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Research papers, The University of Auckland Library

This thesis describes the strategies for earthquake strengthening vintage clay bricks unreinforced masonry (URM) buildings. URM buildings are well known to be vulnerable to damage from earthquake-induced lateral forces that may result in partial or full building collapse. The 2010/2011 Canterbury earthquakes are the most recent destructive natural disaster that resulted in the deaths of 185 people. The earthquake events had drawn people’s attention when URM failure and collapse caused about 39 of the fatality. Despite the poor performance of URM buildings during the 2010/2011 Canterbury earthquakes, a number of successful case study buildings were identified and their details research in-depth. In order to discover the successful seismic retrofitting techniques, two case studies of retrofitted historical buildings located in Christchurch, New Zealand i.e. Orion’s URM substations and an iconic Heritage Hotel (aka Old Government Building) was conducted by investigating and evaluating the earthquake performance of the seismic retrofitting technique applied on the buildings prior to the 2010/2011 Canterbury earthquakes and their performance after the earthquakes sequence. The second part of the research reported in this thesis was directed with the primary aim of developing a cost-effective seismic retrofitting technique with minimal interference to the vintage clay-bricks URM buildings. Two retrofitting techniques, (i) near-surface mounted steel wire rope (NSM-SWR) with further investigation on URM wallettes to get deeper understanding the URM in-plane behaviour, and (ii) FRP anchor are reported in this research thesis.

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.