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Research papers, The University of Auckland Library

As damage and loss caused by natural hazards have increased worldwide over the past several decades, it is important for governments and aid agencies to have tools that enable effective post-disaster livelihood recovery to create self-sufficiency for the affected population. This study introduces a framework of critical components that constitute livelihood recovery and the critical factors that lead to people’s livelihood recovery. A comparative case study is employed in this research, combined with questionnaire surveys and interviews with those communities affected by large earthquakes in Lushan, China and in Christchurch and Kaikōura, New Zealand. In Lushan, China, a framework with four livelihood components was established, namely, housing, employment, wellbeing and external assistance. Respondents considered recovery of their housing to be the most essential element for livelihood diversification. External assistance was also rated highly in assisting with their livelihood recovery. Family ties and social connections seemed to have played a larger role than that of government agencies and NGOs. However, the recovery of livelihood cannot be fully achieved without wellbeing aspects being taken into account, and people believed that quality of life and their physical and mental health were essential for livelihood restoration. In Christchurch, New Zealand, the identified livelihood components were validated through in-depth interviews. The results showed that the above framework presenting what constitutes successful livelihood recovery could also be applied in Christchurch. This study also identified the critical factors to affect livelihood recovery following the Lushan and Kaikōura earthquakes, and these include community safety, availability of family support, level of community cohesion, long-term livelihood support, external housing recovery support, level of housing recovery and availability of health and wellbeing support. The framework developed will provide guidance for policy makers and aid agencies to prioritise their strategies and initiatives in assisting people to reinstate their livelihood in a timely manner post-disaster. It will also assist the policy makers and practitioners in China and New Zealand by setting an agenda for preparing for livelihood recovery in non-urgent times so the economic impact and livelihood disruption of those affected can be effectively mitigated.

Research papers, The University of Auckland Library

The recent instances of seismic activity in Canterbury (2010/11) and Kaikōura (2016) in New Zealand have exposed an unexpected level of damage to non-structural components, such as buried pipelines and building envelope systems. The cost of broken buried infrastructure, such as pipeline systems, to the Christchurch Council was excessive, as was the cost of repairing building envelopes to building owners in both Christchurch and Wellington (due to the Kaikōura earthquake), which indicates there are problems with compliance pathways for both of these systems. Councils rely on product testing and robust engineering design practices to provide compliance certification on the suitability of product systems, while asset and building owners rely on the compliance as proof of an acceptable design. In addition, forensic engineers and lifeline analysts rely on the same product testing and design techniques to analyse earthquake-related failures or predict future outcomes pre-earthquake, respectively. The aim of this research was to record the actual field-observed damage from the Canterbury and Kaikōura earthquakes of seismic damage to buried pipeline and building envelope systems, develop suitable testing protocols to be able to test the systems’ seismic resilience, and produce prediction design tools that deliver results that reflect the collected field observations with better accuracy than the present tools used by forensic engineers and lifeline analysts. The main research chapters of this thesis comprise of four publications that describe the gathering of seismic damage to pipes (Publication 1 of 4) and building envelopes (Publication 2 of 4). Experimental testing and the development of prediction design tools for both systems are described in Publications 3 and 4. The field observation (discussed in Publication 1 of 4) revealed that segmented pipe joints, such as those used in thick-walled PVC pipes, were particularly unsatisfactory with respect to the joint’s seismic resilience capabilities. Once the joint was damaged, silt and other deleterious material were able to penetrate the pipeline, causing blockages and the shutdown of key infrastructure services. At present, the governing Standards for PVC pipes are AS/NZS 1477 (pressure systems) and AS/NZS 1260 (gravity systems), which do not include a protocol for evaluating the PVC pipes for joint seismic resilience. Testing methodologies were designed to test a PVC pipe joint under various different simultaneously applied axial and transverse loads (discussed in Publication 3 of 4). The goal of the laboratory experiment was to establish an easy to apply testing protocol that could fill the void in the mentioned standards and produce boundary data that could be used to develop a design tool that could predict the observed failures given site-specific conditions surrounding the pipe. A tremendous amount of building envelope glazing system damage was recorded in the CBDs of both Christchurch and Wellington, which included gasket dislodgement, cracked glazing, and dislodged glazing. The observational research (Publication 2 of 4) concluded that the glazing systems were a good indication of building envelope damage as the glazing had consistent breaking characteristics, like a ballistic fuse used in forensic blast analysis. The compliance testing protocol recognised in the New Zealand Building Code, Verification Method E2/VM1, relies on the testing method from the Standard AS/NZS 4284 and stipulates the inclusion of typical penetrations, such as glazing systems, to be included in the test specimen. Some of the building envelope systems that failed in the recent New Zealand earthquakes were assessed with glazing systems using either the AS/NZS 4284 or E2/VM1 methods and still failed unexpectedly, which suggests that improvements to the testing protocols are required. An experiment was designed to mimic the observed earthquake damage using bi-directional loading (discussed in Publication 4 of 4) and to identify improvements to the current testing protocol. In a similar way to pipes, the observational and test data was then used to develop a design prediction tool. For both pipes (Publication 3 of 4) and glazing systems (Publication 4 of 4), experimentation suggests that modifying the existing testing Standards would yield more realistic earthquake damage results. The research indicates that including a specific joint testing regime for pipes and positioning the glazing system in a specific location in the specimen would improve the relevant Standards with respect to seismic resilience of these systems. Improving seismic resilience in pipe joints and glazing systems would improve existing Council compliance pathways, which would potentially reduce the liability of damage claims against the government after an earthquake event. The developed design prediction tool, for both pipe and glazing systems, uses local data specific to the system being scrutinised, such as local geology, dimensional characteristics of the system, actual or predicted peak ground accelerations (both vertically and horizontally) and results of product-specific bi-directional testing. The design prediction tools would improve the accuracy of existing techniques used by forensic engineers examining the cause of failure after an earthquake and for lifeline analysts examining predictive earthquake damage scenarios.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.