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Research papers, The University of Auckland Library

During the 2010/2011 Canterbury earthquakes, Reinforced Concrete Frame with Masonry Infill (RCFMI) buildings were subjected to significant lateral loads. A survey conducted by Christchurch City Council (CCC) and the Canterbury Earthquake Recovery Authority (CERA) documented 10,777 damaged buildings, which included building characteristics (building address, the number of storeys, the year of construction, and building use) and post-earthquake damage observations (building safety information, observed damage, level of damage, and current state of the buildings). This data was merged into the Canterbury Earthquake Building Assessment (CEBA) database and was utilised to generate empirical fragility curves using the lognormal distribution method. The proposed fragility curves were expected to provide a reliable estimation of the mean vulnerability for commercial RCFMI buildings in the region. http://www.13thcms.com/wp-content/uploads/2017/05/Symposium-Info-and-Presentation-Schedule.pdf VoR - Version of Record

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.