TY - JOUR AU - Kolesnikov, Gennady AU - Shekov, Vitali TI - Estimation of the Elastic Modulus of Granite Under Uniaxial Compression at the Pre-Peak Stage Using the PROS Method for Approximating Experimental Data JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 16 PY - 2026 IS - 5 PG - 21 SN - 2076-3417 DO - 10.3390/app16052571 UR - https://m2.mtmt.hu/api/publication/37272383 ID - 37272383 LA - English DB - MTMT ER - TY - JOUR AU - Narimani Ghourtlar, Samad AU - Vásárhelyi, Balázs TI - New Insights Into Poisson's Ratio Variability in Rocks Under Load JF - INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS J2 - INT J NUMER ANAL MET VL - 50 PY - 2026 IS - 2 SP - 854 EP - 869 PG - 16 SN - 0363-9061 DO - 10.1002/nag.70146 UR - https://m2.mtmt.hu/api/publication/36454998 ID - 36454998 AB - Poisson's ratio is a fundamental parameter in rock mechanics, traditionally often treated as a constant. This study challenges that notion by developing a universal empirical model that captures its significant stress-dependency from initial loading to failure. The model was rigorously calibrated using 42 uniaxial compressive tests on granite and successfully generalized to limestone, marl, sandstone, and saltstone, representing a wide spectrum of mechanical behaviors from brittle to ductile. Its predictive accuracy was quantified using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The model demonstrated high fidelity, particularly at the critical crack initiation stage (sigma/sigma c = 0.5), where errors were minimized. Across all five rock types and three calculation methods (secant, average, tangent), the MAPE ranged from 1.4% to 3.7%, RMSE from 0.007 to 0.012, and MAE from 0.005 to 0.010. Performance was superior for ductile rocks like marl (MAPE approximate to 1.9%) and saltstone (MAPE approximate to 1.4%) due to their homogeneous deformation, while effectively capturing the more complex, crack-dominated response of granite and sandstone. The research conclusively demonstrates that Poisson's ratio is not a fixed material property but a dynamically evolving parameter. This work provides a practical and robust framework that moves beyond the assumption of a constant Poisson's ratio, offering a significant improvement for accurate deformation analysis in numerical modeling, underground excavation design, and reservoir geomechanics. LA - English DB - MTMT ER - TY - CHAP AU - Narimani Ghourtlar, Samad AU - Vásárhelyi, Balázs AU - Davarpanah, S.M. ED - Kristin, Hilde Holmøy ED - Javier, Macias ED - Are, Håvard Høien ED - Mari, Lie Arntsen TI - Predicting the Crack Stress Thresholds of the Intact Granitic Rocks by machine learning and multivariate analysis techniques T2 - ISRM International Symposium Eurock 2025 : Expanding the Underground Space PB - Norsk Betongforening CY - Oslo SN - 9788282080798 PY - 2025 SP - 1 EP - 8 PG - 8 UR - https://m2.mtmt.hu/api/publication/36213605 ID - 36213605 AB - This research aims to significantly advance the understanding of crack stress thresholds in rocks by utilizing intelligent and statistical methodologies applied to a comprehensive database of granitic rocks. The database includes essential mechanical parameters, such as crack initiation stress and crack damage stress, as outputs, and unconfined compressive stress, Young's modulus, and Poisson's ratio as inputs. The study employs multivariate regression analysis (MRA), Decision tree (DT), and artificial neural network (ANN) techniques to analyze the data. To assess and compare the performance of these models, various metrics are used, including root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), mean absolute percent error (MAPE) and the a20-index. The findings reveal that the ANN method surpasses MRA-based and decision tree regression-based approaches in accurately predicting the output parameters. The ANN model demonstrates outstanding predictive performance for σci, achieving an R²=0.934, RMSE=5.965, MAE=4.79, MAPE=5.3% and VAF=93.34%. Similarly, the model excels in predicting σcd with an R²=0.985, RMSE=4.676, MAE=3.785, MAPE=2.3%, VAF=98.4%, Additionally, sensitivity analysis identifies unconfined Compressive stress (UCS) as the most critical factor in predicting crack initiation stress (σci) and crack damage stress (σcd). The findings suggest that the ANN model is more effective in predicting σci and σcd of rocks compared to other methods. LA - English DB - MTMT ER - TY - JOUR AU - Narimani Ghourtlar, Samad AU - Kovács, László AU - Vásárhelyi, Balázs TI - An innovative method to determine the stress-dependency of Poisson’s ratio of granitic rocks JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 15 PY - 2025 IS - 1 PG - 15 SN - 2045-2322 DO - 10.1038/s41598-024-75892-2 UR - https://m2.mtmt.hu/api/publication/36127413 ID - 36127413 AB - Uniaxial Compressive test (UCS) results are essential in evaluation the values of Poisson’s ratio. However, according to the suggestion of the International Society for Rock Mechanics, Poisson’s ratio can be determined using three alternative methods: the secant, average and tangent. Applying these methods causes discrepancies in the results; according to our experiences, the differences can be threefold or more. This paper aims to study the process of changes of Poisson’s ratio for intact rock during loading from micro-crack initiation to failure stage. The objective of this theoretical investigation is to establish a straightforward mathematical formulation between σ/σ c and intact rock’s Poisson’s ratio value. To outline these changes forty two granite rocks were investigated from Bátaapáti radioactive waste repository (Hungary) and the calculation was performed by using the new formula from the beginning of loading till failure stage at UCS test. In the laboratory test program, Poisson’s ratio derived from standard tests varies with momentary stress; it steadily increases as stress rises until reaching the stress level causing unstable crack propagation. Additionally, the Poisson’s rate follows a linear increase with stress, up to the point of unstable crack propagation stress. The research demonstrated that the proposed equations provide competent values for the root mean squared error value (ranging from 0 to 0.04), the mean absolute percentage error (ranging from 0.6% to 18%) and the mean absolute error (ranging from 0 to 0.04). Contrary to previous ideas, our results suggest that the Poisson’s ratio is not a constant for rigid rocks. LA - English DB - MTMT ER - TY - JOUR AU - Narimani Ghourtlar, Samad AU - Vásárhelyi, Balázs TI - Leveraging machine learning for precision prediction of geomechanical properties of granitic rocks: a comparative analysis of MLR, ANN, and ANFIS models JF - EARTH SCIENCE INFORMATICS J2 - EARTH SCI INF VL - 18 PY - 2025 IS - 1 PG - 27 SN - 1865-0473 DO - 10.1007/s12145-024-01653-4 UR - https://m2.mtmt.hu/api/publication/35667976 ID - 35667976 AB - Geomechanical properties such as uniaxial compressive strength (UCS) and modulus of elasticity (E) are essential for evaluating the mechanical behavior of rocks and crucial for rock engineering projects. Researchers frequently estimate these values indirectly because it is not always feasible to get high-quality core samples. Four machine learning models including multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were introduced and employed to examine the prediction of UCS and E in 221 granitic rock samples from Bataapáti nuclear reservoir in Hungary. The variables to calculate UCS and E are: density, P-wave velocity, shear strength and Brazilian tensile strength. The study compared the performance of the proposed machine learning models and found that the ANFIS model had obtained the most satisfactory prediction accuracy. Based on the sensitive analysis, the BTS is the most important variable for predicting UCS and Vp for E. Six popular performance indices, including the root-mean-square error (RMSE), mean absolute error (MAE), the coefficient of determination (R2), mean absolute percent error (MAPE), variance accounts for (VAF) and a20-index were adopted to evaluate all models. The ANFIS model demonstrates outstanding predictive performance for UCS, achieving an R²=0.99, RMSE = 2.445, MAE = 1.489, MAPE = 1.3%, VAF = 99.16%, and an a20-index of 1 for the training datasets. For the testing datasets, it attains an R²=0.97, RMSE = 5.544, MAE = 3.502, MAPE = 3.1%, VAF = 96.43%, and an a20-index of 1. Similarly, the model excels in predicting E with an R²=0.93, RMSE = 3.914, MAE = 2.91, MAPE = 5.2%, VAF = 92.23%, and an a20-index of 1 for training datasets, and an R²=0.92, RMSE = 3.195, MAE = 2.613, MAPE = 4.4%, VAF = 92.22%, and an a20-index of 1 for testing datasets. Also, a sequence of Taylor diagrams was generated to assess the performance of the models. The findings suggest that the ANFIS model is more effective in predicting UCS and E of rocks compared to other methods. However, its application should be approached cautiously, especially at the initial design phase, and limited to specific rock types. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. LA - English DB - MTMT ER - TY - JOUR AU - Setiawan, Muhamad Ragil AU - Hilman, Zaki AU - Haqiqi, Rian Bayu AU - Farishi, Bilal Al TI - Mineralogical Characteristics and Critical Stress Variation in Rock Samples from Bukit Kunyit, Bandar Lampung JF - JOURNAL OF SCIENCE AND APPLICATIVE TECHNOLOGY J2 - JSAT VL - 9 PY - 2025 IS - 1 SN - 2581-0545 DO - 10.35472/jsat.v9i1.2026 UR - https://m2.mtmt.hu/api/publication/36471713 ID - 36471713 LA - English DB - MTMT ER - TY - JOUR AU - Dudek, Mateusz AU - Mrochen, Dawid AU - Sroka, Anton AU - Tajdus, Krzysztof TI - Integrating the Finite Element Method with Python Scripting to Assess Mining Impacts on Surface Deformations JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 14 PY - 2024 IS - 17 PG - 18 SN - 2076-3417 DO - 10.3390/app14177797 UR - https://m2.mtmt.hu/api/publication/35380954 ID - 35380954 N1 - Funding Agency and Grant Number: Ministry of Education and Science; Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) [PLG/2023/016419] Funding text: Part of the work in this paper was carried out in 2022 as part of the statutory work of the Strata Mechanics Research Institute of Polish Academy of Sciences in Krakow, financed by the Ministry of Education and Science. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016419. LA - English DB - MTMT ER - TY - JOUR AU - Narimani Ghourtlar, Samad AU - Davarpanah, Seyed Morteza AU - Vásárhelyi, Balázs TI - Estimation of the Poisson's Ratio of the Rock Mass JF - PERIODICA POLYTECHNICA-CIVIL ENGINEERING J2 - PERIOD POLYTECH CIV ENG VL - 68 PY - 2024 IS - 1 SP - 274 EP - 288 PG - 15 SN - 0553-6626 DO - 10.3311/PPci.22689 UR - https://m2.mtmt.hu/api/publication/34234011 ID - 34234011 AB - The value of Poisson's ratio is a crucial parameter in rock mechanics and engineering for both intact rock and rock mass. Poisson's ratio has not gotten the attention it merits compared to other essential mechanical characteristics of intact rock and rock mass. Limited relationships exist between rock mass classification systems (such as RMR, RMQR, Q, and GSI) and Poisson's ratio. This paper provides a comprehensive review of models proposed by various researchers for estimating Poisson's ratio for rock mass. The different methods are compared, and new general equations are derived. The results indicate that the Poisson's ratio value of rock mass is inversely proportional to its quality and strength and depends on the Poisson's ratio value of the intact rock. Specifically, a linear equation is obtained using the RMR or GSI system, showing that the Poisson's ratio increases as the quality and strength of the rock mass decrease. The Q system has a logarithmic link between the rock mass quality and Poisson's ratio. It should be noted that the derived equations are applicable only under the assumption of a homogeneous isotropic rock mass. LA - English DB - MTMT ER - TY - JOUR AU - Somodi, Gábor AU - Vásárhelyi, Balázs TI - Borehole Analysis with the Modification of RQD Value JF - GEOTECHNICS J2 - GEOTECHN VL - 3 PY - 2023 IS - 4 SP - 1017 EP - 1032 PG - 16 SN - 2673-7094 DO - 10.3390/geotechnics3040055 UR - https://m2.mtmt.hu/api/publication/34567022 ID - 34567022 AB - The most common classification method of drill cores is the Rock Quality Designation (RQD) value, which indicates the percentage of rock cores longer than 10 cm in a given core section. This core logging procedure is the basic parameter in the most useful rock mass classification methods like Rock Mass Rate (RMR) and Rock Mass Quality (Q). It is also used to determine the Geological Strength Index (GSI), which has become widely used in the last 20 years. One of the basic problems of the RQD value is that it does not distinguish different rock cores longer than 10 cm (100% is obtained for one piece of 1 m length and 10 pieces of 10 cm length) and a uniform result is obtained for shorter units. In this paper, the so-called Integrated RQD (Int_RQD) factor is introduced to eliminate these problems and to provide a better description of fracture density in the core logging procedure. As it uses the original core logging procedure, historical RQD data can also be reevaluated. Considering that RQD is an input parameter for most rock engineering classifications, these systems such as GSI can be reviewed based on the new RQD definition proposed herein. LA - English DB - MTMT ER -