Gurocak, Z.; Alemdag, S.; Bostanci, H.T.; Gokceoglu, C.

Kinematic analysis is one of the most used methods to evaluate potential failures such as planar, wedge and toppling in rock slopes. To analyze possible failure types, orientations of slope and discontinuity geometry with the friction angle of the discontinuities planes are used. In this study, kinematical analyses were evaluated to obtain discontinuity controlled potential failure types in the Gumushane Granitoid Complex which is exposed in Gumushane City and the surroundings. Unstable slope orientations for planar, wedge and toppling failure types were determined. The unstable orientations obtained from kinematic analyses were used to create Geographic Information System (GIS) based instability risk maps. The maps generated by the GIS were evaluated using the Fuzzy Interference System (FIS) method to produce instability risk maps for the study area.

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.