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Povzetek: The use of geothermal energy, which comes from both deep geothermal systems and the shallow underground, has been developing rapidly in the last few decades. The purpose of the paper is to present the results of measurements of the thermal properties of all rock samples and sediments that were available from boreholes, two tunnels and numerous surface locations in Slovenia in the period from 1982 to the end of 2022. In relation to the shallow geothermal potential, a special effort is needed to characterize the thermal properties of the rocks and sediments and to implement thermal energy transfer technology. In this sense, knowledge of the thermal conductivity of rocks and sediments is required to assess the possibility of low-enthalpy heat exchange in a given local area. The largest number of measurements was taken to determine thermal conductivity. Determinations of thermal diffusivity were carried out on a much smaller number of rock and sediment samples, as well as determinations of radiogenic heat production in rocks. The results of thermal conductivity measurements on 430 samples from 119 wells, 20 samples from two tunnels and 156 samples from surface locations are shown. The highest thermal conductivities are shown by samples of dolomite, quartz conglomerate and conglomerate, phyllonite, quartz phyllite and gneiss, while the lowest are measured in sediments such as clay, lignite with clay, peat and dry sand. The determined radioactive heat generation is the lowest for milonitized dolomite and highest for dark grey sandstone with shale clasts. Our results are comparable to those already published worldwide, and they could be the basis for the possible future Slovenian standard for the thermal properties of measured rocks and sediments.
Ključne besede: thermal conductivity, thermal diffusivity, borehole, tunnel, surface, rock, sediment, radioactive heat generation, Slovenia
Objavljeno v DiRROS: 15.01.2024; Ogledov: 172; Prenosov: 89
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Povzetek: 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.
Ključne besede: artificial neural network, camera-type 3D scanner, rock mechanics, rock joint, joint roughness
Objavljeno v DiRROS: 18.01.2023; Ogledov: 335; Prenosov: 179
Celotno besedilo (1,25 MB)
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