Projects per year
Abstract
Cable ploughing is an important technique for burying and protecting offshore cables. The ability to predict the required tow force and plough performance is essential to allow vessel selection and project programming. Existing tow force models require calibration against full-scale field testing to determine empirical parameters, a requirement that may hinder their use. In this study the factors controlling the plough resistance were investigated using a series of dry and saturated 1/50th scale model cable plough tests in sand in a geotechnical centrifuge at 50g at a range of target trench depths, sand relative densities, and plough velocities. An improved model for predicting cable plough tow force that separates out the key components of resistance and allows tow force prediction without the use of field-derived empirical coefficients is presented. It is demonstrated that the key parameters in this model can be easily determined from in situ cone penetration tests (CPTs), allowing it to be used offshore where site investigation techniques may be more limited compared to onshore locations. The model is validated against the centrifuge cable plough tests, demonstrating it can correctly predict both the static (dry) and rate effect (saturated) tow forces.
Original language | English |
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Pages (from-to) | 1466-1477 |
Number of pages | 12 |
Journal | Canadian Geotechnical Journal |
Volume | 58 |
Issue number | 10 |
Early online date | 23 Nov 2020 |
DOIs | |
Publication status | Published - Oct 2021 |
Keywords
- Cable
- cable plough
- Offshore
- Cable ploughing
- Sand
- Rate effects
- Cone penetration test (CPT)
- Centrifuge modelling
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Civil and Structural Engineering
Fingerprint
Dive into the research topics of 'A cone penetration test (CPT) approach to cable plough performance prediction based upon centrifuge model testing'. Together they form a unique fingerprint.Projects
- 1 Finished
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Seabed Ploughing: Modelling for Infrastructure Installation (Joint with Durham University)
Brennan, A. (Investigator) & Brown, M. (Investigator)
Engineering and Physical Sciences Research Council
1/10/14 → 31/12/17
Project: Research
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An implicit material point-to-rigid body contact approach for large deformation soil-structure interaction
Bird, R. E., Pretti, G., Coombs, W. M., Augarde, C., Sharif, Y., Brown, M., Carter, G., Macdonald, C. & Johnson, K., Oct 2024, In: Computers and Geotechnics. 174, 16 p., 106646.Research output: Contribution to journal › Article › peer-review
Open AccessFile36 Downloads (Pure) -
Cone Penetration Tests (CPTs) in layered soils: a Material Point approach
Bird, R., Coombs, W. M., Augarde, C., Brown, M., Sharif, Y., Carter, G., Johnson, K. & Macdonald, C., 2023. 6 p.Research output: Contribution to conference › Paper › peer-review
Open AccessFile116 Downloads (Pure) -
Comparative Assessment of Pipeline Plough Performance Prediction Models Against Field Experience in Sand
Brunning, P., Ashton, G., Brown, M., Robinson, S. & Lauder, K. D., 2020, Proceedings of the 30th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers, p. 1366-1372 7 p. (Proceedings of the International Offshore and Polar Engineering Conference; vol. 2020-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile
Activities
- 1 Invited talk
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Society for Underwater Technology (SUT) Aberdeen Evening Meeting Invited Presentation: Gadgets & Widgets: Where Is Your Anchor And How Will It Behave?
Sharif, Y. (Speaker), Davidson, C. (Speaker), Brown, M. (Speaker) & Brennan, A. (Speaker)
18 Sept 2024Activity: Talk or presentation types › Invited talk
Datasets
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A CPT approach to cable plough performance prediction based upon centrifuge model testing - Measured Data
Robinson, S. (Creator), Brown, M. (Creator), Matsui, H. (Creator), Brennan, A. (Creator), Augarde, C. (Creator), Coombs, W. M. (Creator) & Cortis, M. (Creator), University of Dundee, 30 Nov 2020
DOI: 10.15132/10000161
Dataset
File