Yazar "Tunusluoglu, C." seçeneğine göre listele

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    A new general empirical approach for the prediction of rock mass strengths of soft to hard rock masses
    (Pergamon-Elsevier Science Ltd, 2011) Dinc, O. S.; Sonmez, H.; Tunusluoglu, C.; Kasapoglu, K. E.
    It is almost impossible to prepare representative cores of rock masses including discontinuities patterns for laboratory studies. To overcome these difficulties, researchers have focused on developing empirical equations for estimating of the stress-strain behavior of a rock mass, including measurements of the discontinuity patterns. As can be seen in the literature, the uniaxial compressive strength value of rock mass (UCSRM) can be estimated by reducing the uniaxial compressive strength of intact rock material (UCSi) based on the quality of a rock mass, represented by variables such as Rock Mass Rating (RMR), Geological Strength Index (GSI) and Q value. For this reason, a unique reducing curve form empirical equation has limited application and generally, cannot be applied to all kind of rock masses from particularly soft to hard rock masses. In this study, a new general empirical approach is constructed to estimate the strength of rock masses of varying hardness. The new empirical equations have been calibrated using data from five slope failures and four sets of uniaxial compressive strength data of rock masses. In the new empirical equations, the UCSi is considered not only to be a scale parameter used in the strength reduction but also used to adjust the degree of strength reduction in conjunction with elastic modulus of the rock material (E-i). The disturbance factor on the rock mass is taken into consideration by two separate reduction factors applied to the Structure Rating (SR) to capture increasing joint density, and to the s and m(b) parameters of the Hoek-Brown criterion, to decrease the degree of interlocking. Hence, non-interlocked (cohesionless under zero normal stress) rock masses such as spoil piles can also be modeled in the new empirical approach. (C) 2011 Elsevier Ltd. All rights reserved.
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    Artificial Neural Network (ANN) based model for predicting of overall strength of Volcanic Bimrock
    (Crc Press-Taylor & Francis Group, 2014) Sonmez, H.; Coskun, A.; Ercanoglu, M.; Turer, D.; Kasapoglu, K. E.; Tunusluoglu, C.
    The uniaxial compressive strength of rock material (UCS) is one of the fundamental input parameters for engineering applications to be constructed on/in rock masses such as deep slopes, tunnels and dams. However, preparation of the high quality cores for laboratory studies is generally difficult for some types of rock such as laminated and/or fragmented rock material. To overcome this difficulty empirical prediction models were developed by considering some input parameters. Geological mixtures composed of rock blocks surrounded by weak matrix material are known as Block-In-Matrix-Rock (Bimrock) in literature. Agglomerate is a special type of Bimrock, which is composed of andesite fragments surrounded by tuff matrix and it is an example of Volcanic Bimrock. Preparation of core samples for experimental studies from agglomerate is problematic due to the strength contrast between andesite rock fragments and tuff matrix. To overcome these difficulties, some prediction tools have been studied by regression analyses in the literature. In this study, Artificial Neural Network (ANN) as a prediction tool was used to construct a model for prediction of overall UCS of Volcanic Bimrock. While Volumetric Block Proportion (VBP), Volumetric Block Count (VBC) and fractal dimensions (1 and 2 dimensional) were selected as input parameters, normalized overall uniaxal strength of agglomerate to uniaxal compressive strength of tuff matrix is output parameter. Fractal geometry has been used as popular method to define irregular shapes as a quantity in literature. The boundary strength between an-desite fragments and tuff matrix is also sensitive to fragment shape and surface roughness of andesite fragments. Therefore fractal dimensions were selected as input parameters to incorporate this effect on boundary strength. While previously developed computer code FRACRUN was used to determine average fractal dimension of andesite fragments in agglomerate cores, previously developed computer code ANNES was used for ANN based model construction. In addition, similar to Volumetric Joint Count (Jv) which is widely used in rock mass characterization, Volumetric Block Count (VBC) was defined as another input parameter for determination of Bimrock UCS considering some of studies about performed in literature. The highest prediction performance was obtained from the model which considers Volumetric Block Proportion (VBP), Volumetric Block Count (VBC) and 1D fractal dimension as inputs.
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    Possible mechanical behavior of Elmadag and Artova formations during tunnel excavation
    (Crc Press-Taylor & Francis Group, 2014) Dinc, S. O.; Tunusluoglu, C.
    It is vital to understand the stability fields of rock masses of recent engineering structures in Turkey (e.g., high-speed railway tunnel system) before their safely construction. This research project investigates rock mass stability of Ankara-Elmadag tunnel - extending 2975 m, covering 12.5 m diameter space, within a sectional on the high-speed railway system between Ankara and Sivas. Based on our field orientied geological work, we identified two rock formations in the complex geologic setting, proximal to the Ankara ophiolitic Melange. Stratigraphically, Triassic age metasandstone-siltsone (Elmadag formation) was thrusted over the Serpentinites, Cretaceous (Artova formation). In addition to this, rock mechanical parameters based on laboratory studies are received from TCDD (Turkish State Railways). The data derived from these studies are combined to evaluate the rock mass characaterization, quality and strength to determine the engineering behaviours of the rock masses in the region where tunnel will be constructed.