Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis

KAHRAMAN S.

NEURAL PROCESSING LETTERS, cilt.43, sa.2, ss.523-535, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1007/s11063-015-9424-7
  • Dergi Adı: NEURAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.523-535
  • Anahtar Kelimeler: Diamond drilling, Penetration rate, Rock properties, Regression analysis, Artificial neural networks, MACHINE PERFORMANCES, ROCK MODULUS, PREDICTION, STRENGTH, PARAMETERS, SANDSTONES, MODELS
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Diamond drilling has been widely used in the different civil engineering projects. The prediction of penetration rate in the drilling is especially useful for the feasibility studies. In this study, the predictability of penetration rate for the diamond drilling was investigated from the operational variables and the rock properties such as the uniaxial compressive strength, the tensile strength and the relative abrasiveness. Both the multiple regression and the artificial neural networks (ANN) analysis were used in the study. Very good models were derived from ANN analysis for the prediction of penetration rate. The comparison of ANN models with the regression models indicated that ANN models were much more reliable than the regression models. It is concluded that the penetration rate for the diamond drilling can be reliably estimated from the uniaxial compressive strength, the tensile strength and the relative abrasiveness using the ANN models.