the cost-effective consideration of non-structural elements. The first point in this work is that non-structural elements significantly affected the costs associated with repairing steel eccentrically braced frame (EBF) links. The decommissioning or rerouting of non-structural elements in the vicinity of damaged links in the case study building attributed to approximately half the total cost of their repair. Such costs could be significantly reduced if the original positioning of non-structural elements took account of the potential need to repair the EBF links. The second point highlighted is the role that pre-cast cladding apparently played on the distribution and type of damage in the building. Loss estimates obtained following the FEMA P-58 framework vary considerably when cladding is or isnt modelled, both because of changes to drift demands up the height of the building and because certain types of subsequent damage are likely to be cheaper to repair than others. Finally, costly repairs to non-structural partition walls were required not only after the moment magnitude 7.1 earthquake in 2010 but also in multiple aftershocks in the years that followed. Repair costs associated with aftershock events exceeded those from the main event, emphasizing the need to consider aftershocks within modern performance-based earthquake engineering and also the opportunity that exists to make more cost-effective repair strategies following damaging earthquakes.

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.