Bayesian prediction of TBM penetration rate in rock mass

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dc.contributor.authorAdoko, Amoussou Coffi
dc.contributor.authorGokceoglu, Candan
dc.contributor.authorYagiz, Saffet
dc.creatorAmoussou Coffi, Adoko
dc.date.accessioned2017-12-14T06:22:22Z
dc.date.available2017-12-14T06:22:22Z
dc.date.issued2017-08-30
dc.description.abstractAbstract One of the essential tasks in the excavation of tunnels with TBM is the reliable estimation of its performance needed for the planning, cost control and other decision making on the feasibility of the tunneling project. The current study aims at predicting the rate of penetration (RoP) of TBM on the basis of the rock mass parameters including the uniaxial compressive strength (UCS), intact rock brittleness (BI), the angle between the plane of weakness and the TBM driven direction (α) and the distance between planes of weakness (DPW). To this end, datasets from the Queens Water Tunnel No. 3 project, New York City, are compiled and used to establish the models. The Bayesian inference approach is implemented to identify the most appropriate models for estimating the RoP among eight (8) candidate models that have been proposed. The selected TBM empirical models are fitted to field data. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and Markov chain Monte Carlo (MCMC) techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. Meanwhile, the deviance information criterion (DIC) is used as the main prediction accuracy indicator and therefore, to rank the models taking into account both their fit and complexity. Overall, the results indicate that the proposed RoP model possesses satisfactory predictive performance.en_US
dc.identifierDOI:10.1016/j.enggeo.2017.06.014
dc.identifier.citationAmoussou Coffi Adoko, Candan Gokceoglu, Saffet Yagiz, Bayesian prediction of TBM penetration rate in rock mass, In Engineering Geology, Volume 226, 2017, Pages 245-256en_US
dc.identifier.issn00137952
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0013795217300091
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/2896
dc.language.isoenen_US
dc.publisherEngineering Geologyen_US
dc.relation.ispartofEngineering Geology
dc.rights.license© 2017 Elsevier B.V. All rights reserved.
dc.subjectTBM penetration rate predictionen_US
dc.subjectModel selectionen_US
dc.subjectBayesian inferenceen_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectRock massen_US
dc.titleBayesian prediction of TBM penetration rate in rock massen_US
dc.typeArticleen_US
elsevier.aggregationtypeJournal
elsevier.coverdate2017-08-30
elsevier.coverdisplaydate30 August 2017
elsevier.endingpage256
elsevier.identifier.doi10.1016/j.enggeo.2017.06.014
elsevier.identifier.eid1-s2.0-S0013795217300091
elsevier.identifier.piiS0013-7952(17)30009-1
elsevier.identifier.scopusid85021456942
elsevier.openaccess0
elsevier.openaccessarticlefalse
elsevier.openarchivearticlefalse
elsevier.startingpage245
elsevier.teaserOne of the essential tasks in the excavation of tunnels with TBM is the reliable estimation of its performance needed for the planning, cost control and other decision making on the feasibility of the...
elsevier.volume226
workflow.import.sourcescience

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