Journal article
Prediction of microtunnelling jacking forces using a probabilistic observational approach
- Abstract:
- Microtunnelling is an increasingly popular means of locating utilities below ground. The ability to predict the total jacking force requirements during a drive is highly desirable for anomaly detection, to ensure the available thrust is not exceeded, and to prevent damage to the pipe string and/or launch shaft. However, prediction of the total jacking force is complicated by site geology, the use of a lubricated overcut, work stoppages, tunnel boring machine driving style and pipe misalignment. This paper introduces a probabilistic observational approach for forecasting jacking forces during microtunnelling. Gaussian process regression is adopted for this purpose which allows forecasts to be performed within a probabilistic framework. The proposed approach is applied to two recent UK microtunnelling monitoring projects and the forecasts are appraised through comparisons to predictions determined using design methods currently applied in industry. The results show that the proposed framework provides excellent forecasts of the monitored field data and highlights a significant opportunity to complement existing prescriptive design methods with probabilistic forecasting techniques.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 2.2MB, Terms of use)
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- Publisher copy:
- 10.1016/j.tust.2020.103749
Authors
- Publisher:
- Elsevier
- Journal:
- Tunnelling and Underground Space Technology More from this journal
- Volume:
- 109
- Article number:
- 103749
- Publication date:
- 2020-12-29
- Acceptance date:
- 2020-11-23
- DOI:
- ISSN:
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0886-7798
- Language:
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English
- Keywords:
- Pubs id:
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1147448
- Local pid:
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pubs:1147448
- Deposit date:
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2020-12-01
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier
- Copyright date:
- 2020
- Rights statement:
- © 2020 Elsevier Ltd. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article, available under the terms of a Creative Commons, Attribution, Non-Commercial, No Derivatives licence. The final version is available online from Elsevier at: https://doi.org/10.1016/j.tust.2020.103749
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