Yazarlar

• Alemdag, S.(1)
• Alemdag, Selcuk(1)
• Cabalar, A.F.(1)
• Cevik, A.(1)
• Gokceoglu, C.(1)
• Gurocak, Z.(1)
• Gurocak, Zulfu(1)
• Kanik, Mustafa(1)
• Kaya, Ayberk(1)

• MakaleAçık Erişim2025/r/gumushane/13503
  Re-evaluation of excavation class limits for underground excavations based on new data

  Gurocak, Zulfu; Kaya, Ayberk; Kanik, Mustafa; Alemdag, Selcuk

  Many researchers have proposed empirical excavatability classifications to easily and quickly assess rock masses. These classifications mostly apply to surface excavations, while those for underground excavations are limited. Updating the classifications proposed for underground excavations based on new data is important in terms of eliminating the deficiencies in this regard. This study determined the engineering properties (RMR, Q, GSI), rock material strengths, and in-situ excavation classes of 16 underground rock masses. The new data were compared with excavation classifications from various researchers and empirical classes. The study found that empirical classifications for surface conditions are not applicable for underground. Excavation is more challenging in underground conditions due to stress from overburden. While excavation classes align for good to very good rock masses in both conditions, there is no perfect match for medium, weak, and very weak rock masses for underground. The study suggests that for underground excavation classes (blasting, hammer&blasting, hammer, and digging), both RMR89 and Q values should be used together to differentiate between classes. When using GSI for classification, the Is(50) value of the rock material should also be considered. The σcm parameter is the most critical in evaluating rock mass excavatability.

  İncele
• MakaleYayıncıda açık erişim2016/r/gumushane/15222
  Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

  Alemdag, S.; Gurocak, Z.; Cevik, A.; Cabalar, A.F.; Gokceoglu, C.

  This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.

  İncele