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Images, UC QuakeStudies

Alan Hoskin, a member of the University of Canterbury's E-Learning team, in their temporary office in the James Hight building. The photographer comments, "First looks at our new temporary (maybe) office space. Our group will stay here until April or May 2011, then will move to another floor in the Central Library. Bean bag. Alan wanted the beanbag but Jess said no".

Images, UC QuakeStudies

A road cone on the side of Kingsford Street in the Horseshoe Lake district. The cone has been decorated to look like Santa Claus. The photograph has been captioned by BeckerFraserPhotos, "This cone looks jaunty with a particularly flirtatious swing to its skirt, which provides a nice contrast to the sand-bagged road sign".

Research papers, The University of Auckland Library

The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.