ANOMALY DETECTION IN SLOPE SURFACE STRAIN USING DEEP LEARNING

DOI 1 Citations
  • HIRAOKA Nobutaka
    (独)労働者健康安全機構労働安全衛生総合研究所 建設安全研究グループ
  • KIKKAWA Naotaka
    (独)労働者健康安全機構労働安全衛生総合研究所 建設安全研究グループ
  • ITOH Kazuya
    東京都市大学　建築都市デザイン学部都市工学科

Bibliographic Information

Other Title
  • 深層学習による斜面表層ひずみの異常検知

Abstract

<p>The operation of a monitoring system based on the detection of signs of collapse from slope observation data is effective as a soft measure to prevent landslide disasters. The major challenge of this monitoring system is to define how to trigger an alarm for evacuation when the measured data changes. In this study, we use time series data of slope surface strain measured in a slope failure experiment at the centrifuge modeling. We used LSTM, one of the deep learning methods, to predict the data, and verified the method to detect the anomaly of the slope by the residual between the predicted and measured values. From the time series of the number of anomalies detected by the eight sensors, it was confirmed that the anomalies could be detected before the slope collapse. In addition, the surface strains were converted to velocities in order to make the time series data stationary. In this case, it was confirmed that the anomaly of the slope could be detected before the collapse.</p>

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Details 詳細情報について

  • CRID
    1390571563557661568
  • NII Article ID
    130008118335
  • DOI
    10.11532/jsceiii.2.j2_556
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • Crossref
  • Abstract License Flag
    Disallowed

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