An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data

Jee Hee Jung, Heeyoung Chung, Young Sam Kwon, In Mo Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper presents an artificial neural network (ANN) model that predicts ground conditions ahead of a tunnel face by using shield tunnel boring machine (TBM) data obtained during the tunneling operation. The primary advantage of the proposed technique is that, by using TBM data, no additional data acquisition device is required. Ground type classifications and machine data normalization methods are introduced to maintain the consistency of the measured data and improve prediction accuracy. The efficacy of the proposed model is demonstrated by its 96% accuracy in predicting ground type one ring ahead of the tunnel face.

Original languageEnglish
JournalKSCE Journal of Civil Engineering
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • artificial neural network (ANN)
  • backpropagation (BP) algorithm
  • ground condition prediction
  • ground types
  • TBM data
  • tunnel boring machine (TBM)
  • tunnel face

ASJC Scopus subject areas

  • Civil and Structural Engineering

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