Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

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GÖKÇEOĞLU C.

SN APPLIED SCIENCES, cilt.4, sa.1, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 4 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s42452-021-04903-y
  • Dergi Adı: SN APPLIED SCIENCES
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, INSPEC
  • Anahtar Kelimeler: TBM, Rate of penetration, Bahce-Nurdag tunnel, Metamorphic rock, ANN, Regression model, UNIAXIAL COMPRESSIVE STRENGTH, TBM PERFORMANCE PREDICTION, CERCHAR ABRASIVITY INDEX, DEFORMATION MODULUS, GRANITIC-ROCKS, NEURAL-NETWORK, MODEL, REGRESSION, OPTIMIZATION, TOOLS
  • Hacettepe Üniversitesi Adresli: Evet

Özet

One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, alpha angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.