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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.

Research papers, University of Canterbury Library

New Zealand has a long tradition of using light timber frame for construction of its domestic dwellings. After the most recent earthquakes (e.g. Canterbury earthquakes sequence), wooden residential houses showed satisfactory life safety performance. However, poor performance was reported in terms of their seismic resilience. Although numerous innovative methods to mitigate damage have been introduced to the New Zealand community in order to improve wooden house performance, these retrofit options have not been readily taken up. The low number of retrofitted wooden-framed houses leads to questions about whether homeowners are aware of the necessity of seismic retrofitting their houses to achieve a satisfactory seismic performance. This study aims to explore different retrofit technologies that can be applied to wooden-framed houses in Wellington, taking into account the need of homeowners to understand the risk, likelihood and extent of damage expected after an event. A survey will be conducted in Wellington about perceptions of homeowners towards the expected performance of their wooden-framed houses. The survey questions were designed to gain an understanding of homeowners' levels of safety and awareness of possible damage after a seismic event. Afterwards, a structural review of a sample of the houses will be undertaken to identify common features and detail potential seismic concerns. The findings will break down barriers to making improvements in the performance of wooden-framed houses and lead to enhancements in the confidence of homeowners in the event of future seismic activity. This will result in increased understanding and contribute towards an accessible knowledge base, which will possibly increase significantly the use of these technologies and avoid unnecessary economic and social costs after a seismic event.