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.
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.