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dc.contributor.authorJun Zhang, Afrasyab Khan
dc.contributor.authorShi, Ruoli
dc.contributor.authorShi, Shaohua
dc.contributor.authorAlzo'ubi, Abdelkareem
dc.contributor.authorETAL;
dc.date.accessioned2022-09-22T07:03:54Z
dc.date.available2022-09-22T07:03:54Z
dc.date.issued2021-07
dc.identifier.citationZhang, J., Shi, R., Shi, S., Alzo’ubi, A. K., Roco-Videla, A., Hussein, M., & Khan, A. (2021). Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP). Engineering with Computers, 1-17.‏en_US
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/4058
dc.description.abstractRectangular tunnel boring machine (TBM) is applied for the tunnels’ excavation including a cross section of circular and rectangular shape within the various rocks and soil strata. Excessive structural forces, which is produced within tunnel linings, might affect the serviceability and safety of tunnels whose forces acting on tunnels linings during the initial design period have to be accurately calculated. Few numerical studies have been conducted on different soil-rectangular tunnel systems to clarify the critical response characteristics of cut-and-cover (rectangular) tunnels adjusted to transversal ground shaking. In this case, predicting the soil dynamic shear stresses developed around the tunnel is an elaborate task due to the interaction of TBM in the rectangular form and the rock. Despite doing the empirical studies in analyzing the rectangular tunnels systems, using artificial intelligence (AI) methods could significantly develop the optimization of TBM tunneling and decreasing the cost, error percentages, disturbance, and time-consuming complications related to tunneling. In this study, two algorithms, namely, support vector machine (SVM) and gene expression programming (GEP), were used to accurately predict the soil dynamic shear stresses developed around the tunnel. The models were developed and measured resulting that SVM can indicate a high-performance capacity in predicting the soil dynamic shear stresses developed around the tunnel through the rectangular TBM excavation machine.en_US
dc.language.isoenen_US
dc.publisherSpringer Londonen_US
dc.subjectRectangularen_US
dc.subjectGene expression programming (GEP)en_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectRectangular tunnel boring machine (TBM)en_US
dc.titleNumerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)en_US
dc.title.alternativeJournal articleen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1007/s00366-021-01473-w


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