This thesis revisits the topic of earthquake recovery in Christchurch City more than a decade after the Canterbury earthquakes. Despite promising visions of a community reconnected and a sustainable and liveable city, significant portions of the city’s core – the Red Zone – remain dilapidated and “eerily empty”. At the same time, new developments in other areas have proven to be alienated or underutilised. Currently, the Canterbury Earthquake Recovery Authority’s plans for the rebuilding highlight the delivery of more residential housing to re-populate the city centre. However, prevalent approaches to housing development in Christchurch are ineffective for building an inclusive and active community. Hence, the central inquiry of the thesis is how the development of housing complexes can revitalise the Red Zone within the Christchurch city centre. The inquiry has been carried out through a research-through-design methodology, recognising the importance of an in-depth investigation that is contextualised and combined with the intuition and embodied knowledge of the designer. The investigation focuses on a neglected site in the Red Zone in the heart of Christchurch city, with significant Victorian and Edwardian Baroque heritage buildings, including Odeon Theatre, Lawrie & Wilson Auctioneers, and Sol Square, owned by The Regional Council Environment Canterbury. The design inquiry argues, develops, and is carried through a place-assemblage lens to housing development for city recovery, which recognizes the significance of socially responsive architecture that explores urban renewal by forging connections within the social network. Therefore, place-assemblage criteria and methods for developing socially active and meaningful housing developments are identified. Firstly, this thesis argues that co-living housing models are more focused on people relations and collective identity than the dominant developer-driven housing rebuilds, as they prioritise conduits for interaction and shared social meaning and practices. Secondly, the adaptive reuse of derelict heritage structures is proposed to reinvigorate the urban fabric, as heritage is seen to be conceived as and from a social assemblage of people. The design is realised by the principles outlined in the ICOMOS charter, which involves incorporating the material histories of existing structures and preserving the intangible heritage of the site by ensuring the continuity of cultural practices. Lastly, design processes and methods are also vital for place-sensitive results, which pay attention to the site’s unique characteristics to engage with local stakeholders and communities. The research explores place-assemblage methods of photographic extraction, the drawing of story maps, precedent studies, assemblage maps, bricolages, and paper models, which show an assembly of layers that piece together the existing heritage, social conduits, urban commons and housing to conceptualise the social network within its place
Livelihood holds the key to a rapid recovery following a large-scale devastating disaster, building its resilience is of paramount importance. While much attention has been given to how to help people who are displaced from their jobs to regain employment, little research on livelihood resilience has been undertaken for those relocated communities following a disaster event. By studying five re-located villages post-2004 Indian Ocean Tsunami in Banda Aceh and Aceh Besar, Indonesia, this research has identified the indicators of livelihood resilience and the critical factors driving it for post-disaster relocated communities. A mixed approach, combining questionnaire surveys, semistructured interviews, and field observations, was used for the collection of data. Housing entitlement, the physical and mental health of residents, access to external livelihood support and the provision of infrastructure and basic services were identified as amongst the most critical indicators that represent the level of livelihood resilience. Early recovery income support, physical and mental health, availability and timeliness of livelihood support, together with cultural sensitivity and governance structure, are amongst the most important factors. Given the nature of resettlement, access to infrastructure, location of relocated sites, the safety of the neighbourhood and the ability to transfer to other jobs/skills also play an important role in establishing sustained employment for relocated communities in Indonesia. Those indicators and factors were synthesised into a framework which was further tested in the recovery of Christchurch, and Kaikoura, New Zealand during their recovery from devastating earthquakes. It is suggested that the framework can be used by government agencies and aid organisations to assess the livelihood resilience of post-disaster relocated communities. This will help better them plan support policies and/or prioritise resilience investment strategies to ensure that the recovery needs of those relocated are best met
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
