3D geological model of the completed Farringdon underground railway station
Gakis, Angelos; Cabrero, Paula; Entwisle, David; Kessler, Holger. 2016 3D geological model of the completed Farringdon underground railway station. In: Black, Mike, (ed.) Crossrail Project, infrastructure, design and construction. Volume 3. London, UK, Thomas Telford Limited and Crossrail 2016, 431-446.
Full text not available from this repository. (Request a copy)Abstract/Summary
The complexity and the unknowns of the geology at Farringdon, primarily associated with the Lambeth Group, required a state-of-the-art geotechnical approach in order to manage the risks related to the open face, sprayed concrete lining (SCL) tunnelling. This was aided by the 3D geological model developed by the British Geological Survey (BGS) in 2009 for the proposed Farringdon underground railway station and which was provided to the contractor's team in 2013, in order to continue the revision of the model. The model was initially updated with additional ground data from boreholes and shaft excavation (2009 to 2013). It then became an integral part of the site supervision workflow, being updated daily with data from the tunnel face. This dynamic model became a ‘live’ geological database of increasing accuracy with time, allowing for geological predictions for the forthcoming tunnel excavations. In parallel, the understanding of the complexity of the Lambeth Group geology was significantly improved, refining the location and characteristics of the multiple faults and the thickness and continuity of the high-risk sand lenses. This paper aims to describe how a BGS 3D geological model was developed to be used in combination with tunnelling works for the first time, the benefits from its use and the lessons learned with respect to the geology of the Lambeth Group.
Item Type: | Publication - Book Section |
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Digital Object Identifier (DOI): | https://doi.org/10.1680/cpid.61293.431 |
Additional Keywords: | 3D geological model, risk reduction |
NORA Subject Terms: | Earth Sciences Computer Science Data and Information |
Date made live: | 22 Dec 2016 09:24 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/515514 |
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