Welcome to the Recover newsletter Issue 6 from the Marine Ecology Research Group (MERG) of the University of Canterbury. Recover is designed to keep you updated on our MBIE-funded earthquake recovery project called RECOVER (Reef Ecology, Coastal Values & Earthquake Recovery). This 6th instalment features the ‘new land’ created by the earthquake uplift of the coastline, recreational uses of beaches in Marlborough, and pāua survey work and hatchery projects with our partners in Kaikōura.
This report contributes to a collaborative project between the Marlborough District Council (MDC) and University of Canterbury (UC) which aims to help protect and promote the recovery of native dune systems on the Marlborough coast. It is centred around the mapping of dune vegetation and identification of dune protection zones for old-growth seed sources of the native sand-binders spinifex (Spinifex sericeus) and pīngao (Ficinia spiralis). Both are key habitat-formers associated with nationally threatened dune ecosystems, and pīngao is an important weaving resource and Ngāi Tahu taonga species. The primary goal is to protect existing seed sources that are vital for natural regeneration following major disturbances such as the earthquake event. Several additional protection zones are also identified for areas where new dunes are successfully regenerating, including areas being actively restored in the Beach Aid project that is assisting new native dunes to become established where there is available space.
While societal messages can encourage an unhealthy strive for perfection, the notion of embracing individual flaws and openly displaying vulnerabilities can appear foreign and outlandish. However, when fallibility is acknowledged and imperfection embraced, intimate relationships built on foundations of acceptance, trust and understanding can be established. In an architectural context, similar deep-rooted connections can be formed between a people and a place through the retention of layers of historical identity. When a building is allowed to age with blemishes laid bare for all to see, an architectural work can exhibit a sense of 'humanising vulnerability' where the bruises and scars it bears are able to visually communicate its contextual narrative. This thesis explores the notion of designing to capitalise on past decay through revitalisation of the former Wood Brothers Flour Mill in Addington, Christchurch (1891). Known as one of the city's last great industrial buildings, the 130-year-old structure remains hugely impressive due to its sheer size and scale despite being abandoned and subject to vandalism for a number of years. Its condition of obsolescence ensured the retention of visible signs of wear and tear in addition to the extensive damage caused by the 2010-12 Canterbury earthquakes. In offering a challenge to renovation and reconstruction as a means of conservation, this thesis asks if 'doing less' has the potential to 'do more'. How can an understanding of architecture as an ongoing process inform a design approach to celebrate ageing and patina? While the complex is undergoing redevelopment at the time of writing, the design project embraces the condition of the historic buildings in the immediate aftermath of the earthquakes and builds upon the patina of the mill and adjacent flour and grain store in developing a design for their adaptation as a micro-distillery. Research into the traditional Japanese ideology of wabi-sabi and its practical applications form the basis for a regenerative design approach which finds value in imperfection, impermanence and incompleteness. The thesis combines a literature review, precedent review and site analysis together with a design proposal. This thesis shows that adaptive reuse projects can benefit from an active collaboration with the processes of decay. Instead of a mindset where an architectural work is considered the finished article upon completion of construction, an empathetic and sensitive design philosophy is employed in which careful thought is given to the continued preservation and evolution of a structure with the recognition that evidence of past wear, tear, patina and weathering can all contribute positively to a building's future. In this fashion, rather than simply remaining as relics of the past, buildings can allow the landscape of their urban context to shape and mould them to ensure that their architectural experience can continue to be enjoyed by generations to come.
The Canterbury earthquake sequence of 2010-2011 wrought ruptures in not only the physical landscape of Canterbury and Christchurch’s material form, but also in its social, economic, and political fabrics and the lives of Christchurch inhabitants. In the years that followed, the widespread demolition of the CBD that followed the earthquakes produced a bleak landscape of grey rubble punctuated by damaged, abandoned buildings. It was into this post-earthquake landscape that Gap Filler and other ‘transitional’ organisations inserted playful, creative, experimental projects to bring life and energy back into the CBD. This thesis examines those interventions and the development of the ‘Transitional Movement’ between July 2013 and June 2015 via the methods of walking interviews and participant observation. This critical period in Christchurch’s recovery serves as an example of what happens when do-it-yourself (DIY) urbanism is done at scale across the CBD and what urban experimentation can offer city-making. Through an understanding of space as produced, informed by Lefebvre’s thinking, I explore how these creative urban interventions manifested a different temporality to orthodox planning and demonstrate how the ‘soft’ politics of these interventions contain the potential for gentrification and also a more radical politics of the city, by creating an opening space for difference.
This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.
