Digital repository of Slovenian research organisations

Search the repository
A+ | A- | Help | SLO | ENG

Query: search in
search in
search in
search in

Options:
  Reset


Query: "keywords" (rock mechanics) .

1 - 2 / 2
First pagePrevious page1Next pageLast page
1.
Quantified joint surface description and joint shear strength of small rock samples
Karmen Fifer Bizjak, Andraž Geršak, 2018, original scientific article

Abstract: Geotechnical structures in rock masses such as tunnels, underground caverns, dam foundations and rock slopes often have problems with a jointed rock mass. The shear behaviour of a jointed rock mass depends on the mechanical behaviour of the discontinuities in that particular rock mass. If we want to understand the mechanical behaviour of a jointed rock mass, it is necessary to study the deformation and strength of a single joint. One of the primary objectives of this work is to improve the understanding of the frictional behaviour of rough rock joints under shear loads with regard to the roughness of the joint surface. The main problem is how to measure and quantify the roughness of the surface joint and connect the morphological parameters into a shear strength criterion. Until now, several criteria have been developed; however, all of them used large rock samples (20×10×10 cm). It is often not possible to get large samples, especially when the rock is under a few meters thick layer of soil. In this case, samples of rock can only be acquired with investigation borehole drilling, which means that the samples of rock are small and of different shapes. The paper presents the modified criterion that is suitable for calculating the peak shear stress of small samples.
Keywords: camera-type 3D scanner, rock mechanics rock joint, roughness of the joints, rock joint shear strength
Published in DiRROS: 11.12.2023; Views: 168; Downloads: 79
.pdf Full text (847,17 KB)
This document has many files! More...

2.
Prediction of the peak shear strength of the rock joints with artificial neural networks
Karmen Fifer Bizjak, Rok Vezočnik, 2022, original scientific article

Abstract: With the development of computer technology, artificial neural networks are becoming increasingly useful in the field of engineering geology and geotechnics. With artificial neural networks, the geomechanical properties of rocks or their behaviour could be predicted under different stress conditions. Slope failures or underground excavations in rocks mostly occurred through joints, which are essential for the stability of geotechnical structures. This is why the peak shear strength of a rock joint is the most important parameter for a rock mass stability. Testing of the shear characteristics of joints is often time consuming and suitable specimens for testing are difficult to obtain during the research phase. The roughness of the joint surface, tensile strength and vertical load have a great influence on the peak shear strength of the rock joint. In the presented paper, the surface roughness of joints was measured with a photogrammetric scanner, and the peak shear strength was determined by the Robertson direct shear test. Based on six input characteristics of the rock joints, the artificial neural network, using a backpropagation learning algorithm, successfully learned to predict the peak shear strength of the rock joint. The trained artificial neural network predicted the peak shear strength for similar lithological and geological conditions with average estimation error of 6%. The results of the calculation with artificial neural networks were compared with the Grasselli experimental model, which showed a higher error in comparison with the artificial neural network model.
Keywords: artificial neural network, camera-type 3D scanner, rock mechanics, rock joint, joint roughness
Published in DiRROS: 18.01.2023; Views: 323; Downloads: 174
.pdf Full text (1,25 MB)
This document has many files! More...
Search done in 0.08 sec.
Back to top