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

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

Fingerprint

Tunnels
Neural networks
Data acquisition

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

Cite this

An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data. / Jung, Jee Hee; Chung, Heeyoung; Kwon, Young Sam; Lee, In Mo.

In: KSCE Journal of Civil Engineering, 01.01.2019.

Research output: Contribution to journalArticle

@article{490c711aa45743ab9aa874ae12da5fa6,
title = "An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data",
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.",
keywords = "artificial neural network (ANN), backpropagation (BP) algorithm, ground condition prediction, ground types, TBM data, tunnel boring machine (TBM), tunnel face",
author = "Jung, {Jee Hee} and Heeyoung Chung and Kwon, {Young Sam} and Lee, {In Mo}",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/s12205-019-1460-9",
language = "English",
journal = "KSCE Journal of Civil Engineering",
issn = "1226-7988",
publisher = "Korean Society of Civil Engineers",

}

TY - JOUR

T1 - An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data

AU - Jung, Jee Hee

AU - Chung, Heeyoung

AU - Kwon, Young Sam

AU - Lee, In Mo

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - artificial neural network (ANN)

KW - backpropagation (BP) algorithm

KW - ground condition prediction

KW - ground types

KW - TBM data

KW - tunnel boring machine (TBM)

KW - tunnel face

UR - http://www.scopus.com/inward/record.url?scp=85066035210&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066035210&partnerID=8YFLogxK

U2 - 10.1007/s12205-019-1460-9

DO - 10.1007/s12205-019-1460-9

M3 - Article

JO - KSCE Journal of Civil Engineering

JF - KSCE Journal of Civil Engineering

SN - 1226-7988

ER -