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Images for machines; more images...

Images, UC QuakeStudies

A digitally manipulated image of a high-reach excavator demolishing a building. The photographer comments, "After the earthquakes in Christchurch, New Zealand the demolition of most of the City Centre began. After two years the government thought that the progress was far too slow, so began the start of the automatic demolition. Luckily when the solar powered demolition machines started to cause indiscriminate death and destruction they were isolated to the South Island and unable to cross the seas".

Research Papers, Lincoln University

After 160 years of colonial settlement, Christchurch has recently experienced a sequence of devastating earthquakes and seen the need for a widespread de- and re-construction of the central city, as well as, many of the surrounding neighbourhoods and peri-urban satellite settlements. This paper will offer a view of the opportunities and restrictions to the post-earthquake re-development of Christchurch as informed by ‘growth machine’ theory. A case study investigating an illegal dump in central Christchurch will be used to assess the applicability of growth machine theory to the current disaster response.

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.

Images, Canterbury Museum

One red and black fabric quilt comprised of pieced and appliquéd block work with both hand and machine stitching; machine quilted with embellishments and a one piece bordered back; an image of the ChristChurch Cathedral is in the centre and features pen work. Designed and quilted by the Coast Quilters of Whangaroa from fabric sent in by listener...

Images, Canterbury Museum

One red and black fabric quilt comprised of pieced and appliquéd block work with both hand and machine stitching; machine quilted with embellishments and a one piece bordered back; an image of the ChristChurch Cathedral is in the centre and features pen work. Designed and quilted by the Coast Quilters of Whangaroa from fabric sent in by listener...

Research papers, University of Canterbury Library

Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.

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, University of Canterbury Library

After a high-intensity seismic event, inspections of structural damages need to be carried out as soon as possible in order to optimize the emergency management, as well as improving the recovery time. In the current practice, damage inspections are performed by an experienced engineer, who physically inspect the structures. This way of doing not only requires a significant amount of time and high skilled human resources, but also raises the concern about the inspector’s safety. A promising alternative is represented using new technologies, such as drones and artificial intelligence, which can perform part of the damage classification task. In fact, drones can safely access high hazard components of the structures: for instance, bridge piers or abutments, and perform the reconnaissance by using highresolution cameras. Furthermore, images can be automatically processed by machine learning algorithms, and damages detected. In this paper, the possibility of applying such technologies for inspecting New Zealand bridges is explored. Firstly, a machine-learning model for damage detection by performing image analysis is presented. Specifically, the algorithm was trained to recognize cracks in concrete members. A sensitivity analysis was carried out to evaluate the algorithm accuracy by using database images. Depending on the confidence level desired,i.e. by allowing a manual classification where the alghortim confidence is below a specific tolerance, the accuracy was found reaching up to 84.7%. In the second part, the model is applied to detect the damage observed on the Anzac Bridge (GPS coordinates -43.500865, 172.701138) in Christchurch by performing a drone reconnaissance. Reults show that the accuracy of the damage detection was equal to 88% and 63% for cracking and spalling, respectively.