Browsing by Author "Varol, Ogun Ozan"

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A Comparative Slope Stability Analyses of Heavily Jointed Open-Pit Slopes Using 3d Limit Equilibrium and Finite Element Methods: a Case Study From Bingöl, Türkiye
(Taylor & Francis Ltd, 2024) Varol, Ogun Ozan; Ayhan, Mustafa; Akin, Mutluhan
This study investigates the slope stability of Bing & ouml;l open-pit iron mine slopes using 3D limit equilibrium and finite element methods. The pit benches are 3 m wide and 6 m high, with an overall slope angle of 38 degrees. The geological composition comprises slightly to moderately weathered and heavily jointed phyllite, micaschist, and gneiss. Shear strength parameters were determined through back analyses of already failed sections using the Hoek-Brown failure criterion and Geological Strength Index (GSI). Factor of safety values for static and dynamic conditions were calculated using 3D limit equilibrium and 2D finite element analyses. Results show good agreement between the two methods, with benches in the northern section exhibiting a significant decrease in safety factor under dynamic conditions, indicating near-limit equilibrium slopes.
Predicting Uniaxial Compressive Strength of Tuff After Accelerated Freeze-Thaw Testing: Comparative Analysis of Regression Models and Artificial Neural Networks
(Science Press, 2024) Varol, Ogun Ozan
Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of T & uuml;rkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.