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Images for Alices; more images...

Videos, UC QuakeStudies

A video of Adam McGrath, Jess Shanks and Alice Ryan Williams from Lyttelton band The Eastern singing songs to people in the community. The song recipients were nominated by family, friends or workmates, and in total seven people were chosen, including a teacher, office worker and traffic controller. The video and event were organised by All Right?. The video was distributed by The Press and was posted to the All Right? Facebook Timeline in October 2014.

Images, UC QuakeStudies

An aerial photograph of the Christchurch central city. The photograph has been captioned by BeckerFraserPhotos, "High Street runs across this photograph in the top third from the old Majestic Theatre at the intersection of Manchester and Lichfield Streets to the intersection of Madras and St Asaph Street which is just beyond the edge of the photo".

Images, UC QuakeStudies

An aerial photograph of the Christchurch central city. The photograph has been captioned by BeckerFraserPhotos, "The central city, with the Majestic Theatre in the centre of the photograph. Lichfield Street runs from bottom left diagonally up the photograph to the top right. The City Council building is prominent in the bottom left corner and Latimer Square in the top left corner".

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