Application of several optimization techniques for estimating TBM advance rate in granitic rocks

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dc.contributor.authorArmaghani, Danial Jahed
dc.contributor.authorKoopialipoor, Mohammadreza
dc.contributor.authorMarto, Aminaton
dc.contributor.authorYagiz, Saffet
dc.date.accessioned2019-12-11T07:34:33Z
dc.date.available2019-12-11T07:34:33Z
dc.date.issued2019-08
dc.descriptionhttps://www.sciencedirect.com/science/article/pii/S1674775518303056en_US
dc.description.abstractThis study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior.en_US
dc.identifier.citationArmaghani, D. J., Koopialipoor, M., Marto, A., & Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11(4), 779–789. https://doi.org/10.1016/j.jrmge.2019.01.002en_US
dc.identifier.other10.1016/j.jrmge.2019.01.002
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4368
dc.language.isoenen_US
dc.publisherSCIENCE PRESSen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectTunnel boring machinesen_US
dc.subjectTBMsen_US
dc.subjectAdvance rateen_US
dc.subjectHybrid optimization techniquesen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectImperialist competitive algorithmen_US
dc.subjectICAen_US
dc.subjectPSOen_US
dc.titleApplication of several optimization techniques for estimating TBM advance rate in granitic rocksen_US
dc.typeArticleen_US
workflow.import.sourcescience

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