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Abstract
Prediction of pile performance often relies upon full-scale pile load testing to better manage geotechnical uncertainty and enable less conservative design. Many analysis methods (e.g. the α-method) require a load test database for calibration. Databases of these tests can provide detailed design guidance in specific geological deposits. However, full scale tests are expensive, and the results, for a variety of reasons, are not always shared with the wider geotechnical community. The DINGO database is an openly accessible database of full-scale pile load tests carried out in UK soils. This paper reports on the building of the database as well as the challenges involved and lessons learnt in collecting and sharing the pile test data. The pile test data in the database is presented by sorting for ‘Geological Deposit’, ‘Construction Decade’ and ‘Construction Type’. A preliminary classification of the quality of information contained in the database is also presented.
Original language | English |
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Title of host publication | Piling 2020 |
Subtitle of host publication | Proceedings of the Piling 2020 Conference |
Editors | K.G. Higgins, Y. Ainsworth, D.G. Toll, A.S. Osman |
Place of Publication | London, UK |
Publisher | ICE Publishing |
Pages | 229-234 |
ISBN (Print) | 9780727765048 |
DOIs | |
Publication status | Published - 5 Mar 2021 |
Event | PILING 2020 - Online Duration: 23 Mar 2021 → 26 Mar 2021 https://www.piling2020.org/ |
Conference
Conference | PILING 2020 |
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Period | 23/03/21 → 26/03/21 |
Internet address |
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Dive into the research topics of 'DINGO: A Pile Load Test Database'. Together they form a unique fingerprint.Projects
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Datasets
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The DINGO Database, v1.0
Vardanega, P. J. (Creator), Voyagaki, E. (Creator), Crispin, J. (Creator), Gilder, C. (Creator) & Ntasiou, K. (Creator), University of Bristol, 30 May 2019
DOI: 10.5523/bris.3r14qbdhv648b2p83gjqby2fl8, http://data.bris.ac.uk/data/dataset/3r14qbdhv648b2p83gjqby2fl8
Dataset