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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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Publisher copy:
10.1016/j.tust.2020.103749

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1462-1401


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:
0886-7798


Language:
English
Keywords:
Pubs id:
1147448
Local pid:
pubs:1147448
Deposit date:
2020-12-01
ARK identifier:

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