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Research papers, The University of Auckland Library

Following the Christchurch earthquake of 22 February 2011 a number of researchers were sent to Christchurch, New Zealand to document the damage to masonry buildings as part of “Project Masonry”. Coordinated by the Universities of Auckland and Adelaide, researchers came from Australia, New Zealand, Canada, Italy, Portugal and the US. The types of masonry investigated were unreinforced clay brick masonry, unreinforced stone masonry, reinforced concrete masonry, residential masonry veneer and churches; masonry infill was not part of this study. This paper focuses on the progress of the unreinforced masonry (URM) component of Project Masonry. To date the research team has completed raw data collection on over 600 URM buildings in the Christchurch area. The results from this study will be extremely relevant to Australian cities since URM buildings in New Zealand are similar to those in Australia.

Images, eqnz.chch.2010

A crane topples over on Victoria Street while taking glass up to some windows. No one was hurt and the glass never broke. Victoria Street was closed from 7:30am to later in the evening. This all happen on the Knox Plaza building site. Christchurch October 13, 2014 New Zealand.

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.