The initial goal of this research was to explore how SME business models change in response to a crisis. Keeping this in mind, the business model canvas (Osterwalder & Pigneur, 2010) was used as a tool to analyse SME business models in the Canterbury region of New Zealand. The purpose was to evaluate the changes SMEs instituted in their business models after being hit by a series of earthquakes in 2010 and 2011. The idea was to conduct interviews with business owners and analyse them using grounded theory methods. As this method is iterative and requires simultaneous data collection and analysis, a tentative model was proposed after first phase of the data collection and analysis. However, as a result of this process, it became apparent that owner-specific characteristics, action orientation and networks were more prominent in the data than business model elements. Although the SMEs in this study experienced several operational changes in their business models, such as a change of location, modifications to their payment terms or expanded/restricted target markets, the suggested framework highlights how owner-specific attributes ensured the recovery of their businesses. After the initial framework was suggested, subsequent interviews were conducted to test, verify, and modify the tentative model. Three aspects of business recovery emerged: (a) cognitive coping – the business owner’s mind-set and motive; (b) adaptive coping – the ability of business owner to take corrective actions; and (c) social capital – the social network of a business owner, including formal and informal connections and their significance. Three distinct groups were identified; self-sufficient SMEs, socially-based SMEs and surviving SMEs. This thesis proposes a grounded theory of business recovery for SMEs following a disaster. Cognitive coping and social capital enabled the owners to take actions, which eventually led to the desired outcomes for the businesses.
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
This paper presents an examination of ground motion observations from 20 near-source strong motion stations during the most significant 10 events in the 2010-2011 Canterbury earthquake to examine region-specific systematic effects based on relaxing the conventional ergodic assumption. On the basis of similar site-to-site residuals, surfical geology, and geographical proximity, 15 of the 20 stations are grouped into four sub-regions: the Central Business District; and Western, Eastern, and Northern suburbs. Mean site-to-site residuals for these sub-regions then allows for the possibility of non-ergodic ground motion prediction over these sub-regions of Canterbury, rather than only at strong motion station locations. The ratio of the total non-ergodic vs. ergodic standard deviation is found to be, on average, consistent with previous studies, however it is emphasized that on a site-by-site basis the non-ergodic standard deviation can easily vary by ±20%.
In 2010 and 2011 Christchurch, New Zealand experienced a series of earthquakes that caused extensive damage across the city, but primarily to the Central Business District (CBD) and eastern suburbs. A major feature of the observed damage was extensive and severe soil liquefaction and associated ground damage, affecting buildings and infrastructure. The behaviour of soil during earthquake loading is a complex phenomena that can be most comprehensively analysed through advanced numerical simulations to aid engineers in the design of important buildings and critical facilities. These numerical simulations are highly dependent on the capabilities of the constitutive soil model to replicate the salient features of sand behaviour during cyclic loading, including liquefaction and cyclic mobility, such as the Stress-Density model. For robust analyses advanced soil models require extensive testing to derive engineering parameters under varying loading conditions for calibration. Prior to this research project little testing on Christchurch sands had been completed, and none from natural samples containing important features such as fabric and structure of the sand that may be influenced by the unique stress-history of the deposit. This research programme is focussed on the characterisation of Christchurch sands, as typically found in the CBD, to facilitate advanced soil modelling in both res earch and engineering practice - to simulate earthquake loading on proposed foundation design solutions including expensive ground improvement treatments. This has involved the use of a new Gel Push (GP) sampler to obtain undisturbed samples from below the ground-water table. Due to the variable nature of fluvial deposition, samples with a wide range of soil gradations, and accordingly soil index properties, were obtained from the sampling sites. The quality of the samples is comprehensively examined using available data from the ground investigation and laboratory testing. A meta-quality assessment was considered whereby a each method of evaluation contributed to the final quality index assigned to the specimen. The sampling sites were characterised with available geotechnical field-based test data, primarily the Cone Penetrometer Test (CPT), supported by borehole sampling and shear-wave velocity testing. This characterisation provides a geo- logical context to the sampling sites and samples obtained for element testing. It also facilitated the evaluation of sample quality. The sampling sites were evaluated for liquefaction hazard using the industry standard empirical procedures, and showed good correlation to observations made following the 22 February 2011 earthquake. However, the empirical method over-predicted liquefaction occurrence during the preceding 4 September 2010 event, and under-predicted for the subsequent 13 June 2011 event. The reasons for these discrepancies are discussed. The response of the GP samples to monotonic and cyclic loading was measured in the laboratory through triaxial testing at the University of Canterbury geomechanics laboratory. The undisturbed samples were compared to reconstituted specimens formed in the lab in an attempt to quantify the effect of fabric and structure in the Christchurch sands. Further testing of moist tamped re- constituted specimens (MT) was conducted to define important state parameters and state-dependent properties including the Critical State Line (CSL), and the stress-strain curve for varying state index. To account for the wide-ranging soil gradations, selected representative specimens were used to define four distinct CSL. The input parameters for the Stress-Density Model (S-D) were derived from a suite of tests performed on each representative soil, and with reference to available GP sample data. The results of testing were scrutinised by comparing the data against expected trends. The influence of fabric and structure of the GP samples was observed to result in similar cyclic strength curves at 5 % Double Amplitude (DA) strain criteria, however on close inspection of the test data, clear differences emerged. The natural samples exhibited higher compressibility during initial loading cycles, but thereafter typically exhibited steady growth of plastic strain and excess pore water pressure towards and beyond the strain criteria and initial liquefaction, and no flow was observed. By contrast the reconstituted specimens exhibited a stiffer response during initial loading cycles, but exponential growth in strains and associated excess pore water pressure beyond phase-transformation, and particularly after initial liquefaction where large strains were mobilised in subsequent cycles. These behavioural differences were not well characterised by the cyclic strength curve at 5 % DA strain level, which showed a similar strength for both GP samples and MT specimens. A preliminary calibration of the S-D model for a range of soil gradations is derived from the suite of laboratory test data. Issues encountered include the influence of natural structure on the peak-strength–state index relationship, resulting in much higher peak strengths than typically observed for sands in the literature. For the S-D model this resulted in excessive stiffness to be modelled during cyclic mobility, when the state index becomes large momentarily, causing strain development to halt. This behaviour prevented modelling the observed re- sponse of silty sands to large strains, synonymous with “liquefaction”. Efforts to reduce this effect within the current formulation are proposed as well as future research to address this issue.
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.
The UC CEISMIC Canterbury Earthquakes Digital Archive was built following the devastating earthquakes that hit the Canterbury region in the South Island of New Zealand from 2010 – 2012. 185 people were killed in the 6.3 magnitude earthquake of February 22nd 2011, thousands of homes and businesses were destroyed, and the local community endured over 10,000 aftershocks. The program aims to document and protect the social, cultural, and intellectual legacy of the Canterbury community for the purposes of memorialization and enabling research. The nationally federated archive currently stores 75,000 items, ranging from audio and video interviews to images and official reports. Tens of thousands more items await ingestion. Significant lessons have been learned about data integration in post-disaster contexts, including but not limited to technical architecture, governance, ingestion process, and human ethics. The archive represents a model for future resilience-oriented data integration and preservation products.
When disasters and crises, both man-made and natural, occur, resilient higher education institutions adapt in order to continue teaching and research. This may necessitate the closure of the whole institution, a building and/or other essential infrastructure. In disasters of large scale the impact can be felt for many years. There is an increasing recognition of the need for disaster planning to restructure educational institutions so that they become more resilient to challenges including natural disasters (Seville, Hawker, & Lyttle, 2012).The University of Canterbury (UC) was affected by seismic events that resulted in the closure of the University in September 2010 for 10 days and two weeks at the start of the 2011 academic year This case study research describes ways in which e-learning was deployed and developed by the University to continue and even to improve learning and teaching in the aftermath of a series of earthquakes in 2010 and 2011. A qualitative intrinsic embedded/nested single case study design was chosen for the study. The population was the management, support staff and educators at the University of Canterbury. Participants were recruited with purposive sampling using a snowball strategy where the early key participants were encouraged to recommend further participants. Four sources of data were identified: (1) documents such as policy, reports and guidelines; (2) emails from leaders of the colleges and academics; (3) communications from senior management team posted on the university website during and after the seismic activity of 2010 and 2011; and (4) semi-structured interviews of academics, support staff and members of senior management team. A series of inductive descriptive content analyses identified a number of themes in the data. The Technology Acceptance Model 2 (Venkatesh & Davis, 2000) and the Indicator of Resilience Model (Resilient Organisations, 2012) were used for additional analyses of each of the three cases. Within the University case, the cases of two contrasting Colleges were embedded to produce a total of three case studies describing e-learning from 2000 - 2014. One contrast was the extent of e-learning deployment at the colleges: The College of Education was a leader in the field, while the College of Business and Law had relatively little e-learning at the time of the first earthquake in September 2010. The following six themes emerged from the analyses: Communication about crises, IT infrastructure, Availability of e-learning technologies, Support in the use of e-learning technologies, Timing of crises in academic year and Strategic planning for e-learning. One of the findings confirmed earlier research that communication to members of an organisation and the general public about crises and the recovery from crises is important. The use of communication channels, which students were familiar with and already using, aided the dissemination of the information that UC would be using e-learning as one of the options to complete the academic year. It was also found that e-learning tools were invaluable during the crises and facilitated teaching and learning whilst freeing limited campus space for essential activities and that IT infrastructure was essential to e-learning. The range of e-learning tools and their deployment evolved over the years influenced by repeated crises and facilitated by the availability of centrally located support from the e-Learning support team for a limited set of tools, as well as more localised support and collaboration with colleagues. Furthermore, the reasons and/or rate of e-learning adoption in an educational institution during crises varied with the time of the academic year and the needs of the institution at the time. The duration of the crises also affected the adoption of e-learning. Finally, UC’s lack of an explicit e-learning strategy influenced the two colleges to develop college-specific e-learning plans and those College plans complemented the incorporation of e-learning for the first time in the University’s teaching and learning strategy in 2013. Twelve out of the 13 indicators of the Indicators of Resilience Model were found in the data collected for the study and could be explained using the model; it revealed that UC has become more resilient with e-learning in the aftermath of the seismic activities in 2010 and 2011. The interpretation of the results using TAM2 demonstrated that the adoption of technologies during crises aided in overcoming barriers to learning at the time of the crisis. The recommendations from this study are that in times of crises, educational institutions take advantage of Cloud computing to communicate with members of the institution and stakeholders. Also, that the architecture of a university’s IT infrastructure be made more resilient by increasing redundancy, backup and security, centralisation and Cloud computing. In addition, when under stress it is recommended that new tools are only introduced when they are essential.
Recently developed performance-based earthquake engineering framework, such as one provided by PEER (Deierlein et al. 2003), assist in the quantification in terms of performance such as casualty, monetary losses and downtime. This opens up the opportunity to identify cost-effective retrofit/rehabilitation strategies by comparing upfront costs associated with retrofit with the repair costs that can be expected over time. This loss assessment can be strengthened by learning from recent earthquakes, such as the 2010 Canterbury and 2016 Kaikoura earthquakes. In order to investigate which types of retrofit/rehabilitation strategies may be most cost-effective, a case study building was chosen for this research. The Pacific Tower, a 22-storey EBF apartment located within the Christchurch central business district (CBD), was damaged and repaired during the 2010 Canterbury earthquake series. As such, by taking hazard levels accordingly (i.e. to correspond to the Christchurch CBD), modelling and analysing the structure, and considering the vulnerability and repair costs of its different components, it is possible to predict the expected losses of the aforementioned building. Using this information, cost-effective retrofit/rehabilitation strategy can be determined. This research found that more often than not, it would be beneficial to improve the performance of valuable non-structural components, such as partitions. Although it is true that improving such elements will increase the initial costs, over time, the benefits gained from reduced losses should be expected to overcome the initial costs. Aftershocks do increase the predicted losses of a building even in lower intensities due to the fact that non-structural components can get damaged at such low intensities. By comparing losses computed with and without consideration of aftershocks for a range of historical earthquakes, it was found that the ratio between losses due to main shock with aftershocks to the losses due to the main shock only tended to increase with increasing main shock magnitude. This may be due to the fact that larger magnitude earthquakes tend to generate larger magnitude aftershocks and as those aftershocks happen within a region around the main shock, they are more likely to cause intense shaking and additional damage. In addition to this observation, it was observed that the most significant component of loss of the case study building was the non-structural partition walls.
Between 2010 and 2011, Canterbury experienced a series of four large earthquake events with associated aftershocks which caused widespread damage to residential and commercial infrastructure. Fine grained and uncompacted alluvial soils, typical to the Canterbury outwash plains, were exposed to high peak ground acceleration (PGA) during these events. This rapid increase in PGA induced cyclic strain softening and liquefaction in the saturated, near surface alluvial soils. Extensive research into understanding the response of soils in Canterbury to dynamic loading has since occurred. The Earthquake Commission (EQC), the Ministry of Business and Employment (MBIE), and the Christchurch City Council (CCC) have quantified the potential hazards associated with future seismic events. Theses bodies have tested numerous ground improvement design methods, and subsequently are at the forefront of the Canterbury recovery and rebuild process. Deep Soil Mixing (DSM) has been proven as a viable ground improvement foundation method used to enhance in situ soils by increasing stiffness and positively altering in situ soil characteristics. However, current industry practice for confirming the effectiveness of the DSM method involves specific laboratory and absolute soil test methods associated with the mixed column element itself. Currently, the response of the soil around the columns to DSM installation is poorly understood. This research aims to understand and quantify the effects of DSM columns on near surface alluvial soils between the DSM columns though the implementation of standardised empirical soil test methods. These soil strength properties and ground improvement changes have been investigated using shear wave velocity (Vs), soil behaviour and density response methods. The results of the three different empirical tests indicated a consistent improvement within the ground around the DSM columns in sandier soils. By contrast, cohesive silty soils portrayed less of a consistent response to DSM, although still recorded increases. Generally, within the tests completed 50 mm from the column edge, the soil response indicated a deterioration to DSM. This is likely to be a result of the destruction of the soil fabric as the stress and strain of DSM is applied to the un‐mixed in situ soils. The results suggest that during the installation of DSM columns, a positive ground effect occurs in a similar way to other methods of ground improvement. However, further research, including additional testing following this empirical method, laboratory testing and finite 2D and 3D modelling, would be useful to quantify, in detail, how in situ soils respond and how practitioners should consider these test results in their designs. This thesis begins to evaluate how alluvial soils tend to respond to DSM. Conducting more testing on the research site, on other sites in Christchurch, and around the world, would provide a more complete data set to confirm the results of this research and enable further evaluation. Completing this additional research could help geotechnical DSM practitioners to use standardised empirical test methods to measure and confirm ground improvement rather than using existing test methods in future DSM projects. Further, demonstrating the effectiveness of empirical test methods in a DSM context is likely to enable more cost effective and efficient testing of DSM columns in future geotechnical projects.
Sewerage systems convey sewage, or wastewater, from residential or commercial buildings through complex reticulation networks to treatment plants. During seismic events both transient ground motion and permanent ground deformation can induce physical damage to sewerage system components, limiting or impeding the operability of the whole system. The malfunction of municipal sewerage systems can result in the pollution of nearby waterways through discharge of untreated sewage, pose a public health threat by preventing the use of appropriate sanitation facilities, and cause serious inconvenience for rescuers and residents. Christchurch, the second largest city in New Zealand, was seriously affected by the Canterbury Earthquake Sequence (CES) in 2010-2011. The CES imposed widespread damage to the Christchurch sewerage system (CSS), causing a significant loss of functionality and serviceability to the system. The Christchurch City Council (CCC) relied heavily on temporary sewerage services for several months following the CES. The temporary services were supported by use of chemical and portable toilets to supplement the damaged wastewater system. The rebuild delivery agency -Stronger Christchurch Infrastructure Rebuild Team (SCIRT) was created to be responsible for repair of 85 % of the damaged horizontal infrastructure (i.e., water, wastewater, stormwater systems, and roads) in Christchurch. Numerous initiatives to create platforms/tools aiming to, on the one hand, support the understanding, management and mitigation of seismic risk for infrastructure prior to disasters, and on the other hand, to support the decision-making for post-disaster reconstruction and recovery, have been promoted worldwide. Despite this, the CES in New Zealand highlighted that none of the existing platforms/tools are either accessible and/or readable or usable by emergency managers and decision makers for restoring the CSS. Furthermore, the majority of existing tools have a sole focus on the engineering perspective, while the holistic process of formulating recovery decisions is based on system-wide approach, where a variety of factors in addition to technical considerations are involved. Lastly, there is a paucity of studies focused on the tools and frameworks for supporting decision-making specifically on sewerage system restoration after earthquakes. This thesis develops a decision support framework for sewerage pipe and system restoration after earthquakes, building on the experience and learning of the organisations involved in recovering the CSS following the CES in 2010-2011. The proposed decision support framework includes three modules: 1) Physical Damage Module (PDM); 2) Functional Impact Module (FIM); 3) Pipeline Restoration Module (PRM). The PDM provides seismic fragility matrices and functions for sewer gravity and pressure pipelines for predicting earthquake-induced physical damage, categorised by pipe materials and liquefaction zones. The FIM demonstrates a set of performance indicators that are categorised in five domains: structural, hydraulic, environmental, social and economic domains. These performance indicators are used to assess loss of wastewater system service and the induced functional impacts in three different phases: emergency response, short-term recovery and long-term restoration. Based on the knowledge of the physical and functional status-quo of the sewerage systems post-earthquake captured through the PDM and FIM, the PRM estimates restoration time of sewer networks by use of restoration models developed using a Random Forest technique and graphically represented in terms of restoration curves. The development of a decision support framework for sewer recovery after earthquakes enables decision makers to assess physical damage, evaluate functional impacts relating to hydraulic, environmental, structural, economic and social contexts, and to predict restoration time of sewerage systems. Furthermore, the decision support framework can be potentially employed to underpin system maintenance and upgrade by guiding system rehabilitation and to monitor system behaviours during business-as-usual time. In conjunction with expert judgement and best practices, this framework can be moreover applied to assist asset managers in targeting the inclusion of system resilience as part of asset maintenance programmes.
