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Research papers, University of Canterbury Library

In the last century, seismic design has undergone significant advancements. Starting from the initial concept of designing structures to perform elastically during an earthquake, the modern seismic design philosophy allows structures to respond to ground excitations in an inelastic manner, thereby allowing damage in earthquakes that are significantly less intense than the largest possible ground motion at the site of the structure. Current performance-based multi-objective seismic design methods aim to ensure life-safety in large and rare earthquakes, and to limit structural damage in frequent and moderate earthquakes. As a result, not many recently built buildings have collapsed and very few people have been killed in 21st century buildings even in large earthquakes. Nevertheless, the financial losses to the community arising from damage and downtime in these earthquakes have been unacceptably high (for example; reported to be in excess of 40 billion dollars in the recent Canterbury earthquakes). In the aftermath of the huge financial losses incurred in recent earthquakes, public has unabashedly shown their dissatisfaction over the seismic performance of the built infrastructure. As the current capacity design based seismic design approach relies on inelastic response (i.e. ductility) in pre-identified plastic hinges, it encourages structures to damage (and inadvertently to incur loss in the form of repair and downtime). It has now been widely accepted that while designing ductile structural systems according to the modern seismic design concept can largely ensure life-safety during earthquakes, this also causes buildings to undergo substantial damage (and significant financial loss) in moderate earthquakes. In a quest to match the seismic design objectives with public expectations, researchers are exploring how financial loss can be brought into the decision making process of seismic design. This has facilitated conceptual development of loss optimisation seismic design (LOSD), which involves estimating likely financial losses in design level earthquakes and comparing against acceptable levels of loss to make design decisions (Dhakal 2010a). Adoption of loss based approach in seismic design standards will be a big paradigm shift in earthquake engineering, but it is still a long term dream as the quantification of the interrelationships between earthquake intensity, engineering demand parameters, damage measures, and different forms of losses for different types of buildings (and more importantly the simplification of the interrelationship into design friendly forms) will require a long time. Dissecting the cost of modern buildings suggests that the structural components constitute only a minor portion of the total building cost (Taghavi and Miranda 2003). Moreover, recent research on seismic loss assessment has shown that the damage to non-structural elements and building contents contribute dominantly to the total building loss (Bradley et. al. 2009). In an earthquake, buildings can incur losses of three different forms (damage, downtime, and death/injury commonly referred as 3Ds); but all three forms of seismic loss can be expressed in terms of dollars. It is also obvious that the latter two loss forms (i.e. downtime and death/injury) are related to the extent of damage; which, in a building, will not just be constrained to the load bearing (i.e. structural) elements. As observed in recent earthquakes, even the secondary building components (such as ceilings, partitions, facades, windows parapets, chimneys, canopies) and contents can undergo substantial damage, which can lead to all three forms of loss (Dhakal 2010b). Hence, if financial losses are to be minimised during earthquakes, not only the structural systems, but also the non-structural elements (such as partitions, ceilings, glazing, windows etc.) should be designed for earthquake resistance, and valuable contents should be protected against damage during earthquakes. Several innovative building technologies have been (and are being) developed to reduce building damage during earthquakes (Buchanan et. al. 2011). Most of these developments are aimed at reducing damage to the buildings’ structural systems without due attention to their effects on non-structural systems and building contents. For example, the PRESSS system or Damage Avoidance Design concept aims to enable a building’s structural system to meet the required displacement demand by rocking without the structural elements having to deform inelastically; thereby avoiding damage to these elements. However, as this concept does not necessarily reduce the interstory drift or floor acceleration demands, the damage to non-structural elements and contents can still be high. Similarly, the concept of externally bracing/damping building frames reduces the drift demand (and consequently reduces the structural damage and drift sensitive non-structural damage). Nevertheless, the acceleration sensitive non-structural elements and contents will still be very vulnerable to damage as the floor accelerations are not reduced (arguably increased). Therefore, these concepts may not be able to substantially reduce the total financial losses in all types of buildings. Among the emerging building technologies, base isolation looks very promising as it seems to reduce both inter-storey drifts and floor accelerations, thereby reducing the damage to the structural/non-structural components of a building and its contents. Undoubtedly, a base isolated building will incur substantially reduced loss of all three forms (dollars, downtime, death/injury), even during severe earthquakes. However, base isolating a building or applying any other beneficial technology may incur additional initial costs. In order to provide incentives for builders/owners to adopt these loss-minimising technologies, real-estate and insurance industries will have to acknowledge the reduced risk posed by (and enhanced resilience of) such buildings in setting their rental/sale prices and insurance premiums.

Research papers, University of Canterbury Library

It is not a matter of if a major earthquake will happen in New Zealand, it is when. Earthquakes wreak havoc, cut off power and water supply, lines of communication, sewer, supply chains, and transport infrastructure. People get injured and whole communities can get cut off the rest of the country for extended periods of time. Countries taking measures to increase the population's preparedness tend to suffer less severe consequences than those that do not. Disaster management authorities deliver comprehensive instructions and preparation guidance, yet communities remain grossly underprepared. There are multiple factors that influence motivation for preparedness. Personal experience is one of the most significant factors that influence preparedness motivation. Not many people will experience a severe and damaging earthquake in their lifetime. A serious game (SG) that is a computer simulation of an earthquake is a tool that can let participants experience the earthquake and its aftermath from the safety of their computer. The main result of this research is a positive answer to the question: Can a serious game motivate people to prepare for earthquakes at least just as good as a personal experience of at least a moderate earthquake? There are different levels of immersion this serious game can be implemented at. In this thesis the same earthquake experience scenario – SG “ShakeUp” is implemented as a desktop application and a virtual reality (VR) application. A user study is conducted with the aim of comparing the motivation level achieved by the two versions of the SG “ShakeUp”. In this study no benefits of using VR over traditional desktop application were found: participants trying both versions of the SG “ShakeUp” reported similar levels of motivation to prepare for earthquakes immediately after the experiment. This means that both versions of the experience were equally effective in motivating participants to prepare for earthquakes. An additional benefit of this result is that the cheaper and easier to deliver desktop version can be widely used in various education campaigns. Participants reported being more motivated to prepare for earthquakes by either version of the SG “ShakeUp” than by any other contributing factor, including their previous earthquake experience or participation in a public education campaign. Both versions of the SG “ShakeUp” can successfully overcome personal bias, unrealistic optimism, pessimism, lack of perceived control over one’s earthquake preparation actions, fatalism, and sense of helplessness in the face of the earthquakes and motivate the individual to prepare for earthquakes. Participants without the prior earthquake experience benefit most from the SG “ShakeUp” regardless of the version tried, compared to the participants who had experienced an earthquake: significantly more of them will reconsider their current level of earthquake preparedness; about 24% more of them attribute their increased level of motivation to prepare for earthquakes to the SG “ShakeUp”. For every earthquake preparation action there is about 25% more people who felt motivated to do it after trying the SG “ShakeUp” than those who have done this preparation action before the experiment. After trying either version of the SG “ShakeUp”, people who live in a free standing house and those who live in a rental property reported highest levels of intent to carry on with the preparation actions. The proposed application prototype has been discussed with the University of Canterbury Earthquake Centre and received very positive feedback as having potential for practical use by various disaster management authorities and training institutions. The research shows that the SG “ShakeUp” motivates people to prepare for earthquakes as good as a personal earthquake experience and can be successfully used in various education campaigns.

Research papers, University of Canterbury Library

The increase of the world's population located near areas prone to natural disasters has given rise to new ‘mega risks’; the rebuild after disasters will test the governments’ capabilities to provide appropriate responses to protect the people and businesses. During the aftermath of the Christchurch earthquakes (2010-2012) that destroyed much of the inner city, the government of New Zealand set up a new partnership between the public and private sector to rebuild the city’s infrastructure. The new alliance, called SCIRT, used traditional risk management methods in the many construction projects. And, in hindsight, this was seen as one of the causes for some of the unanticipated problems. This study investigated the risk management practices in the post-disaster recovery to produce a specific risk management model that can be used effectively during future post-disaster situations. The aim was to develop a risk management guideline for more integrated risk management and fill the gap that arises when the traditional risk management framework is used in post-disaster situations. The study used the SCIRT alliance as a case study. The findings of the study are based on time and financial data from 100 rebuild projects, and from surveying and interviewing risk management professionals connected to the infrastructure recovery programme. The study focussed on post-disaster risk management in construction as a whole. It took into consideration the changes that happened to the people, the work and the environment due to the disaster. System thinking, and system dynamics techniques have been used due to the complexity of the recovery and to minimise the effect of unforeseen consequences. Based on an extensive literature review, the following methods were used to produce the model. The analytical hierarchical process and the relative importance index have been used to identify the critical risks inside the recovery project. System theory methods and quantitative graph theory have been used to investigate the dynamics of risks between the different management levels. Qualitative comparative analysis has been used to explore the critical success factors. And finally, causal loop diagrams combined with the grounded theory approach has been used to develop the model itself. The study identified that inexperienced staff, low management competency, poor communication, scope uncertainty, and non-alignment of the timing of strategic decisions with schedule demands, were the key risk factors in recovery projects. Among the critical risk groups, it was found that at a strategic management level, financial risks attracted the highest level of interest, as the client needs to secure funding. At both alliance-management and alliance-execution levels, the safety and environmental risks were given top priority due to a combination of high levels of emotional, reputational and media stresses. Risks arising from a lack of resources combined with the high volume of work and the concern that the cost could go out of control, alongside the aforementioned funding issues encouraged the client to create the recovery alliance model with large reputable construction organisations to lock in the recovery cost, at a time when the scope was still uncertain. This study found that building trust between all parties, clearer communication and a constant interactive flow of information, established a more working environment. Competent and clear allocation of risk management responsibilities, cultural shift, risk prioritisation, and staff training were crucial factors. Finally, the post-disaster risk management (PDRM) model can be described as an integrated risk management model that considers how the changes which happened to the environment, the people and their work, caused them to think differently to ease the complexity of the recovery projects. The model should be used as a guideline for recovery systems, especially after an earthquake, looking in detail at all the attributes and the concepts, which influence the risk management for more effective PDRM. The PDRM model is represented in Causal Loops Diagrams (CLD) in Figure 8.31 and based on 10 principles (Figure 8.32) and 26 concepts (Table 8.1) with its attributes.