@article{MTMT:37272383,
	title = {Estimation of the Elastic Modulus of Granite Under Uniaxial Compression at the Pre-Peak Stage Using the PROS Method for Approximating Experimental Data},
	url = {https://m2.mtmt.hu/api/publication/37272383},
	author = {Kolesnikov, Gennady and Shekov, Vitali},
	doi = {10.3390/app16052571},
	journal-iso = {APPL SCI-BASEL},
	journal = {APPLIED SCIENCES-BASEL},
	volume = {16},
	unique-id = {37272383},
	keywords = {uniaxial compression; PROS; tangent modulus of elasticity; influence of subjective factors; approximating equation},
	year = {2026},
	eissn = {2076-3417}
}

@article{MTMT:36454998,
	title = {New Insights Into Poisson's Ratio Variability in Rocks Under Load},
	url = {https://m2.mtmt.hu/api/publication/36454998},
	author = {Narimani Ghourtlar, Samad and Vásárhelyi, Balázs},
	doi = {10.1002/nag.70146},
	journal-iso = {INT J NUMER ANAL MET},
	journal = {INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS},
	volume = {50},
	unique-id = {36454998},
	issn = {0363-9061},
	abstract = {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.},
	keywords = {STRESS; FRACTURE; VALUES; PROPAGATION; INITIATION; THRESHOLDS; Empirical model; Materials Science, Multidisciplinary; Poisson's ratio; Engineering, Geological; crack initiation stress; crack damage stress},
	year = {2026},
	eissn = {1096-9853},
	pages = {854-869},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}

@inproceedings{MTMT:36213605,
	title = {Predicting the Crack Stress Thresholds of the Intact Granitic Rocks by machine learning and multivariate analysis techniques},
	url = {https://m2.mtmt.hu/api/publication/36213605},
	author = {Narimani Ghourtlar, Samad and Vásárhelyi, Balázs and Davarpanah, S.M.},
	booktitle = {ISRM International Symposium Eurock 2025 : Expanding the Underground Space},
	unique-id = {36213605},
	abstract = {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.},
	year = {2025},
	pages = {1-8},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}

@article{MTMT:36127413,
	title = {An innovative method to determine the stress-dependency of Poisson’s ratio of granitic rocks},
	url = {https://m2.mtmt.hu/api/publication/36127413},
	author = {Narimani Ghourtlar, Samad and Kovács, László and Vásárhelyi, Balázs},
	doi = {10.1038/s41598-024-75892-2},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {15},
	unique-id = {36127413},
	abstract = {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.},
	year = {2025},
	eissn = {2045-2322},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}

@article{MTMT:35667976,
	title = {Leveraging machine learning for precision prediction of geomechanical properties of granitic rocks: a comparative analysis of MLR, ANN, and ANFIS models},
	url = {https://m2.mtmt.hu/api/publication/35667976},
	author = {Narimani Ghourtlar, Samad and Vásárhelyi, Balázs},
	doi = {10.1007/s12145-024-01653-4},
	journal-iso = {EARTH SCI INF},
	journal = {EARTH SCIENCE INFORMATICS},
	volume = {18},
	unique-id = {35667976},
	issn = {1865-0473},
	abstract = {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.},
	keywords = {Hungary; Regression Analysis; Sensitivity analysis; machine learning; uniaxial compressive strength; Compressive Strength; Young's modulus; shear strength; P-wave; Machine learning methods; Multivariate regression analysis; reservoir rock; Intact granitic rock},
	year = {2025},
	eissn = {1865-0481},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}

@article{MTMT:36471713,
	title = {Mineralogical Characteristics and Critical Stress Variation in Rock Samples from Bukit Kunyit, Bandar Lampung},
	url = {https://m2.mtmt.hu/api/publication/36471713},
	author = {Setiawan, Muhamad Ragil and Hilman, Zaki and Haqiqi, Rian Bayu and Farishi, Bilal Al},
	doi = {10.35472/jsat.v9i1.2026},
	journal-iso = {JSAT},
	journal = {JOURNAL OF SCIENCE AND APPLICATIVE TECHNOLOGY},
	volume = {9},
	unique-id = {36471713},
	year = {2025},
	eissn = {2581-0545}
}

@article{MTMT:35380954,
	title = {Integrating the Finite Element Method with Python Scripting to Assess Mining Impacts on Surface Deformations},
	url = {https://m2.mtmt.hu/api/publication/35380954},
	author = {Dudek, Mateusz and Mrochen, Dawid and Sroka, Anton and Tajdus, Krzysztof},
	doi = {10.3390/app14177797},
	journal-iso = {APPL SCI-BASEL},
	journal = {APPLIED SCIENCES-BASEL},
	volume = {14},
	unique-id = {35380954},
	keywords = {FEM; Coal Mining; Surface deformation; Python; strong layer},
	year = {2024},
	eissn = {2076-3417}
}

@article{MTMT:34234011,
	title = {Estimation of the Poisson's Ratio of the Rock Mass},
	url = {https://m2.mtmt.hu/api/publication/34234011},
	author = {Narimani Ghourtlar, Samad and Davarpanah, Seyed Morteza and Vásárhelyi, Balázs},
	doi = {10.3311/PPci.22689},
	journal-iso = {PERIOD POLYTECH CIV ENG},
	journal = {PERIODICA POLYTECHNICA-CIVIL ENGINEERING},
	volume = {68},
	unique-id = {34234011},
	issn = {0553-6626},
	abstract = {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.},
	keywords = {STRESS; YOUNGS MODULUS; strength; Rock mass classification; Rock mass; Poisson's ratio},
	year = {2024},
	eissn = {1587-3773},
	pages = {274-288},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}

@article{MTMT:34567022,
	title = {Borehole Analysis with the Modification of RQD Value},
	url = {https://m2.mtmt.hu/api/publication/34567022},
	author = {Somodi, Gábor and Vásárhelyi, Balázs},
	doi = {10.3390/geotechnics3040055},
	journal-iso = {GEOTECHN},
	journal = {GEOTECHNICS},
	volume = {3},
	unique-id = {34567022},
	abstract = {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.},
	year = {2023},
	eissn = {2673-7094},
	pages = {1017-1032},
	orcid-numbers = {Vásárhelyi, Balázs/0000-0002-0568-1031}
}