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

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...

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Vydáno v:	KSCE Journal of Civil Engineering Ročník 23; číslo 7; s. 3200 - 3206
Hlavní autoři:	Jung, Jee-Hee, Chung, Heeyoung, Kwon, Young-Sam, Lee, In-Mo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Seoul Korean Society of Civil Engineers 01.07.2019 Springer Nature B.V 대한토목학회
Témata:	Accuracy Algorithms Architectural engineering Artificial neural networks Back propagation Boring machines Civil Engineering Data Data acquisition Disclaimers Drilling & boring machinery Engineering Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Mathematical models Methods Model accuracy Neural networks Predictions Trends Tunnel construction Tunnel Engineering Tunneling shields Tunnels 토목공학 artificial neural network (ANN) tunnel boring machine (TBM) ground types backpropagation (BP) algorithm tunnel face TBM data ground condition prediction
ISSN:	1226-7988, 1976-3808
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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.
AbstractList	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. 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. KCI Citation Count: 55
Author	Jung, Jee-Hee Chung, Heeyoung Lee, In-Mo Kwon, Young-Sam
Author_xml	– sequence: 1 givenname: Jee-Hee surname: Jung fullname: Jung, Jee-Hee organization: School of Civil, Environmental and Architectural Engineering, Korea University – sequence: 2 givenname: Heeyoung surname: Chung fullname: Chung, Heeyoung organization: School of Civil, Environmental and Architectural Engineering, Korea University – sequence: 3 givenname: Young-Sam surname: Kwon fullname: Kwon, Young-Sam organization: School of Civil, Environmental and Architectural Engineering, Korea University – sequence: 4 givenname: In-Mo surname: Lee fullname: Lee, In-Mo email: inmolee@korea.ac.kr organization: School of Civil, Environmental and Architectural Engineering, Korea University
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Cites_doi	10.1007/s12205-018-0912-y 10.1016/j.ijmst.2015.05.019 10.1109/4235.585893 10.1016/j.tust.2017.06.015 10.1016/j.tust.2004.02.128
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References	KimPMATLAB deep learning: With machine learning, neural networks and artificial intelligence2017New York, NY, USAApress538010.1007/978-1-4842-2845-6 JungS MA risk management system applicable to TBM tunnel during design and construction stage2014Seoul, Korea (in Korean)Korea University PattersonD WArtificial neural networks: Theory and applications1995New York, NY, USASpringer1179 BudumaNLocascioNFundamentals of deep learning: Designing next-generation machine intelligence algorithms2017CA, USAO’Reilly Media, Sebastopol MaHYinLGongQWangJTBM tunneling in mixed-face ground: Problems and solutionsInternational Journal of Mining Science and Technology201525464164710.1016/j.ijmst.2015.05.019 WolpertD HMacreadyW GNo free lunch theorems for optimizationIEEE Transactions on Evolutionary Computation199711678210.1109/4235.585893 GholamnejadJTayaraniNApplication of artificial neural networks to the prediction of tunnel boring machine penetration rateMining Science and Technology2010205727733 RipleyB DPattern recognition and neural networks1996New York, NY, USACambridge University Press10.1017/CBO97805118126510853.62046 TaylorKDeep learning. Applications with MATAB2017Lavergne, TN, USACreateSpace Independent Publishing Platform HintonG EA practical guide to training restricted boltzmann machines2010Toronto, CanadaUniversity of Toronto VergaraaI MSarogloubCPrediction of TBM performance in mixed-face ground conditionsTunnelling and Underground Space Technology20176911612410.1016/j.tust.2017.06.015 BenardosA GKaliampakosD CModelling TBM performance with artificial neural networksTunnelling and Underground Space Technology200419659760510.1016/j.tust.2004.02.128 ChungHLeeI MJungJ HParkJBayesian networks-based shield TBM risk management system: Methodology development and applicationKSCE Journal of Civil Engineering201923145246510.1007/s12205-018-0912-y HyunK CA risk management system applicable to shield TBM tunnel2013Seoul, Korea (in Korean)Korea University LeCunY ABottouLOrrG BMullerK REfficient backpropNeural Networks: Tricks of the Trade20127700948 BealeM HHaganM TDemuthH BNeural Network Toolbox™ user’s guide2016Natick, MA, USA.The MathWorks Inc. LeeK HPredictions of ground conditions ahead of tunnel face applicable to TBM2014Seoul, Korea (in Korean)Korea University Gholamnejad (10.1007/s12205-019-1460-9_bib5) 2010; 20 Lee (10.1007/s12205-019-1460-9_bib11) 2014 Jung (10.1007/s12205-019-1460-9_bib8) 2014 Kim (10.1007/s12205-019-1460-9_bib9) 2017 Ripley (10.1007/s12205-019-1460-9_bib14) 1996 Hyun (10.1007/s12205-019-1460-9_bib7) 2013 Taylor (10.1007/s12205-019-1460-9_bib15) 2017 Benardos (10.1007/s12205-019-1460-9_bib2) 2004; 19 Hinton (10.1007/s12205-019-1460-9_bib6) 2010 Patterson (10.1007/s12205-019-1460-9_bib13) 1995 Buduma (10.1007/s12205-019-1460-9_bib3) 2017 Chung (10.1007/s12205-019-1460-9_bib4) 2019; 23 Vergaraa (10.1007/s12205-019-1460-9_bib16) 2017; 69 Beale (10.1007/s12205-019-1460-9_bib1) 2016 Wolpert (10.1007/s12205-019-1460-9_bib17) 1997; 1 Ma (10.1007/s12205-019-1460-9_bib12) 2015; 25 LeCun (10.1007/s12205-019-1460-9_bib10) 2012; 7700
References_xml	– reference: LeeK HPredictions of ground conditions ahead of tunnel face applicable to TBM2014Seoul, Korea (in Korean)Korea University – reference: RipleyB DPattern recognition and neural networks1996New York, NY, USACambridge University Press10.1017/CBO97805118126510853.62046 – reference: ChungHLeeI MJungJ HParkJBayesian networks-based shield TBM risk management system: Methodology development and applicationKSCE Journal of Civil Engineering201923145246510.1007/s12205-018-0912-y – reference: JungS MA risk management system applicable to TBM tunnel during design and construction stage2014Seoul, Korea (in Korean)Korea University – reference: HyunK CA risk management system applicable to shield TBM tunnel2013Seoul, Korea (in Korean)Korea University – reference: HintonG EA practical guide to training restricted boltzmann machines2010Toronto, CanadaUniversity of Toronto – reference: TaylorKDeep learning. Applications with MATAB2017Lavergne, TN, USACreateSpace Independent Publishing Platform – reference: GholamnejadJTayaraniNApplication of artificial neural networks to the prediction of tunnel boring machine penetration rateMining Science and Technology2010205727733 – reference: VergaraaI MSarogloubCPrediction of TBM performance in mixed-face ground conditionsTunnelling and Underground Space Technology20176911612410.1016/j.tust.2017.06.015 – reference: MaHYinLGongQWangJTBM tunneling in mixed-face ground: Problems and solutionsInternational Journal of Mining Science and Technology201525464164710.1016/j.ijmst.2015.05.019 – reference: BudumaNLocascioNFundamentals of deep learning: Designing next-generation machine intelligence algorithms2017CA, USAO’Reilly Media, Sebastopol – reference: KimPMATLAB deep learning: With machine learning, neural networks and artificial intelligence2017New York, NY, USAApress538010.1007/978-1-4842-2845-6 – reference: WolpertD HMacreadyW GNo free lunch theorems for optimizationIEEE Transactions on Evolutionary Computation199711678210.1109/4235.585893 – reference: PattersonD WArtificial neural networks: Theory and applications1995New York, NY, USASpringer1179 – reference: LeCunY ABottouLOrrG BMullerK REfficient backpropNeural Networks: Tricks of the Trade20127700948 – reference: BealeM HHaganM TDemuthH BNeural Network Toolbox™ user’s guide2016Natick, MA, USA.The MathWorks Inc. – reference: BenardosA GKaliampakosD CModelling TBM performance with artificial neural networksTunnelling and Underground Space Technology200419659760510.1016/j.tust.2004.02.128 – year: 2010 ident: 10.1007/s12205-019-1460-9_bib6 – year: 2013 ident: 10.1007/s12205-019-1460-9_bib7 – volume: 20 start-page: 727 issue: 5 year: 2010 ident: 10.1007/s12205-019-1460-9_bib5 article-title: Application of artificial neural networks to the prediction of tunnel boring machine penetration rate publication-title: Mining Science and Technology – volume: 23 start-page: 452 issue: 1 year: 2019 ident: 10.1007/s12205-019-1460-9_bib4 article-title: Bayesian networks-based shield TBM risk management system: Methodology development and application publication-title: KSCE Journal of Civil Engineering doi: 10.1007/s12205-018-0912-y – volume: 25 start-page: 641 issue: 4 year: 2015 ident: 10.1007/s12205-019-1460-9_bib12 article-title: TBM tunneling in mixed-face ground: Problems and solutions publication-title: International Journal of Mining Science and Technology doi: 10.1016/j.ijmst.2015.05.019 – year: 2017 ident: 10.1007/s12205-019-1460-9_bib15 – year: 2014 ident: 10.1007/s12205-019-1460-9_bib11 – year: 1996 ident: 10.1007/s12205-019-1460-9_bib14 – start-page: 1 year: 1995 ident: 10.1007/s12205-019-1460-9_bib13 – volume: 7700 start-page: 9 year: 2012 ident: 10.1007/s12205-019-1460-9_bib10 article-title: Efficient backprop publication-title: Neural Networks: Tricks of the Trade – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.1007/s12205-019-1460-9_bib17 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – start-page: 53 year: 2017 ident: 10.1007/s12205-019-1460-9_bib9 – volume: 69 start-page: 116 year: 2017 ident: 10.1007/s12205-019-1460-9_bib16 article-title: Prediction of TBM performance in mixed-face ground conditions publication-title: Tunnelling and Underground Space Technology doi: 10.1016/j.tust.2017.06.015 – year: 2017 ident: 10.1007/s12205-019-1460-9_bib3 – year: 2014 ident: 10.1007/s12205-019-1460-9_bib8 – volume: 19 start-page: 597 issue: 6 year: 2004 ident: 10.1007/s12205-019-1460-9_bib2 article-title: Modelling TBM performance with artificial neural networks publication-title: Tunnelling and Underground Space Technology doi: 10.1016/j.tust.2004.02.128 – year: 2016 ident: 10.1007/s12205-019-1460-9_bib1
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SubjectTerms	Accuracy Algorithms Architectural engineering Artificial neural networks Back propagation Boring machines Civil Engineering Data Data acquisition Disclaimers Drilling & boring machinery Engineering Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Mathematical models Methods Model accuracy Neural networks Predictions Trends Tunnel construction Tunnel Engineering Tunneling shields Tunnels 토목공학
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Title	An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data
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Volume	23
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