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Research Papers, Lincoln University

The 2013 Seddon earthquake (Mw 6.5), the 2013 Lake Grassmere earthquake (Mw 6.6), and the 2016 Kaikōura earthquake (Mw 7.8) provided an opportunity to assemble the most extensive damage database to wine storage tanks ever compiled worldwide. An overview of this damage database is presented herein based on the in-field post-earthquake damage data collected for 2058 wine storage tanks (1512 legged tanks and 546 flat-based tanks) following the 2013 earthquakes and 1401 wine storage tanks (599 legged tanks and 802 flat-based tanks) following the 2016 earthquake. Critique of the earthquake damage database revealed that in 2013, 39% and 47% of the flat-based wine tanks sustained damage to their base shells and anchors respectively, while due to resilience measures implemented following the 2013 earthquakes, in the 2016 earthquake the damage to tank base shells and tank anchors of flat-based wine tanks was reduced to 32% and 23% respectively and instead damage to tank barrels (54%) and tank cones (43%) was identified as the two most frequently occurring damage modes for this type of tank. Analysis of damage data for legged wine tanks revealed that the frame-legs of legged wine tanks sustained the greatest damage percentage among different parts of legged tanks in both the 2013 earthquakes (40%) and in the 2016 earthquake (44%). Analysis of damage data and socio-economic findings highlight the need for industry-wide standards, which may have socio-economic implications for wineries.

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

Timber-based hybrid structures provide a prospective solution for utilizing environmentally friendly timber material in the construction of mid-rise or high-rise structures. This study mainly focuses on structural damage evaluation for a type of timber-steel hybrid structures, which incorporate prefabricated light wood frame shear walls into steel moment-resisting frames (SMRFs). The structural damage of such a hybrid structure was evaluated through shake table tests on a four-story large-scale timber-steel hybrid structure. Four ground motion records (i.e., Wenchuan earthquake, Canterbury earthquake, El-Centro earthquake, and Kobe earthquake) were chosen for the tests, with the consideration of three different probability levels (i.e., minor, moderate and major earthquakes) for each record. During the shake table tests, the hybrid structure performed quite well with visual damage only to wood shear walls. No visual damage in SMRF and the frame-to-wall connections was observed. The correlation of visual damage to seismic intensity, modal-based damage index and inter-story drift was discussed. The reported work provided a basis of knowledge for performance-based seismic design (PBSD) for such timber-based hybrid structures.

Articles, UC QuakeStudies

An edited copy of the pdf transcript of Michelle's second earthquake story, captured by the UC QuakeBox Take 2 project. At the participant's request, parts of this transcript have been redacted. Interviewer: Jennifer Middendorf. Transcriber: Josie Hepburn.