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Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)

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Abstract

Rectangular 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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China “Study on dynamic instability mechanism of tailing dam under hydraulic coupling action in high earthquake intensity and cold region” (51264037); National Natural Science Foundation of China “study on fluid structure coupling macro meso seepage deformation constitutive relationship of glacial earth dam materials in southwest alpine region” (No.11862024).

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Authors and Affiliations

  1. School of Architecture and Planning, Yunnan University, Kunming, 650091, Yunnan, China

    Jun Zhang & Ruoli Shi

  2. School of Architectural Engineering, Sichuan University of Arts and Science, Dazhou, 635000, Sichuan, China

    Shaohua Shi

  3. Civil Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates

    A. K. Alzo’ubi

  4. Programa Magister en ciencias químico-biológicas, Facultad de Ciencias de la Salud, Universidad Bernardo O’Higgins, Santiago, Chile

    Angel Roco-Videla

  5. Departamento de ingeniería Civil, Facultad de Ingeniería, Universidad Católica de la Santísima Concepción, Concepción, Chile

    Angel Roco-Videla

  6. Civil Engineering Department, Faculty of Engineering, Sohag University, Sohâg, Egypt

    Mohamed. M. A. Hussein

  7. Department of Hydraulics and Hydraulic and Pneumatic Systems, Institute of Engineering and Technology, South Ural State University, Lenin Prospect 76, Chelyabinsk, 454080, Russian Federation

    Afrasyab Khan

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Zhang, J., Shi, R., Shi, S. et al. Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP). Engineering with Computers 38 (Suppl 4), 3581–3597 (2022). https://doi.org/10.1007/s00366-021-01473-w

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