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.
This dissertation contains three essays on the impact of unexpected adverse events on student outcomes. All three attempt to identify causal inference using plausibly exogenous shocks and econometric tools, applied to rich administrative data. In Chapter 2, I present evidence of the causal effects of the 2011 Christchurch earthquake on tertiary enrolment and completion. Using the shock of the 2011 earthquake on high school students in the Canterbury region, I estimate the effect of the earthquake on a range of outcomes including tertiary enrolment, degree completion and wages. I find the earthquake causes a substantial increase in tertiary enrolment, particularly for low ability high school leavers from damaged schools. However, I find no evidence that low ability students induced by the earthquake complete a degree on time. In Chapter 3, I identify the impact of repeat disaster exposure on university performance, by comparing outcomes for students who experience their first earthquake while in university, to outcomes for students with prior earthquake exposure. Using a triple-differences estimation strategy with individual-by-year fixed effects, I identify a precise null effect, suggesting that previous experience of earthquakes is not predictive of response to an additional shock two years later. The final chapter investigates the impact of injuries sustained in university on academic performance and wages, using administrative data including no-fault insurance claims, emergency department attendance and hospital admissions, linked with tertiary enrolment. I find injuries, including minor injuries, have a negative effect on re-enrolment, degree completion and grades in university.
