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
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
Road networks are highly exposed to natural hazard events, which can lead to significant economic and social consequences. In New Zealand, events such as the 2011 Christchurch earthquake, the 2016 Kaikōura earthquake, and the Cyclone Gabrielle in 2023 have demonstrated the severe consequences of road network disruptions. Traditional post event economic assessments often focus solely on clean-up and repair costs, neglecting the broader and more enduring impacts these events can have. Furthermore, business cases for resilience investments usually fail when quantifying the economic benefits of mitigation strategies, due to the underestimation of road disruption consequences. Importantly, not all road link disruptions contribute equally to these consequences, making the identification of critical road links a key step in resilience focused investment prioritization. Furthermore, traditional transportation asset management typically evaluates the life cycle of roads under normal conditions, such as traffic loads and standard environmental factors, while neglecting the influence of natural hazards. However, these events can significantly alter road deterioration and increase maintenance costs, emphasizing the need for integrating risk and resilience into transportation asset management approaches. This thesis presents a methodology to evaluate road criticality by assessing the economic consequences of road disruptions in combination with a hazard model in a prioritization index. Initially, the consequences are quantified through increased travel time, higher vehicle operating costs, and increased gas emissions. Thereafter, a new consequence model is introduced to estimate the increase in maintenance costs on alternative routes that absorb diverted traffic following a disruption. These consequence models are initially applied in a 'full-scan' analysis approach, where each road link is removed in turn to quantify its potential impact and, therefore, its criticality. Subsequently, a hazard model is integrated to develop a road prioritization index that combines the expected impacts of road disruptions, the individual road link criticality, and the probability of occurrence of natural hazard events. This index is designed to help road agencies in prioritizing mitigation strategies. Furthermore, the proposed methodology can also be applied to quantify the indirect economic impacts of natural hazard events. The methodology is demonstrated using New Zealand’s South Island inter-urban network as a case study, incorporating an earthquake-induced landslide model, with Python based simulations, providing road agencies a valuable tool to quantify the economic benefits of resilience investments
