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Images, UC QuakeStudies

A photograph of a crowd watching Struan Ashby from Tape Art NZ create the 'Dream Machine'. The 'Dream Machine' was a 9-day long creative project that used dream stories from the audience to turn a shipping container into a 40-foot mural. The photograph was taken at the 2014 SCIRT World Buskers Festival in Hagley Park.

Images, UC QuakeStudies

A photograph of Struan Ashby from Tape Art NZ creating the 'Dream Machine'. The 'Dream Machine' was a 9-day long creative project that used dream stories from the audience to turn a shipping container into a 40 foot mural. The photograph was taken at the 2014 SCIRT World Buskers Festival in Hagley Park.

Images, UC QuakeStudies

A photograph of Erica Duthy and Struan Ashby from Tape Art NZ creating the 'Dream Machine'. The 'Dream Machine' was a 9-day long creative project that used dream stories from the audience to turn a shipping container into a 40-foot mural. The photograph was taken at the 2014 SCIRT World Buskers Festival in Hagley Park.

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.

Research papers, The University of Auckland Library

During the recent devastating earthquakes in Christchurch, many residential houses were damaged due to widespread liquefaction of the ground. In-situ testing is widely used as a convenient method for evaluating liquefaction potential of soils. Cone penetration test (CPT) and standard penetration test (SPT) are the two popular in situ tests which are widely used in New Zealand for site characterization. The Screw Driving Sounding (SDS) method is a relatively new operating system developed in Japan consisting of a machine that drills a rod into the ground by applying torque at seven steps of axial loading. This machine can continuously measure the required torque, load, speed of penetration and rod friction during the test, and therefore can give a clear overview of the soil profile along the depth of penetration. In this paper, based on a number of SDS tests conducted in Christchurch, a correlation was developed between tip resistance of CPT test and SDS parameters for layers consisting of different fines contents. Moreover, using the obtained correlation, a chart was proposed which relates the cyclic resistance ratio to the appropriate SDS parameter. Using the proposed chart, liquefaction potential of soil can be estimated directly using SDS data. As SDS method is simpler, faster and more economical test than CPT and SPT, it can be a reliable alternative in-situ test for soil characterization, especially in residential house constructions.