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Research papers, University of Canterbury Library

Our poster will present on-going QuakeCoRE-founded work on strong motion seismology for Dunedin-Mosgiel area, focusing on ground motion simulations for Dunedin Central Business District (CBD). Source modelling and ground motion simulations are being carried out using the SCEC (Southern California Earthquakes Center) Broad Band simulation Platform (BBP). The platform computes broadband (0-10 Hz) seismograms for earthquakes and was first implemented at the University of Otago in 2016. As large earthquakes has not been experienced in Dunedin in the time of period of instrumental recording, user-specified scenario simulations are of great value. The Akatore Fault, the most active fault in Otago and closest major fault to Dunedin, is the source focused on in the present study. Simulations for various Akatore Fault source scenarios are run and presented. Path and site effects are key components considered in the simulation process. A 1D shear wave velocity profile is required by SCEC BBP, and this is being generated to represent the Akatore-to-CBD path and site within the BBP. A 3D shear velocity model, with high resolution within Dunedin CBD, is being developed in parallel with this study (see Sangster et al. poster). This model will be the basis for developing a 3D shear wave velocity model for greater Dunedin-Mosgiel area for future ground motion simulations, using Canterbury software (currently under development).

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.