This survey was established by the University of Canterbury (UC) to assist the Marlborough community in recording and understanding the level and types of recreational beach uses that are occurring at present on the earthquake-affected coast. The questions were designed to capture a comprehensive view of recreational activities and interests and allowed for any activity, view or perspective to be recorded. All responses were anonymous and no identifying information was collected. The survey used an online format open to all interested people 18+ years of age (for informed consent reasons) over a two month period (October – November 2020). The geographic focus of the survey was the coastline between Marfells Beach and the Waima / Ure River which is the area under currently under consideration by Marlborough District Council for development of a new bylaw. However, the design of the survey questions also allowed respondents to record information pertaining to any other area.
Post-earthquake cordons have been used after seismic events around the world. However, there is limited understanding of cordons and how contextual information of place such as geography, socio-cultural characteristics, economy, institutional and governance structure etc. affect decisions, operational procedures as well as spatial and temporal attributes of cordon establishment. This research aims to fill that gap through a qualitative comparative case study of two cities: Christchurch, New Zealand (Mw 6.2 earthquake, February 2011) and L’Aquila, Italy (Mw 6.3 earthquake, 2009). Both cities suffered comprehensive damage to its city centre and had cordons established for extended period. Data collection was done through purposive and snowball sampling methods whereby 23 key informants were interviewed in total. The interviewee varied in their roles and responsibilities i.e. council members, emergency managers, politicians, business/insurance representatives etc. We found that cordons were established to ensure safety of people and to maintain security of place in both the sites. In both cities, the extended cordon was met with resistance and protests. The extent and duration of establishment of cordon was affected by recovery approach taken in the two cities i.e. in Christchurch demolition was widely done to support recovery allowing for faster removal of cordons where as in L’Aquila, due to its historical importance, the approach to recovery was based on saving all the buildings which extended the duration of cordon. Thus, cordons are affected by site specific needs. It should be removed as soon as practicable which could be made easier with preplanning of cordons.
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.
There is an increasing recognition that the seismic performance of buildings will be affected by the behaviour of both structural and non-structural elements. In light of this, work has been progressing at the University of Canterbury to develop guidelines for the seismic assessment of commercial glazing systems. This paper reviews the seismic assessment guidelines prescribed in Section C10 of the MBIE building assessment guidelines. Subsequently, the C10 approach is used to assess the drift capacity of a number of glazing units recently tested at the University of Canterbury. Comparing the predicted and observed drift capacities, it would appear that the C10 guidelines may lead to nonconservative estimates of drift capacity. Furthermore, the experimental results indicate that watertightness may be lost at very low drift demands, suggesting that guidance for the assessment of serviceability performance would also be beneficial. As such, it is proposed that improved guidance be provided to assist engineers in considering the possible impact that glazing could have on the structural response of a building in a large earthquake.
The earthquake engineering community is currently grappling with the need to improve the post-earthquake reparability of buildings. As part of this, proposals exist to change design criteria for the serviceability limit state (SLS). This paper reviews options for change and considers how these could impact the expected repair costs for typical New Zealand buildings. The expected annual loss (EAL) is selected as a relevant measure or repair costs and performance because (i) EAL provides information on the performance of a building considering a range of intensity levels, (ii) the insurance industry refers to EAL when setting premiums, and (iii) monetary losses are likely to be correlated with loss of building functionality. The paper argues that because the expected annual loss is affected by building performance over a range of intensity levels, the definition of SLS criteria alone may be insufficient to effectively limit losses. However, it is also explained that losses could be limited effectively if the loadings standard were to set the SLS design intensity considering the potential implications on EAL. It is shown that in order to achieve similar values of EAL in Wellington and Christchurch, the return period intensity for SLS design would need to be higher in Christchurch owing to differences in local hazard conditions. The observations made herein are based on a simplified procedure for EAL estimation and hence future research should aim to verify the findings using a detailed loss assessment approach applied to a broad range of case study buildings.
The question of whether forced relocation is beneficial or detrimental to the displaced households is a controversial and important policy question. After the 2011 earthquake in Christchurch, the government designated some of the worst affected areas as Residential Red Zones. Around 20,000 people were forced to move out of these Residential Red Zone areas, and were compensated for that. The objective of this paper is twofold. First, we aim to estimate the impact of relocation on the displaced households in terms of their income, employment, and their mental and physical health. Second, we evaluate whether the impact of relocation varies by the timing of to move, the destination (remaining within the Canterbury region or moving out of it) and demographic factors (gender, age, ethnicity). StatisticsNZ’s Integrated Data Infrastructure (IDI) from 2008 to 2017, which includes data on all households in Canterbury, and a difference-in-difference (DID) technique is used to answer these questions. We find that relocation has a negative impact on the income of the displaced household group. This adverse impact is more severe for later movers. Compared to the control group (that was not relocated), the income of relocated households was reduced by 3% for people who moved immediately after the earthquake in 2011, and 14% for people who moved much later in 2015.
