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Research papers, University of Canterbury Library

The foundation of this thesis rests upon previous research conducted as part of a QuakeCoRE summer scholarship project which investigated the health and safety regulations for utility workers within Aotearoa and in relation to the Canterbury Earthquake Sequence (CES). This project highlighted that the primary Health and Safety at Work Act 2015 was not directly applicable to these workers, given the unique set of risks and dangers. Additionally, the same research found that, in the absence of adequate intervention mechanisms, there is a reliance on internal health and safety procedures and standards, which may be compromised in an emergency scenario. A key element of Aotearoa’s disaster response framework regarding utility workers is the use of emergency powers, whereby the Director of Civil Defence Emergency Management may order utility workers, by proxy, to undertake any order during a state of national emergency or a national transition period.4 This power appears to be unrestrained and creates tension in relation to human rights and worker’s rights. The endangerment of utility workers in a disaster scenario is a global issue, with extensive research suggesting the involvement of utility workers within the immediate aftermath of disasters across many jurisdictions.5 This thesis investigates the involvement of utility workers in emergencies in two different jurisdictions, alongside the legal and non-legal protective measures taken within these jurisdictions to safeguard their mental and physical health.

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