The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
Seismic isolation is an effective technology for significantly reducing damage to buildings and building contents. However, its application to light-frame wood buildings has so far been unable to overcome cost and technical barriers such as susceptibility of light-weight buildings to movement under high-wind loading. The 1994 Northridge Earthquake (6.7 MW) in the United States, 1995 Kobe Earthquake (6.9 MW) in Japan and 2011 Christchurch Earthquake (6.7 Mw) all highlighted significant loss to light-frame wood buildings with over half of earthquake recovery costs allocated to their repair and reconstruction. This poster presents a value case to highlight the benefits of seismically isolated residential buildings compared to the standard fixed-base dwellings for the Wellington region. Loss data generated by insurance claim information from the 2011 Christchurch Earthquake has been used to determine vulnerability functions for the current light-frame wood building stock. By using a simplified single degree of freedom (SDOF) building model, methods for determining vulnerability functions for seismic isolated buildings are developed. Vulnerability functions are then applied directly in a loss assessment to determine the Expected Annual Loss. Vulnerability was shown to dramatically reduce for isolated buildings compared to an equivalent fixed-base building resulting in significant monetary savings, justifying the value case. A state-of-the-art timber modelling software, Timber3D, is then used to model a typical residential building with and without seismic isolation to assess the performance of a proposed seismic isolation system which addresses the technical and cost issues.
Rising disaster losses, growth in global migration, migrant labour trends, and increasingly diverse populations have serious implications for disaster resilience around the world. These issues are of particular concern in New Zealand, which is highly exposed to disaster risk and has the highest proportion of migrant workers to national population in the OECD. Since there has been no research conducted into this issue in New Zealand to date, greater understanding of the social capital used by migrant workers in specific New Zealand contexts is needed to inform more targeted and inclusive disaster risk management approaches. A New Zealand case study is used to investigate the extent and types of social capital and levels of disaster risk awareness reported by members of three Filipino migrant workers organisations catering to dairy farm, construction and aged care workers in different urban and rural Canterbury districts. Findings from (3) semi-structured interviews and (3) focus groups include consistently high reliance on bonding capital and low levels of bridging capital across all three organisations and industry sectors, and in both urban and rural contexts. The transitory, precarious residential status conveyed by temporary work visas, and the difficulty of building bridging capital with host communities has contributed to this heavy reliance on bonding capital. Social media was essential to connect workers with family and friends in other countries, while Filipino migrant workers organisations provided members with valuable access to industry and district-specific networks of other Filipino migrant workers. Linking capital varied between the three organisations, with members of the organisation set up to advocate for dairy farm workers reporting the highest levels of linking capital. Factors influencing the capacity of workers organisations to develop linking capital appeared to include motivation (establishment objectives), length of time since establishment, support from government and industry groups, urban-rural context, income levels and gender. Although aware of publicity around earthquake and tsunami risk in the Canterbury region, participants were less aware of flood risk, and expressed fatalistic attitudes to disaster risk. Workers organisations offer a valuable potential interface between CDEM Group activities and migrant worker communities, since organisation leaders were interested in accessing government support to participate (with and on behalf of members) in disaster risk planning at district and regional level. With the potential to increase disaster resilience among these vulnerable, hard to reach communities, such participation could also help to build capacity across workers organisations (within Canterbury and across the country) to develop linking capital at national, as well as regional level. However, these links will also depend on greater government and industry commitment to providing more targeted and appropriate support for migrant workers, including consideration of the cultural qualifications of staff tasked with liaising with this community.
