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Research papers, The University of Auckland Library

This thesis investigates life-safety risk in earthquakes. The first component of the thesis utilises a dataset of earthquake injuries and deaths from recent earthquakes in New Zealand to identify cause, context, and risk factors of injury and death in the 2011 MW6.3 Christchurch earthquake and 2016 MW7.8 Kaikōura earthquake. Results show that nearly all deaths occurred from being hit by structural elements from buildings, while most injuries were caused by falls, strains and being hit by contents or non-structural elements. Statistical analysis of injured cases compared to an uninjured control group found that age, gender, building damage, shaking intensity, and behaviour during shaking were the most significant risk factors for injury during these earthquakes. The second part of the thesis uses the empirical findings from the first section to develop two tools for managing life-safety risk in earthquakes. The first tool is a casualty estimation model for health system and emergency response planning. An existing casualty model used in New Zealand was validated against observed data from the 2011 Christchurch earthquake and found to underestimate moderate and severe injuries by an order of magnitude. The model was then updated to include human behaviour such as protective actions, falls and strain type injuries that are dependent on shaking intensity, as well as injuries and deaths outside buildings. These improvements resulted in a closer fit to observed casualties for the 2011 Christchurch earthquake. The second tool that was developed is a framework to set seismic loading standards for design based on fatality risk targets. The proposed framework extends the risk-targeted hazard method, by moving beyond collapse risk targets, to fatality risk targets for individuals in buildings and societal risk in cities. The framework also includes treatment of epistemic uncertainty in seismic hazard to allow this uncertainty to be used in risk-based decision making. The framework is demonstrated by showing how the current New Zealand loading standards could be revised to achieve uniform life-safety risk across the country and how the introduction of a new loading factor can reduce risk aggregation in cities. Not on Alma, moved and emailed. 1/02/2023 ce

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.