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Details of Grant 

EPSRC Reference: GR/R94602/01
Title: Stochastic modelling of fractures in rock masses
Principal Investigator: Fowell, Dr RJ
Other Investigators:
Mardia, Professor K
Researcher Co-Investigators:
Project Partners:
Farmer,Ian,Associates Ltd Industrial Tomography Systems plc Nimbus Diamond Tool & Machine Co Ltd
Department: Mining and Mineral Engineering
Organisation: University of Leeds
Scheme: Standard Research (Pre-FEC)
Starts: 17 June 2002 Ends: 16 September 2005 Value (£): 262,498
EPSRC Research Topic Classifications:
Statistics & Appl. Probability Waste Management
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Mar 2001 MARCH 2001 Mathematics Responsive Mode Deferred
Summary on Grant Application Form
The context of this proposal is the characterisation of rock masses for environmental risk assessment. The areas covered include risk assessment for the safe storage of hazardous wastes in underground repositories; prediction of pollutant trajectories in the subsurface of the earth and their likely consequences; investigations into the causes and effects of contamination of the natural environment; and promotion of the public understanding of risks associated with earth engineering projects. Risk analysis in the design and assessment of underground waste repositories and landfill sites involves the assessment of the likelihood, often over very long periods of time, of contaminants being transported out of the repository. In crystalline rocks this likelihood rests on flow-paths provided by connected fractures. Risk analysis requires an assessment of the uncertainties caused by the limited amount, and nature, of the data and this is only possible via a stochastic (probabilistic) approach. Repeated stochastic simulation of fracture networks will provide sets of possible fracture patterns covering the range of possible connectivities and thereby allow an assessment of the risk of encountering contaminant pathways. The simulated fracture patterns can be validated against laboratory-scale rock masses to provide a realistic basis on which to assess uncertainties, such as fluid flow, as a function of the connectivity, lengths and densities of fractures. We will extend our own work in spatial statistics, and that of others, to provide validated methods for the simulation of fracture networks with stochastic discontinuity geometries.
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Organisation Website: http://www.leeds.ac.uk