20.500.12556/DiRROS-16013-ee24446a-668f-c9fb-4368-ccc623fca89d Prediction of the peak shear strength of the rock joints with artificial neural networks Napoved vrhunske strižne trdnosti po razpoki v kamnini z nevronskimi mrežami 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. Nevronske mreže postajajo z razvojem računalniške tehnologije vedno bolj uporabne tudi na področju inženirske geologije in geotehnike. Z nevronskimi mrežami lahko na osnovi večjega števila podatkov napovemo geomehanske lastnosti kamnine ali njihovo obnašanje v različnih napetostnih pogojih. Porušitve brežin ali podzemnih prostorov v kamninskem masivu se večinoma pojavijo po razpokah, zato so strižne lastnosti v razpokah ali prelomih bistvene za stabilnost geotehničnih objektov. Preiskave strižnih lastnosti so večinoma dolgotrajne, prav tako pa je pri vrtanju v fazi raziskav težko pridobiti primerne vzorce. Velik vpliv na velikost vrhunske strižne trdnosti ima hrapavost površine razpoke, natezna trdnost in vertikalna obremenitev. V predstavljenem članku je hrapavost površine razpok izmerjena s fotogravimetričnim skenerjem, vrhunska strižna trdnost pa je določena z Robertsonovo direktno strižno preiskavo. Na osnovi šestih vhodni karakteristik razpok in kamnine ter izmerjene strižne trdnosti z Robertsonovo preiskavo, lahko z naučeno nevronsko mrežo uspešno napovemo vrhunsko strižno trdnost po razpoki. Tako naučena nevronska mreža lahko dovolj natančno napove vrhunsko strižno trdnost za podobne litološke razmere in geološke pogoje, z upoštevanjem dokaj nizke napake, to je 6%. Rezultate izračuna z nevronskimi mrežami smo primerjali z eksperimentalnim modelom, ki je v primerjavi z nevronskimi mrežami pokazal višjo napako napovedi vrhunske strižne trdnosti. artificial neural network camera-type 3D scanner rock mechanics rock joint joint roughness nevronska mreža mehanika kamnin razpoke hrapavost razpok 3D skener s kamero true false true Geološki zavod Slovenije Angleški jezik Slovenski jezik © Author(s) 2022. Neznano 2023-01-06 03:34:50 2023-01-18 09:36:18 2024-08-06 09:08:38 0000-00-00 00:00:00 2022 0 0 str. 149-158 no. 2 vol. 65 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-11-18 624 0016-7789 10.5474/geologija.2022.009 129981955 https://www.geologija-revija.si/index.php/geologija/article/view/1839/1904 1 2ec0f626-970b-11ed-b359-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=21377 1839-Article_Text-12342406-1-10-20221118.pdf 1839-Article_Text-12342406-1-10-20221118.pdf 1 80F64BC9F1D28397E389BFEE0323FE9E a4b7bac7968ed3744979e5f35fb895bdc26ebf0576b0862db00c13b357ac08b9 1d46d3dc-970c-11ed-b359-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=21378 Zavod za gradbeništvo Slovenije Geologija 0 0 0