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      <abstractText>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.</abstractText>
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      <abstractText>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.</abstractText>
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2025.12.03. 10:05 Andódy Katalin (BME admin4)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: 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 ...&lt;/pre&gt;

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      <abstractText>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.</abstractText>
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