Assessment of anomaly detection methods applied to microtunneling

Brian B. Sheil, Stephen K. Suryasentana, Wen Chieh Cheng

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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-A nd 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.

Original languageEnglish
Article number04020094
JournalJournal of geotechnical and geoenvironmental engineering
Volume146
Issue number9
Early online date10 Jul 2020
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • microtunneling
  • anomaly detection methods
  • tunnel boring machines
  • data-driven anomaly detection (AD) techniques
  • robust detection of incipient faults
  • case histories
  • pipe jacking
  • boring machines (machine tools)
  • jacking forces

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