This thesis explores the intricate relationship between dance and trauma, focusing on how embodied practices facilitate psychological recovery. Dominant narratives often prioritise cognitive experiences, limiting our understanding of healing. By employing a qualitative, post-positivist and critical autoethnographic approach, I reflect on my journey through trauma following the Christchurch earthquakes, utilising journal entries from the point of view of my younger self to illustrate the transformative power of movement and dance. The key themes of this research are structure and routine, socialisation, and alleviation of anxious thoughts, demonstrating how engaging with the body challenges conventional notions of recovery. Furthermore, it highlights the complementary role of Dance Movement Therapy in trauma- informed practices, advocating for a holistic approach that recognises the mind-body connection. The findings underscore the necessity of viewing trauma as an embodied experience and propose a shift toward movement-based therapeutic practices that empower individuals through their lived experiences. Ultimately, this research calls for reimagining therapeutic frameworks, emphasising dance's potential to complement current trauma- informed therapies and promote a bottom-up approach to recovery
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
