Prediction of Pipe-Jacking Forces Using a Bayesian Updating Approach
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 148, Issue 1
Abstract
An accurate estimation of the jacking forces likely to be experienced during microtunnelling is a key design concern for the structural capacity of pipe segments, the location of intermediate jacking stations, and the efficacy of the pipe-jacking project itself. This paper presents a Bayesian updating approach for the prediction of jacking forces during microtunnelling. The proposed framework was applied to two pipe-jacking case histories completed in the United Kingdom: a 275-m drive in silt and silty sand, and a drive in mudstone. To benchmark the Bayesian predictions, a classical optimization technique, namely genetic algorithms, is also considered. The results show that predictions of pipe-jacking forces using the prior best estimate of model input parameters provided a significant overprediction of the monitored jacking forces for both drives. This highlights the difficulty of capturing the complex geotechnical conditions during tunnelling within prescriptive design approaches and the importance of robust back-analysis techniques. Bayesian updating was shown to be a very effective option, in which significant improvements in the mean predictions and associated variance of the total jacking force are obtained as more data are acquired from the drive.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Royal Academy of Engineering under the Research Fellowship Scheme and the Engineering and Physical Sciences Research Council (Grant No. EP/T006900/1).
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Journal of Geotechnical and Geoenvironmental Engineering
Volume 148 • Issue 1 • January 2022
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© 2021 American Society of Civil Engineers.
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Received: Aug 31, 2020
Accepted: Jun 17, 2021
Published online: Oct 27, 2021
Published in print: Jan 1, 2022
Discussion open until: Mar 27, 2022
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