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

doi:10.1007/s12205-019-1460-9

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

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

44 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 language	English
Pages (from-to)	3200-3206
Number of pages	7
Journal	KSCE Journal of Civil Engineering
Volume	23
Issue number	7
DOIs	https://doi.org/10.1007/s12205-019-1460-9
Publication status	Published - 2019 Jul 1

Keywords

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

ASJC Scopus subject areas

Civil and Structural Engineering

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10.1007/s12205-019-1460-9

Cite this

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

author = "Jung, {Jee Hee} and Heeyoung Chung and Kwon, {Young Sam} and Lee, {In Mo}",

note = "Funding Information: This research was supported by a grant (Project number: 18SCIP-B066321-06, Development of Key Subsea Tunneling Technology) from Infrastructure and Transportation Technology Promotion Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government. Publisher Copyright: {\textcopyright} 2019, Korean Society of Civil Engineers.",

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AU - Kwon, Young Sam

AU - Lee, In Mo

N1 - Funding Information: This research was supported by a grant (Project number: 18SCIP-B066321-06, Development of Key Subsea Tunneling Technology) from Infrastructure and Transportation Technology Promotion Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government. Publisher Copyright: © 2019, Korean Society of Civil Engineers.

PY - 2019/7/1

Y1 - 2019/7/1

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 - TBM data

KW - artificial neural network (ANN)

KW - backpropagation (BP) algorithm

KW - ground condition prediction

KW - ground types

KW - tunnel boring machine (TBM)

KW - tunnel face

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

Abstract

Keywords

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