Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251992
Title: The design of a relational database on the geotechnical properties of Northern England glacial till
Author: Hashemi, Siamak
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2002
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Abstract:
The landscape of Northern England has been mostly formed by glacial activities during the Quaternary period, and glacial till materials have been deposited over the northern counties of England during these glacial activities. Townships, industrial developments and infrastructure works exist or are planned in these areas. The variable and often complex successions in which glacial tills occur have frequently led to problems on civil and mining engin eering projects. Glacial tills are engineering soils which have been defined as a poorly sorted mixture of clay, silt, sand, gravel, cobble and boulder sized material deposited directly from glacier ice. The glacial tills of the counties in Northern England are the subject of many studies which are carried out in order to determine the properties of the overlying glacial deposits. Ground investigations have been carried out for opencast coal projects. A large number of samples were obtained and extensive laboratory testing has been carried out. Using the results of these investigations and tests, a geotechnical database is being developed that should provide a useful resource for civil and mining engineers in the northern counties region. Its purpose is the extensive analysis of the parameters that are used to define the geotechnical properties of Northern England glacial tills. This should give a better understanding of the engineering behaviour of glacial tills and parameter selection for engineering design. In addition to statistical analysis, Neural Networks, a model of Artificial Intelligence, are used to find correlations between the different parameters and to develop new methods of modelling and predicting geotechnical design parameters. Neural technology is an emerging field of artificial intelligence that has attracted the interest of many scientists and engineers. They are information-processing systems that can mimic the biological system of the brain and can be trained to complete and classify input patterns, or to complete a function of their input. In this project the data available from the database are used to train Neural Networks to classify glacial tills according to their geotechnical properties and investigate their potential in predicting geotechnical design parameters.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.251992  DOI: Not available
Keywords: Soil mechanics Soil science Glaciology Frozen ground Snow Civil engineering
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