Assessment of Anomaly Detection Methods Applied to Microtunneling
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 146, Issue 9
Abstract
The proliferation of data collected by modern tunnel boring machines presents a substantial opportunity for the application of data-driven anomaly detection (AD) techniques that can adapt dynamically to site specific conditions. Based on jacking forces measured during microtunneling, this paper explores the potential for AD methods to provide a more accurate and robust detection of incipient faults. A selection of the most popular AD methods proposed in the literature, comprising both clustering- and regression-based techniques, are considered for this purpose. The relative merits of each approach is assessed through comparisons to three microtunneling case histories in which anomalous jacking force behavior was encountered. The results highlight an exciting potential for the use of anomaly detection techniques to reduce unplanned downtimes and operation costs.
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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 research was funded by the Royal Academy of Engineering under the Research Fellowship scheme.
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Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Nov 2, 2019
Accepted: Apr 6, 2020
Published online: Jul 10, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 10, 2020
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