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.
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.
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.
The affect that the Christchurch Earthquake Sequence(CES) had on Christchurch residents was severe, and the consequences are still being felt today. The Ōtākaro Avon River Corridor (OARC) was particularly impacted, a geographic zone that had over 7,000 homes which needed to be vacated and demolished. The CES demonstrated how disastrous a natural hazard can be on unprepared communities. With the increasing volatility of climate change being felt around the world, considering ways in which communities can reduce their vulnerabilities to natural hazards is vital. This research explores how communities can reduce their vulnerabilities to natural hazards by becoming more adaptable, and in particular the extent to which tiny homes could facilitate the development of adaptive communities. In doing so, three main themes were explored throughout this research: (1) tiny homes, (2) environmental adaptation and (3) community adaptability. To ensure that it is relevant and provides real value to the local community, the research draws upon the local case study of the Riverlution Tiny House Village(RTHV), an innovative community approach to adaptable, affordable, low-impact, sustainable living on margins of land which are no longer suitable for permanent housing. The main findings of the research are that Christchurch is at risk of climate change and natural hazards and it is therefore important to consider ways in which communities can stay intact and connected while adapting to the risks they face. Tiny homes provide an effective way of doing so, as they represent a tangible way that people can take adaptation into their own hands while maintaining a high-quality lifestyle.
With sea level rise (SLR) fast becoming one of the most pressing matters for governments worldwide, there has been mass amounts of research done on the impacts of SLR. However, these studies have largely focussed on the ways that SLR will impact both the natural and built environment, along with how the risk to low-lying coastal communities can be mitigated, while the inevitable impacts that this will have on mental well-being has been understudied. This research has attempted to determine the ways in which SLR can impact the mental well-being of those living in a low-lying coastal community, along with how these impacts could be mitigated while remaining adaptable to future environmental change. This was done through conducting an in-depth literature review to understand current SLR projections, the key components of mental well-being and how SLR can influence changes to mental well-being. This literature review then shaped a questionnaire which was distributed to residents of the New Brighton coastline. This questionnaire asked respondents how they interact with the local environment, how much they know about SLR and its associated hazards, whether SLR causes any level of stress or worry along with how respondents feel that these impacts could be mitigated. This research found that SLR impacts the mental well-being of those living in low-lying coastal communities through various methods: firstly, the respondents perceived risk to SLR and its associated hazards, which was found to be influenced by the suburbs that respondents live in, their knowledge of SLR, their main sources of information and the prior experience of the Canterbury Earthquake Sequence (CES). Secondly, the financial aspects of SLR were also found to be drivers of stress or worry, with depreciating property values and rising insurance premiums being frequently noted by respondents. It was found that the majority of respondents agreed that being involved in and informed of the protection process, having more readable and accurate information, and an increased engagement with community events and greenspaces would help to reduce the stress or worry caused by SLR, while remaining adaptable to future environmental change.
