Péter Görög

The long-term behaviour of brittle materials plays a critical role in understanding time-dependent deformation and failure. For example, it is nowadays essential in the design of a nuclear waste disposal system expected to function for a very long period. It is also crucial when the land above underground quarries that are more than a hundred years old is used as a construction zone. Although brittle materials, such as limestones, dolomites, marls, and concrete, also typically have high stiffness and strength under normal loading conditions, their long-term behaviour, including creep processes, reduces their strength properties over engineering time scales. Creep tests under constant load were performed to investigate this behaviour and to compare the properties of different materials. In brittle rocks, the creep response is commonly divided into three stages: primary (decelerating strain rate), secondary (quasi-steady strain rate), and tertiary (accelerating strain rate). The transition from secondary to tertiary creep is particularly important, as it reflects the onset of unstable crack propagation and provides a basis for predicting long-term failure. Time-dependent deformation is strongly influenced by environmental factors, including moisture, rock composition, and stress level. For example, the behaviour of water-sensitive rocks such as porous limestone, which is the host rock of the cellar system in Budapest, and marls is influenced by the water content. However, in the initial phase of the research, dry samples are tested without applying confining pressure. Samples of drilled rock cores obtained from different locations were examined under long-term uniaxial loading conditions. The normal and long-term behaviour of these materials is evaluated and compared. The results will be used in the second phase of the research to calculate the long-term stability of underground structures and determine the differences between the short-term and long-term behaviour of the cellars, taking into account the effect of surface loads.

Slope stability analysis traditionally relies on empirical classifications, analytical methods, and numerical modelling to assess the safety of rock slopes. Although these physics-based approaches provide robust results, their application in large parametric investigations and probabilistic analyses can be computationally demanding. Building upon a comprehensive geological and seismic dataset developed for the Vilonya dolomite quarry in Hungary, this study explores the capability of machine learning (ML) techniques to efficiently predict the Factor of Safety (FoS). The dataset integrates slope geometry, discontinuity characteristics, rock-mass properties derived from the Hoek–Brown and Mohr–Coulomb criteria, and seismic loading conditions representing both static and pseudo-dynamic scenarios. Four supervised learning algorithms—Random Forest, Support Vector Regression, Artificial Neural Networks, and Decision Trees—were evaluated to develop surrogate models for FoS prediction. Model performance was assessed using cross-validation to ensure robustness and generalizability. The results demonstrate that ensemble learning achieved the highest predictive accuracy, while kernel-based and neural approaches provided competitive performance. Feature importance analysis indicates that failure plane geometry and seismic loading are the primary controlling factors governing slope stability, consistent with fundamental rock mechanics principles. The findings suggest that ML-based surrogate models can significantly accelerate scenario evaluation and support probabilistic safety assessments. This data-driven framework complements conventional geotechnical methods and offers a practical tool for preliminary design, sensitivity analysis, and rapid stability screening in quarry environments subjected to seismic influence.

The paper outlines the physical parameters and textural differences of clay marl, marl, and calcareous marl, focusing on the physical changes that are caused by water. The characterization of marls is often very difficult since their properties are variable, covering a broad range from low-strength friable rock to highly cemented durable lithologies. Consequently, the prediction of their physical parameters requires further consideration. The paper addresses this issue by physically and texturally characterizing Eocene marly lithologies and comparing their properties to various limestones. Density, water absorption, ultrasonic pulse velocity, uniaxial compressive strength (UCS), Brazilian tensile strength, Young’s modulus, and their interdependencies are explained. The link between physical properties and various micro-fabrics is also outlined, with the help of new equations. Our tests show that UCS of calcareous marl can be as high as 100 MPa, but it decreases when the sample is water saturated. Relationships were calculated between the physical parameters of air-dry and water-saturated samples. Correlations between Young’s moduli and uniaxial compressive strength are also explained. The results of our analyses were compared to published physical parameters of marls and limestone, and relationships were outlined between the parameters of these carbonates.

Due to inherent geological uncertainties, slope stability is a critical factor in open-pit mining operations. These uncertainties affect stability assessments, including spatial variability, weathering, and human factors. The quarry in Vilonya, Hungary, excavates dolomite rock, characterized by fragmentation and variable joint conditions. Post-mining, stability is assessed to determine whether slopes can maintain steeper angles than the standard 45°. This study evaluates the stability of slopes in the Vilonya dolomite quarry using probabilistic methods to account for geotechnical variability and to assess the feasibility of steeper slope angles. A combination of field measurements, laboratory tests, and computational analyses were employed. Joint orientations and roughness were determined through photogrammetry and Barton comb measurements. Statistical analysis of rock parameters were done by software like Analytic Solver. Stability was analyzed using Rocscience software (Dips, RocPlane, SWedge, Slide2) for various failure mechanisms, including planar and wedge sliding, as well as global stability. Kinematic analyses identified critical joint sets that may contribute to slope failure. Probabilistic assessments showed that some joint intersections present failure probabilities as high as 67.42% for wedge failure, while planar sliding risks were negligible. Global stability analysis indicated no critical failures, with safety factors consistently above 1.35 across all slopes. Probabilistic methods reveal significant insights into slope stability that deterministic approaches may overlook. The study confirms the feasibility of maintaining steeper slope angles under controlled risk, optimizing extraction while ensuring stability. Incorporating probabilistic analysis is recommended for reliable slope design in similar geological settings.

This study presents a probabilistic geotechnical analysis of the Visonta Keleti-III lignite mining area, focusing on the statistical evaluation of soil parameters and their integration into slope stability modeling. The objective was to provide a more accurate representation of the spatial variability of geological formations and mechanical soil properties in contrast to traditional deterministic approaches. The analysis was based on over 3300 laboratory samples from 28 boreholes, processed through multi-stage outlier filtering and regression techniques. Strong correlations were identified between physical soil parameters—such as wet and dry bulk density, void ratio, and plasticity index—particularly in cohesive soils. The probabilistic slope stability analysis applied the Bishop simplified method in combination with Latin Hypercube simulation. Results demonstrate that traditional methods tend to underestimate slope failure risk, whereas the probabilistic approach reveals failure probabilities ranging from 0% to 46.7% across different sections. The use of tailored statistical tools—such as Python-based filtering algorithms and distribution fitting via MATLAB—enabled more realistic modeling of geotechnical behavior. The findings emphasize the necessity of statistical methodologies in mine design, particularly in geologically heterogeneous, multilayered environments, where spatial uncertainty plays a critical role in slope stability assessments.

This study presents a probabilistic geotechnical analysis of the Visonta Keleti-III lignite mining area, focusing on the statistical evaluation of soil parameters and their integration into slope stability modeling. The objective was to provide a more accurate representation of the spatial variability of geological formations and mechanical soil pro-perties, in contrast to traditional deterministic approaches. The analysis was based on over 3,300 laboratory samples from 28 boreholes, processed through multi-stage outlier filtering and regression techniques. Strong correlations were identified between physical soil parameters—such as wet and dry bulk density, void ratio, and plasticity index—particularly in cohesive soils. The probabilistic slope stability analysis applied the Bishop simplified method in combination with Latin Hypercube simulation. Results demonstrate that traditional methods tend to underestimate slope failure risk, whereas the probabilistic approach reveals failure probabilities ranging from 0% to 46.7% across different sections. The use of tailored statistical tools—such as Python-based filtering algorithms and distribution fitting via MATLAB—enabled more realistic modeling of geotechnical behavior. The findings emphasize the necessity of statistical methodologies in mine design, particularly in geologically heterogeneous, multilayered environments, where spatial uncertainty plays a critical role in slope stability assessments.

This study examined eight different vaulted stone bridges with 1-2-3-4 spans. On-site measurements were conducted to assess the main characteristics of the bridges, including geometric dimensions, the types, main properties of the stones used, and the existing damages. Various laboratory tests were performed on samples taken from the bridges. Using the data obtained, numerical analyses were carried out to determine the load-bearing capacity of the structures. How the existing structural damages affect the load-bearing values was investigated. Depending on the type and extent of the damage, the reduction in load-bearing capacity can range from just a few percent to as much as 90%. It was revealed that the most significant factors affecting load-bearing capacity are the geometric dimensions and the frictional interaction between the elements.

Considering the creep behavior of soft and weak rocks is critical for analyzing the long-term stability of underground constructions. This paper introduces a novel creep constitutive model to characterize the creep behavior of rocks under uniaxial and triaxial stress states. The fractional derivative Abel dashpot was used to improve the Burger model, and a viscoplastic component was added in series with the modified Burgers model to replicate the tertiary phase of rock creep. The effectiveness of the model was verified using creep test data from various soft rocks and monitoring measurements from a tunnel excavated in heavily jointed weak rock masses. Furthermore, a sensitivity analysis was carried out to assess the impact of the model parameters on creep deformation, and a comparative study was performed to evaluate the efficacy of the suggested model in modeling the accelerated stage of rock creep compared with some existing models. The strong agreement observed between the calculated results and both the creep test data and tunnel monitoring measurements underscores the accuracy and validity of the proposed model. The comparative analysis further revealed that the proposed model offers the highest fitting efficiency for describing the tertiary stage of rock creep. These findings suggest that the model effectively captures the creep behavior of rocks and precisely represents the entire creep process.

Soft rocks in various forms of marly materials have an important role in the geological structure of Dalmatia (Croatia) and Budapest region (Hungary). These materials can cause serious problems in design and construction, due to their complex behavior under atmospheric conditions. For the purpose of better understanding soft rocks behavior and their properties, a database with approximately 600 samples collected throughout Dalmatia was made and collated along with 260 samples from the Budapest area. Based on the collected data, 30 regression analyses were performed among 14 material properties, and eight different types of linear and non-linear correlations with high coefficients of determination were selected, which connect the important mechanical properties (uniaxial compressive strength and Young’s modulus) with physical properties of marls (porosity, dry unit weight, and ultrasonic pulse wave velocity). Most of the determined relations show symmetry between the marls from the Dalmatian region and the Budapest region, and the relation between dry unit weight and uniaxial compressive strength asymmetry can be observed between the marls with different origins. Obtained correlations may be used in cases where little or no information on geotechnical parameters is available, or where results need to be verified due to the problems present in soft rock testing.