Recent tsunami events have highlighted the importance of effective tsunami risk management strategies (including land-use planning, structural and natural mitigation, warning systems, education and evacuation planning). However, the rarity of tsunami means that empirical data concerning reactions to tsunami warnings and evacuation behaviour is rare when compared to findings for evacuations from other hazards. More knowledge is required to document the full evacuation process, including responses to warnings, pre-evacuation actions, evacuation dynamics, and the return home. Tsunami evacuation modelling has the potential to inform evidence-based tsunami risk planning and response. However, to date, tsunami evacuation models have largely focused on the timings of evacuations, rather than behaviours of those evacuating. In this research, survey data was gathered from coastal communities in Banks Peninsula and Christchurch, New Zealand, relating to behaviours and actions during the November 14th 2016 Kaikōura earthquake tsunami. Survey questions asked about immediate actions following the earthquake shaking, reactions to tsunami warnings, pre-evacuation actions, evacuation dynamics and details on congestion. This data was analysed to characterise trends and identify factors that influenced evacuation actions and behaviour, and was further used to develop a realistic evacuation model prototype to evaluate the capacity of the roading network in Banks Peninsula during a tsunami evacuation. The evacuation model incorporated tsunami risk management strategies that have been implemented by local authorities, and exposure and vulnerability data, alongside the empirical data collected from the survey. This research enhances knowledge of tsunami evacuation behaviour and reactions to tsunami warnings, and can be used to refine evacuation planning to ensure that people can evacuate efficiently, thereby reducing their tsunami exposure and personal risk.
Recent global tsunami events have highlighted the importance of effective tsunami risk management strategies (including land-use planning, structural and natural defences, warning systems, education and evacuation measures). However, the rarity of tsunami means that empirical data concerning reactions to tsunami warnings and tsunami evacuation behaviour is rare when compared to findings about evacuations to avoid other sources of hazard. To date empirical research into tsunami evacuations has focused on evacuation rates, rather than other aspects of the evacuation process. More knowledge is required about responses to warnings, pre-evacuation actions, evacuation dynamics and the return home after evacuations. Tsunami evacuation modelling has the potential to inform evidence-based tsunami risk planning and response. However to date tsunami evacuation models have largely focused on timings of evacuations, rather than evacuation behaviours. This Masters research uses a New Zealand case study to reduce both of these knowledge gaps. Qualitative survey data was gathered from populations across coastal communities in Banks Peninsula and Christchurch, New Zealand, required to evacuate due to the tsunami generated by the November 14th 2016 Kaikōura Earthquake. Survey questions asked about reactions to tsunami warnings, actions taken prior to evacuating and movements during the 2016 tsunami evacuation. This data was analysed to characterise trends and identify factors that influenced evacuation actions and behaviour. Finally, it was used to develop an evacuation model for Banks Peninsula. Where appropriate, the modelling inputs were informed by the survey data. Three key findings were identified from the results of the evacuation behaviour survey. Although 38% of the total survey respondents identified the earthquake shaking as a natural cue for the tsunami, most relied on receiving official warnings, including sirens, to prompt evacuations. Respondents sought further official information to inform their evacuation decisions, with 39% of respondents delaying their evacuation in order to do so. Finally, 96% of total respondents evacuated by car. This led to congestion, particularly in more densely populated Christchurch city suburbs. Prior to this research, evacuation modelling had not been completed for Banks Peninsula. The results of the modelling showed that if evacuees know how to respond to tsunami warnings and where and how to evacuate, there are no issues. However, if there are poor conditions, including if people do not evacuate immediately, if there are issues with the roading network, or if people do not know where or how to evacuate, evacuation times increase with there being more bottlenecks leading out of the evacuation zones. The results of this thesis highlight the importance of effective tsunami education and evacuation planning. Reducing exposure to tsunami risk through prompt evacuation relies on knowledge of how to interpret tsunami warnings, and when, where and how to evacuate. Recommendations from this research outline the need for public education and engagement, and the incorporation of evacuation signage, information boards and evacuation drills. Overall these findings provide more comprehensive picture of tsunami evacuation behaviour and decision making based on empirical data from a recent evacuation, which can be used to improve tsunami risk management strategies. This empirical data can also be used to inform evacuation modelling to improve the accuracy and realism of the evacuation models.
Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.
The initial goal of this research was to explore how SME business models change in response to a crisis. Keeping this in mind, the business model canvas (Osterwalder & Pigneur, 2010) was used as a tool to analyse SME business models in the Canterbury region of New Zealand. The purpose was to evaluate the changes SMEs instituted in their business models after being hit by a series of earthquakes in 2010 and 2011. The idea was to conduct interviews with business owners and analyse them using grounded theory methods. As this method is iterative and requires simultaneous data collection and analysis, a tentative model was proposed after first phase of the data collection and analysis. However, as a result of this process, it became apparent that owner-specific characteristics, action orientation and networks were more prominent in the data than business model elements. Although the SMEs in this study experienced several operational changes in their business models, such as a change of location, modifications to their payment terms or expanded/restricted target markets, the suggested framework highlights how owner-specific attributes ensured the recovery of their businesses. After the initial framework was suggested, subsequent interviews were conducted to test, verify, and modify the tentative model. Three aspects of business recovery emerged: (a) cognitive coping – the business owner’s mind-set and motive; (b) adaptive coping – the ability of business owner to take corrective actions; and (c) social capital – the social network of a business owner, including formal and informal connections and their significance. Three distinct groups were identified; self-sufficient SMEs, socially-based SMEs and surviving SMEs. This thesis proposes a grounded theory of business recovery for SMEs following a disaster. Cognitive coping and social capital enabled the owners to take actions, which eventually led to the desired outcomes for the businesses.
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.
There is a growing awareness of the need for the earthquake engineering practice to incorporate in addition to empirical approaches in evaluation of liquefaction hazards advanced methods which can more realistically represent soil behaviour during earthquakes. Currently, this implementation is hindered by a number of challenges mainly associated with the amount of data and user-experience required for such advanced methods. In this study, we present key steps of an advanced seismic effective-stress analysis procedure, which on the one hand can be fully automated and, on the other hand, requires no additional input (at least for preliminary applications) compared to simplified cone penetration test (CPT)-based liquefaction procedures. In this way, effective-stress analysis can be routinely applied for quick, yet more robust estimations of liquefaction hazards, in a similar fashion to the simplified procedures. Important insights regarding the dynamic interactions in liquefying soils and the actual system response of a deposit can be gained from such analyses, as illustrated with the application to two sites from Christchurch, New Zealand.
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.
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.
Designing a structure for higher- than-code seismic performance can result in significant economic and environmental benefits. This higher performance can be achieved using the principles of Performance-Based Design, in which engineers design structures to minimize the probabilistic lifecycle seismic impacts on a building. Although the concept of Performance-Based Design is not particularly new, the initial capital costs associated with designing structures for higher performance have historically hindered the widespread adoption of performance-based design practices. To overcome this roadblock, this research is focused on providing policy makers and stakeholders with evidence-based environmental incentives for designing structures in New Zealand for higher seismic performance. In the first phase of the research, the environmental impacts of demolitions in Christchurch following the Canterbury Earthquakes were quantified to demonstrate the environmental consequences of demolitions following seismic events. That is the focus here. A building data set consisting of 142 concrete buildings that were demolished following the earthquake was used to quantify the environmental impacts of the demolitions in terms of the embodied carbon and energy in the building materials. A reduced set of buildings was used to develop a material takeoff model to estimate material quantities in the entire building set, and a lifecycle assessment tool was used to calculate the embodied carbon and energy in the materials. The results revealed staggering impacts in terms of the embodied carbon and energy in the materials in the demolished buildings. Ongoing work is focused developing an environmental impact framework that incorporates all the complex factors (e.g. construction methodologies, repair methodologies (if applicable), demolition methodologies (if applicable), and waste management) that contribute to the environmental impacts of building repair and demolition following earthquakes.
