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Research papers, University of Canterbury Library

This dissertation addresses several fundamental and applied aspects of ground motion selection for seismic response analyses. In particular, the following topics are addressed: the theory and application of ground motion selection for scenario earthquake ruptures; the consideration of causal parameter bounds in ground motion selection; ground motion selection in the near-fault region where directivity effect is significant; and methodologies for epistemic uncertainty consideration and propagation in the context of ground motion selection and seismic performance assessment. The paragraphs below outline each contribution in more detail. A scenario-based ground motion selection method is presented which considers the joint distribution of multiple intensity measure (IM) types based on the generalised conditional intensity measure (GCIM) methodology (Bradley, 2010b, 2012c). The ground motion selection algorithm is based on generating realisations of the considered IM distributions for a specific rupture scenario and then finding the prospective ground motions which best fit the realisations using an optimal amplitude scaling factor. In addition, using different rupture scenarios and site conditions, two important aspects of the GCIM methodology are scrutinised: (i) different weight vectors for the various IMs considered; and (ii) quantifying the importance of replicate selections for ensembles with different numbers of desired ground motions. As an application of the developed scenario-based ground motion selection method, ground motion ensembles are selected to represent several major earthquake scenarios in New Zealand that pose a significant seismic hazard, namely, Alpine, Hope and Porters Pass ruptures for Christchurch city; and Wellington, Ohariu, and Wairarapa ruptures for Wellington city. A rigorous basis is developed, and sensitivity analyses performed, for the consideration of bounds on causal parameters (e.g., magnitude, source-to-site distance, and site condition) for ground motion selection. The effect of causal parameter bound selection on both the number of available prospective ground motions from an initial empirical as-recorded database, and the statistical properties of IMs of selected ground motions are examined. It is also demonstrated that using causal parameter bounds is not a reliable approach to implicitly account for ground motion duration and cumulative effects when selection is based on only spectral acceleration (SA) ordinates. Specific causal parameter bounding criteria are recommended for general use as a ‘default’ bounding criterion with possible adjustments from the analyst based on problem-specific preferences. An approach is presented to consider the forward directivity effects in seismic hazard analysis, which does not separate the hazard calculations for pulse-like and non-pulse-like ground motions. Also, the ability of ground motion selection methods to appropriately select records containing forward directivity pulse motions in the near-fault region is examined. Particular attention is given to ground motion selection which is explicitly based on ground motion IMs, including SA, duration, and cumulative measures; rather than a focus on implicit parameters (i.e., distance, and pulse or non-pulse classifications) that are conventionally used to heuristically distinguish between the near-fault and far-field records. No ad hoc criteria, in terms of the number of directivity ground motions and their pulse periods, are enforced for selecting pulse-like records. Example applications are presented with different rupture characteristics, source-to-site geometry, and site conditions. It is advocated that the selection of ground motions in the near-fault region based on IM properties alone is preferred to that in which the proportion of pulse-like motions and their pulse periods are specified a priori as strict criteria for ground motion selection. Three methods are presented to propagate the effect of seismic hazard and ground motion selection epistemic uncertainties to seismic performance metrics. These methods differ in their level of rigor considered to propagate the epistemic uncertainty in the conditional distribution of IMs utilised in ground motion selection, selected ground motion ensembles, and the number of nonlinear response history analyses performed to obtain the distribution of engineering demand parameters. These methods are compared for an example site where it is observed that, for seismic demand levels below the collapse limit, epistemic uncertainty in ground motion selection is a smaller uncertainty contributor relative to the uncertainty in the seismic hazard itself. In contrast, uncertainty in ground motion selection process increases the uncertainty in the seismic demand hazard for near-collapse demand levels.

Research papers, University of Canterbury Library

SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database.

Research papers, University of Canterbury Library

Voluntary turnover has been the subject of scholarly inquiry for more than 100 years and much is understood about the drivers of turnover, and the decision-making processes involved. To date most models of voluntary turnover have assumed a rational and sequential decision process, initiated primarily by dissatisfaction with the job and the perceived availability of alternatives. Operating within a strong predictive research agenda, countless studies have sought to validate, extend and refine these traditional models through the addition of distal antecedents, mediators, moderators, and proximal antecedents of turnover. The net result of this research is a large body of empirical support for a somewhat modest relationship between job dissatisfaction, perceived alternatives, turnover intentions, job search behaviour and actual turnover. Far less scholarly attention has been directed at understanding shock-induced turnover that is not necessarily derived from dissatisfaction. Moreover, almost no consideration has been given to understanding how a significant and commonly experienced extra-organisational shock, such as natural disaster, might impact turnover decision making. Additionally, the dynamic and cumulative impacts of multiple shocks on turnover decision making have to date not been examined by turnover researchers. In addressing these gaps this thesis presents a leaver-centric theory of employee turnover decision making that is grounded in the post-disaster context. Data for the study were collected from in-depth interviews with 31 leavers in four large organisations in Christchurch, New Zealand; an area that experienced a major natural disaster in the form of the Canterbury earthquake sequence. This context provided a unique setting in which to study turnover as the primary shock was followed by a series of smaller shocks, resulting in a period of sustained disruption to the pre-shock status quo. Grounded theory methods are used to develop a typology of leaving which describes four distinct patterns of turnover decision making that follow a significant extra-organisational shock. The proposed typology not only addresses the heterogeneous and complex nature of turnover decision making, but also provides a more nuanced explanation of the turnover process explicating how the choice of decision path followed is influenced by four contextual factors which emerged from the data: (1) pre-shock motivational state; (2) decision difficulty; (3) experienced shock magnitude; and (4) the availability of resources. The research findings address several shortcomings in the extant literature on employee turnover, and offer practical recommendations for managers seeking to retain employees in a post-disaster setting.