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.
New Zealand has a long tradition of using light timber frame for construction of its domestic dwellings. After the most recent earthquakes (e.g. Canterbury earthquakes sequence), wooden residential houses showed satisfactory life safety performance which aligns with New Zealand design codes requirements. However, poor performance was reported in terms of their seismic resilience that can be generally associated with community demands. Future expectations of the seismic performance of wooden-framed houses by homeowners were assessed in this research. Homeowners in the Wellington region were asked in a survey about the levels of safety and expected possible damage in their houses after a seismic event. Findings bring questions about whether New Zealand code requirements are good enough to satisfy community demands. Also, questions whether available information of strengthening techniques to structurally prepare wooden-framed houses to face future major earthquakes can help to make homeowners feel safer at home during major seismic events.
Following the 2010/2011 Canterbury earthquakes, approximately 60% of multi-story buildings with reinforced concrete walls required demolition. Both practitioners and researchers have increasingly realized that low-damage structural systems could be an alternative to improve the seismic behaviour of concrete buildings and to reduce the economic and social impact of structural damage in future earthquakes. To verify the seismic response of a low-damage concrete wall building representing state-of-art design practice, a shake table test on a two-story concrete building was recently conducted as part of an ILEE-QuakeCoRE collaborative research program. The building utilized flexible wall-to-floor connections in the long span direction and isolating wall-to-floor devices in the short span direction to provide a comparison of their respective behaviour. Additionally, the wall-to-floor interaction such as effects of wall uplift on the link slab, and force transfer mechanism from floor to the wall will be discussed in this paper.
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.
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 QuakeCoRE Emerging Researchers Chapter (QERC) is a network of students and emerging researchers composed of three chapters: Auckland, Canterbury, and Wellington. Our aim is to promote networking, collaboration, and knowledge sharing among emerging researchers in the earthquake resilience community. QERC does this by organising technical, social, and outreach events. As with everyone else during the pandemic crisis, QERC had to change its approach in organising events. However, instead of treating it as an obstacle, QERC utilised the lockdown period as an opportunity to connect the three chapters and organised more events than they usually would during normal times. In the 11 weeks that universities were closed and New Zealand was under Alert Levels 2, 3 and 4, QERC organised 15 various events such as research presentations, well-being workshops, a women's catch-up, and a trivia night. However, as the weeks went by, the novelty of online meetings faded and fewer people came to the virtual events. Therefore as soon as the country moved to Alert Level 1, the Chapters started organising in-person events, which members were eager to attend. Nonetheless, the option to join events remotely still remains and the three chapters continue to collaborate for various events.
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.
Lake Taupō in New Zealand is associated with frequent unrest and small to moderate eruptions. It presents a high consequence risk scenario with immense potential for destruction to the community and the surrounding environment. Unrest associated with eruptions may also trigger earthquakes. While it is challenging to educate people about the hazards and risks associated with multiple eruptive scenarios, effective education of students can lead to better mitigation strategies and risk reduction. Digital resources with user-directed outcomes have been successfully used to teach action oriented skills relevant for communication during volcanic crisis [4]. However, the use of choose your own adventure strategies to enhance low probability risk literacy for Secondary school outreach has not been fully explored. To investigate how digital narrative storytelling can mediate caldera risk literacy, a module “The Kid who cried Supervolcano” will be introduced in two secondary school classrooms in Christchurch and Rotorua. The module highlights four learning objectives: (a) Super-volcanoes are beautiful but can be dangerous (b) earthquake (unrest) activity is normal for super-volcanoes (c) Small eruptions are possible from super-volcanoes and can be dangerous in our lifetimes (d) Super-eruptions are unlikely in our lifetimes. Students will create their digital narrative using the platform Elementari (www.elementari.io). The findings from this study will provide clear understanding of students’ understanding of risk perceptions of volcanic eruption scenarios and associated hazards and inform the design of educational resources geared towards caldera risk literacy.
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.
High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination of whether a ground motion record is high-quality is poorly handled by automation with mathematical functions and can become prohibitive if done manually. Machine learning applications are well-suited to this problem, and a previous feed-forward neural network was developed (Bellagamba et al. 2019) to determine high-quality records from small crustal events in the Canterbury and Wellington regions for simulation validation. This prior work was however limited by the omission of moderate-to-large magnitude events and those from other tectonic environments, as well as a lack of explicit determination of the minimum usable frequency of the ground motion. To address these shortcomings, an updated neural network was developed to predict the quality of ground motion records for all magnitudes and all tectonic sources—active shallow crustal, subduction intraslab, and subduction interface—in New Zealand. The predictive performance of the previous feed-forward neural network was matched by the neural network in the domain of small crustal records, and this level of predictive performance is now extended to all source magnitudes and types in New Zealand making the neural network applicable to global ground motion databases. Furthermore, the neural network provides quality and minimum usable frequency predictions for each of the three orthogonal components of a record which may then be mapped into a binary quality decision or otherwise applied as desired. This framework provides flexibility for the end user to predict high-quality records with various acceptability thresholds allowing for this neural network to be used in a range of applications.
Reconnaissance reports have highlighted the poor performance of non-ductile reinforced concrete buildings during the 2010-11 Canterbury earthquakes. These buildings are widely expected to result in significant losses under future earthquakes due to their seismic vulnerability and prevalence in densely populated urban areas. Wellington, for example, contains more than 70 pre-1970s multi-storey reinforced concrete buildings, ranging in height from 5 to 18 storeys. This study seeks to characterise the seismic performance and evaluate the likely failure modes of a typical pre-1970s reinforced concrete building in Wellington, by conducting advanced numerical simulations to evaluate its 3D nonlinear dynamic response. A representative 9-storey office building constructed in 1951 is chosen for this study and modelled in the finite element analysis programme DIANA, using a previously developed and validated approach to predict the failure modes of doubly reinforced walls with confined boundary regions. The structure consists of long walls and robust framing elements resulting in a stiff lateral load resisting system. Barbell-shaped walls are flanked by stiff columns with sufficient transverse reinforcement to serve as boundary regions. Curved shell elements are used to model the walls and their boundary columns, for which the steel reinforcement is explicitly modelled. Line elements are used to model the frame elements. The steel reinforcement in each member is explicitly modelled. The floor slabs are modelled using elastic shell elements. The model is analysed under short and long duration ground motions selected to match site specific targets in Wellington at the DBE and MCE intensity levels. The observed response of the building including drift profiles at each intesity level, strain localization effects around wall openings, and the influence of bidirectional loading are discussed.
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.
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.
