Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
Disasters are rare events with major consequences; yet comparatively little is known about managing employee needs in disaster situations. Based on case studies of four organisations following the devastating earthquakes of 2010 - 2011 in Christchurch, New Zealand, this paper presents a framework using redefined notions of employee needs and expectations, and charting the ways in which these influence organisational recovery and performance. Analysis of in-depth interview data from 47 respondents in four organisations highlighted the evolving nature of employee needs and the crucial role of middle management leadership in mitigating the effects of disasters. The findings have counterintuitive implications for human resource functions in a disaster, suggesting that organisational justice forms a central framework for managing organisational responses to support and engage employees for promoting business recovery.
The author followed five primary (elementary) schools over three years as they responded to and began to recover from the 2010–2011 earthquakes in and around the city of Christchurch in the Canterbury region of New Zealand. The purpose was to capture the stories for the schools themselves, their communities, and for New Zealand’s historical records. From the wider study, data from the qualitative interviews highlighted themes such as children’s responses or the changing roles of principals and teachers. The theme discussed in this article, however, is the role that schools played in the provision of facilities and services to meet (a) physical needs (food, water, shelter, and safety); and (b) emotional, social, and psychological needs (communication, emotional support, psychological counseling, and social cohesion)—both for themselves and their wider communities. The role schools played is examined across the immediate, short-, medium-, and long-term response periods before being discussed through a social bonding theoretical lens. The article concludes by recommending stronger engagement with schools when considering disaster policy, planning, and preparation http://www.schoolcommunitynetwork.org/SCJ.aspx
INTRODUCTION This project falls under the Flagship 3: Wellington Coordinated Project. It supports other projects within FP3 to create a holistic understanding of risks posed by collapsed buildings due to future earthquake/s and the secondary consequences of cordoning in the short, mid and long term. Cordoning of the Christchurch CBD for more than two years and its subsequent implications on people and businesses had a significant impact on the recovery of Christchurch. Learning from this and experiences from the Kaikōura earthquake (where cordons were also established around selected buildings, Figure 3) have highlighted the need to understand the effects of cordons and plan for it before an earthquake occurs
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.
In 2013 Becca Wood, Spatial Performance Practitioner, and Molly Mullen, Applied Theatre Practitioner, collaborated to create a short ambulatory performance with audio score for a group of drama educators attending a conference workshop on the possibilities of walking as performance. The performance was created remotely from the intended site: Rangi Ruru Girls’ School, in Christchurch, New Zealand. Following the destruction of the 2012 earthquake, this site was in a state of transformation and recovery. The performance walk attended to the histories, geographies and politics of this place, somatically, architecturally and socially. This paper engages with three critical questions: How might mediated listening and walking activate the coming together of bodies and place? What performative shifts occurred for the participants in the walk and workshop? How might we come to our senses? Through a performative practice of mediated site-based listening and walking, this paper is a reflection on the creative process and performance. We consider the potential for technologically mediated performance to offer new modes for learning and creative practice through interdisciplinary and evolving intermedial practices. http://www.tandfonline.com/toc/crde20/current AM - Accepted Manuscript
Developing a holistic understanding of social, cultural, and economic impacts of disasters can help in building disaster risk knowledge for policy making and planning. Many methods can help in developing an understanding of the impacts of a disaster, including interviews and surveys with people who have experienced disaster, which may be invasive at times and create stress for the participants to relive their experiences. In the past decade, social media, blog posts, video blogs (i.e. “vlogs”), and crowdsourcing mechanisms such as Humanitarian OpenStreetMap and Ushahidi, have become prominent platforms for people to share their experiences and impacts of an event from the ground. These platforms allow for the discovery of a range of impact information, from physical impacts, to social, cultural, and psychological impacts. It can also reveal interesting behavioural information such as their decision to heed a warning or not, as people tend to share their experiences and their reactions online. This information can help researchers and authorities understand both the impacts as well as behavioural responses to hazards, which can then shape how early warning systems are designed and delivered. It can also help to identify gaps in desired behavioural responses. This poster presents a selection of cases identified from the literature and grey literature, such as the Haiti earthquake, the Christchurch earthquake, Hurricane Sandy, and Hurricane Harvey, where online platforms were widely used during and after a disaster to document impacts, experiences, and behavioural responses. A summary of key learnings and areas for future research is provided.
The Canterbury region of New Zealand experienced a sequence of strong earthquakes during 2010-2011. Responses included government acquisition of many thousands of residential properties in the city of Christchurch in areas with severe earthquake effects. A large and contiguous tract of this ‘red zoned’ land lies in close proximity to the Ōtākaro / Avon River and is known as the Avon-Ōtākaro Red Zone (AORZ). The focus of this study was to provide an overview of the floodplain characteristics of the AORZ and review of international experience in ecological restoration of similar river margin and floodplain ecosystems to extract restoration principles and associated learnings. Compared to pre-earthquake ground levels, the dominant trend in the AORZ is subsidence, together with lateral movement especially in the vicinity of waterway. An important consequence of land subsidence in the lower Ōtākaro / Avon River is greater exposure to flooding and the effects of sea level rise. Scenario modelling for sea level rise indicates that much of the AORZ is exposed to inundation within a 100 year planning horizon based on a 1 m sea level rise. As with decisions on built infrastructure, investments in nature-based ‘green infrastructure’ also require a sound business case including attention to risks posed by climate change. Future-proofing of the expected benefits of ecological restoration must therefore be secured by design. Understanding and managing the hydrology and floodplain dynamics are vital to the future of the AORZ. However, these characteristics are shared by other floodplain and river restoration projects worldwide. Identifying successful approaches provides a useful a source of useful information for floodplain planning in the AORZ. This report presents results from a comparative case study of three international examples to identify relevant principles for large-scale floodplain management at coastal lowland sites.
Following the Canterbury earthquake sequence of 2010-11, a large and contiguous tract of vacated ‘red zoned’ land lies alongside the lower Ōtākaro / Avon River and is known as the Avon-Ōtākaro Red Zone (AORZ). This is the second report in the Ecological Regeneration Options (ERO) project that addresses future land uses in the AORZ. The purpose of this report is to present results from an assessment of restoration opportunities conducted in April 2017. The objectives of the assessment were to identify potential benefits of ecological restoration activities across both land and water systems in the AORZ and characterise the key options for their implementation. The focus of this report is not to provide specific advice on the methods for achieving specific restoration endpoints per se. This will vary at different sites and scales with a large number of combinations possible. Rather, the emphasis is on providing an overview of the many restoration and regeneration options in their totality across the AORZ. An additional objective is to support their adequate assessment in the identification of optimum land uses and adaptive management practices for the AORZ. Participatory processes may play a useful role in assessment and stakeholder engagement by providing opportunities for social learning and the co-creation of new knowledge. We used a facilitated local knowledge based approach that generated a large quantity of reliable and site specific data in a short period of time. By inviting participation from a wide knowledge-holder network inclusivity is improved in comparison to small-group expert panel approaches. Similar approaches could be applied to other information gathering and assessment needs in the regeneration planning process. Findings from this study represent the most comprehensive set of concepts available to date to address the potential benefits of ecological regeneration in the AORZ. This is a core topic for planning to avoid missed opportunities and opportunity costs. The results identify a wide range of activities that may be applied to generate benefits for Christchurch and beyond, all involving aspects of a potential new ecology in the AORZ. These may be combined at a range of scales to create scenarios, quantify benefits, and explore the potential for synergies between different land use options. A particular challenge is acquiring the information needed within relatively short time frames. Early attention to gathering baseline data, addressing technical knowledge gaps, and developing conceptual frameworks to account for the many spatio-temporal aspects are all key activities that will assist in delivering the best outcomes. Methodologies by which these many facets can be pulled together in quantitative and comparative assessments are the focus of the final report in the ERO series.
This thesis describes the management process of innovation through construction infrastructure projects. This research focuses on the innovation management process at the project level from four views. These are categorised into the separate yet related areas of: “innovation definition”, “Project time”, “project team motivation” and “Project temporary organisation”. A practical knowledge is developed for each of these research areas that enables project practitioners to make the best decision for the right type of innovation at the right phase of projects, through a capable project organisation. The research developed a holistic view on both innovation and the construction infrastructure project as two complex phenomena. An infrastructure project is a long-term capital investment, highly risky and an uncertain. Infrastructure projects can play a key role in innovation and performance improvement throughout the construction industry. The delivery of an infrastructure project is affected in most cases by critical issues of budget constraint, programme delays and safety Where the business climate is characterized by uncertainty, risk and a high level of technological change, construction infrastructure projects are unable to cope with the requirement to develop innovation. Innovation in infrastructure projects, as one of the key performance indicators (KPI) has been identified as a critical capability for performance improvement through the industry. However, in spite of the importance of infrastructure projects in improving innovation, there are a few research efforts that have developed a comprehensive view on the project context and its drivers and inhibitors for innovation in the construction industry. Two main reasons are given as the inhibitors through the process of comprehensive research on innovation management in construction. The first reason is the absence of an understanding of innovation itself. The second is a bias towards research at a firm and individual level, so a comprehensive assessment of project-related factors and their effects on innovation in infrastructure projects has not been undertaken. This study overcomes these issues by adopting as a case study approach of a successful infrastructure project. This research examines more than 500 construction innovations generated by a unique infrastructure alliance. SCIRT (Stronger Christchurch Infrastructure Rebuild Team) is a temporary alliancing organisation that was created to rebuild and recover the damaged infrastructure after the Christchurch 2011 earthquake. Researchers were given full access to the innovation project information and innovation systems under a contract with SCIRT Learning Legacy, provided the research with material which is critical for understanding innovations in large, complex alliancing infrastructure organisation. In this research, an innovation classification model was first constructed. Clear definitions have been developed for six types of construction innovation with a variety of level of novelties and benefits. The innovation classification model was applied on the SCIRT innovation database and the resultant trends and behaviours of different types of innovation are presented. The trends and behaviours through different types of SCIRT innovations developed a unique opportunity to research the projectrelated factors and their effect on the behaviour of different classified types of innovation throughout the project’s lifecycle. The result was the identification of specific characteristics of an infrastructure project that affect the innovation management process at the project level. These were categorised in four separate chapters. The first study presents the relationship between six classified types of innovation, the level of novelty and the benefit they come up with, by applying the innovation classification model on SCIRT innovation database. The second study focused on the innovation potential and limitations in different project lifecycle phases by using a logic relationship between the six classified types of innovation and the three classified phases of the SCIRT project. The third study result develops a holistic view of different elements of the SCIRT motivation system and results in a relationship between the maturity level of definition developed for innovation as one of the KPIs and a desire though the SCIRT innovation incentive system to motivate more important innovations throughout the project. The fourth study is about the role of the project’s temporary organisation that finally results in a multiple-view innovation model being developed for project organisation capability assessment in the construction industry. The result of this thesis provides practical and instrumental knowledge to be used by a project practitioner. Benefits of the current thesis could be categorized in four groups. The first group is the innovation classification model that provides a clear definition for six classified types of innovation with four levels of novelty and specifically defined outcomes and the relationship between the innovation types, novelty and benefit. The second is the ability that is provided for the project practitioner to make the best decision for the right type of innovation at the right phases of a project’s lifecycle. The third is an optimisation that is applied on the SCIRT innovation motivation system that enables the project practitioner to incentivize the right type of innovation with the right level of financial gain. This drives the project teams to develop a more important innovation instead of a simple problemsolving one. Finally, the last and probably more important benefit is the recommended multiple-view innovation model. This is a tool that could be used by a project practitioner in order to empower the project team to support innovation throughout the project.
