This thesis examines the ground motion prediction component of earthquake loss estimation (ELE) frameworks and is based on the assertion that reducing the uncertainty in ground motion prediction will result in improved accuracy of loss estimates. The objective is to obtain improved ground motion predictions by identifying and quantifying the sources of uncertainty in the predictions, with particular focus on the portion of the uncertainty that can be reduced. The work presented in this thesis starts with an examination of ground motion measures commonly used in ELE and their relative utility. The ground motion measure Arias Intensity is identified as well-suited to application in a number of problems in earthquake engineering and this along with the lack of a robust equation for its prediction, leads to the development of a new predictive equation for Arias Intensity. Next, the prediction of Arias Intensity at spatially separated locations is studied in order to develop a model for the spatial correlation of Arias Intensity so that loss estimates for spatially distributed portfolios may be obtained. Thirdly, the sources of uncertainties in the predicted values of Arias Intensity are investigated and the uncertainties are characterised and quantified in order to establish whether or not they may be reduced. The impacts of these uncertainties on the new predictive equation for Arias Intensity are also examined. The final part of the thesis focusses on the use of GIS to display the information described in the previous sections on ground motion prediction. Particular attention is given to enhancing the display of uncertainties in ground motion predictions. This thesis demonstrates that the impacts of uncertainty on ground motion predictions and therefore earthquake loss estimation are significant, making this research of particular importance in this field.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:556587 |
| Date | January 2012 |
| Creators | Foulser-Piggott, Roxane |
| Contributors | Stafford, Peter ; Ochieng, Washington |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/9751 |
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