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Research papers, The University of Auckland Library

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.

Research papers, University of Canterbury Library

Researchers have begun to explore the opportunity presented by blue-green infrastructure(a subset of nature-based solutions that provide blue and green space in urban infrastructure)as a response to the pressures of climate change. The 2010/2011 Canterbury earthquake sequence created a unique landscape within which there is opportunity to experiment with and invest in new solutions to climate change adaptation in urban centres. Constructed wetlands are an example of blue-green infrastructure that can potentially support resilience in urban communities. This research explores interactions between communities and constructed wetlands to understand how this may influence perceptions of community resilience. The regeneration of the Ōtākaro Avon River Corridor (OARC) provides a space to investigate these relationships. Seven stakeholders from the community, industry, and academia, each with experience in blue-green infrastructure in the OARC, participated in a series of semi-structured interviews. Each participant was given the opportunity to reflect on their perspectives of community, community resilience, and constructed wetlands and their interconnections. Interview questions aligned with the overarching research objectives to (1) understand perceptions around the role of wetlands in urban communities, (2) develop a definition for community resilience in the context of the Ōtākaro Avon community, and (3) reflect on how wetlands can contribute to (or detract from) community resilience. This study found that constructed wetlands can facilitate learning about the challenges and solutions needed to adapt to climate change. From the perspective of the community representatives, community resilience is linked to social capital. Strong social networks and a relationship with nature were emphasised as core components of a community’s ability to adapt to disruption. Constructed wetlands are therefore recognised as potentially contributing to community resilience by providing spaces for people to engage with each other and nature. Investment in constructed wetlands can support a wider response to climate change impacts. This research was undertaken with the support of the Ōtākaro Living Laboratory Trust, who are invested in the future of the OARC. The outcomes of this study suggest that there is an opportunity to use wetland spaces to establish programmes that explore the perceptions of constructed wetlands from a broader community definition, at each stage of the wetland life cycle, and at wider scales(e.g., at a city scale or beyond).