Mateusz Janiszewski

Fluid flow through fractured rock networks is controlled by several factors, including hydraulic pressure gradients, fracture density, roughness, aperture, connectivity, and intersections. Characterizing hydraulic behaviour at fracture intersections is essential for understanding flow in rough fractured rock masses, especially for nuclear waste disposal. In this study, a block of Finnish Kuru granite (10 cm × 10 cm × 10 cm) was prepared with two perpendicular and intersecting fractures. Photogrammetry was applied to reconstruct three-dimensional (3D) point clouds of the sample. To scale 3D models obtained by photogrammetry, markers with predefined distances were used, and accuracy was assessed by using Root Mean Squared Error (RMSE). Subsequently, roughness and physical aperture of fractures were quantified. The 3D point clouds were converted into a mesh domain in MeshInspector and imported into COMSOL Multiphysics, where fluid flow was simulated by solving the Navier–Stokes equations. Simulations were conducted under hydraulic pressure gradients ranging from 10 to 180 kPa/m, in increments of 10 kPa/m. The influence of intersections on flow for two inlets and two outlets was investigated. The Forchheimer equation was used to analyze the numerical results. The photogrammetric reconstruction accuracy yielded an RMSE of 15 μm. Physical apertures for the two inlets were 190 and 192 μm, while for Outlets 1 and 2, they were 177 and 192 μm, respectively. 3D Roughness values were similar, ranging from 8.7 to 9.3. Numerical results showed nonlinear flow behavior at the outlets. The Forchheimer fitting was used to calculate hydraulic apertures and nonlinear parameters. A higher flow rate with a larger physical aperture, demonstrating the role of geometry in flow distribution. This research shows that fracture intersections influence fluid flow behavior, leading to discharge changes based on geometrical properties, such as physical aperture. These factors must be considered in fracture network flow analysis.

Rock mass characterisation, specifically characterising fracture distribution, density, and orientation, is crucial for a better understanding of stability and hydraulic properties. There are several methods for characterising fracture networks, such as manual mapping and remote sensing. This paper presents a proof-of-concept workflow for digital rock-mass analysis using a 360° imaging system photogrammetry combined with a three-dimensional Gaussian Splatting (3DGS). The method was tested on a tunnel drift (Site 1) and a 20-meter wall section (Site 2) in the Underground Research Laboratory of Aalto University (URLA). A compact 360° camera was used to acquire continuous panoramic image sequences of two sites in the tunnel. These images were processed into a 3D representation via photogrammetry combined with 3DGS, providing both accurate surface geometry and highly efficient and hyper-realistic real-time rendering. Fractures and discontinuity sets were extracted from the reconstructed surfaces using established methods such as 3D point cloud processing and semi-automatic clustering of fracture planes, allowing 3D fracture orientation measurements. The resulting orientations of discontinuity sets measured digitally from the 360° camera-based 3D point cloud differ 3.4° on average compared to previous measurements obtained from laser scanning and photogrammetry data. The 3D point cloud coloured by discontinuity sets was imported into Blender and visualised on the 3DGS. This enabled users to inspect the tunnel digitally, visualise structural features, and review spatial data in high resolution. The study demonstrates that a functional end-to-end digital rock-mass sensing pipeline can be assembled using an inexpensive 360° camera and existing software tools, offering a practical foundation for advancing automated rock-mass intelligence in future mining and tunnelling applications.

Geotechnical core logging provides crucial input for rock engineering, including mine design, stability assessment, and rock-mass classification. Yet the process remains prone to human error and lacks reproducibility. Core box photographs, widely collected in the industry, represent a largely underutilised primary data source for recomputing geotechnical metrics. This study investigates the practical benefits of applying artificial intelligence (AI) to such images, focusing on reproducibility and enabling multiple rock-mass characterisation workflows from metric-agnostic outputs. An AI-based instance segmentation method (Segment Anything Model; SAM), combined with context-specific filtering, was applied to an image dataset from the Boliden Zinkgruvan mine, Sweden. The workflow automatically extracts the intact core pieces in each image, digitally measures their lengths, and produces a fracture-position database. Natural and mechanical fractures are then differentiated using markings produced by a geologist, enabling computation of rock quality designation (RQD). The results are compared with manual measurements. The workflow achieved a mean absolute difference between AI-computed and manually measured RQD of 7.8 percentage points. Outlier analysis suggested that large discrepancies between manual and computed RQD may be related to human error, indicating that AI-assisted methods can support quality assurance/quality control (QA/QC) of manual logs. The results demonstrate that recording fracture positions rather than interval-based metrics reported over fixed lengths (e.g., RQD every 1 m) enables transparent recalculation of RQD and supports the derivation of additional metrics such as fracture frequency (P10). This highlights how AI-assisted fracture extraction can increase reproducibility in core logging and produce design-independent data that can be transformed into multiple geotechnical metrics and used across different analysis frameworks (empirical methods, numerical simulations, DFNs). The findings confirm that AI-assisted core logging enables reproducible downstream metric computation and design-independent rock-mass metrics from standard core photographs, aiding QA/QC of manual logs and use across different analysis frameworks.

Lined rock caverns (LRCs) are a promising solution for high-pressure hydrogen storage in crystalline bedrock, where early-stage design requires rapid assessment of geometry-dependent boundary stresses. Existing approaches are either analytically rigorous but impractical for scoping, or flexible but computationally expensive. This study presents a linear-elastic approximation for estimating boundary stresses in ellipsoidal caverns by superimposing classical Inglis solutions and applying a geometric reprojection to approximate three-dimensional effects. Internal pressure is incorporated through FEM calibrated stress intensity factors. The method is evaluated against three-dimensional finite element models over a wide range of aspect ratios and stress conditions representative of Finnish bedrock. For far-field loading, the approach reproduces numerical results with typical errors of 2-5 MPa. For pressurised cases, the inclusion of stress intensity factors improves agreement, reducing errors to below 1 MPa for roof stresses and 0.5-1 MPa for wall stresses. The results indicate that far-field stress redistribution can be approximated reasonably using reprojected two-dimensional solutions, whereas pressure-induced stresses require curvature-dependent correction. The proposed method provides a fast tool for early-stage design and parametric screening, while identifying conditions where full numerical modelling remains necessary.

Fractures significantly impact fluid flow in rock masses and comprehending how dislocations influence fracture-controlled flow is important for evaluating fluid movement in geological engineering applications, particularly for the long-term safety of geological nuclear waste disposal sites. In this method, A 6 cm × 6 cm × 10 cm Kuru granite specimen containing an induced tensile fracture was scanned using photogrammetry. Photogrammetry was used to capture the fracture surfaces and aperture geometries of the perfectly matched state and each dislocation step of the specimen’s upper half, using markers with known distances to accurately align and scale the dislocated surfaces. The 3D models of the upper and lower surfaces of the physical aperture were then modelled for each dislocation step and reproduced using Fused Deposition Modelling (FDM) 3D printing. The printed aperture models incorporated an inlet and outlet to allow fluid flow testing within a fully watertight assembly, enabling precise measurements through accurately replicated apertures. This configuration provided controlled experimental conditions and enhanced the reliability and precision of the flow data. A custom fluid-flow apparatus was developed to measure flow rates through the replicas at varying water pressures, enabling experimental examination of the effects of dislocation on fluid-flow behaviour. Further results were evaluated through the Forchheimer equation for hydraulic aperture calculation. The fluid flow tests revealed that increasing fracture dislocation results in higher flow rates, following a nonlinear relationship with the pressure gradient and leads to increased hydraulic apertures up to the tested limit of 5 mm dislocation. However, limitations related to the replication of fracture surfaces and physical apertures in 3D printing and photogrammetry must be assessed to ensure the accuracy of the results.

Sensors are a key component of virtual reality (VR) technology, as they enable motion tracking, interaction with the environment, and realistic representation of user behaviour in virtual space. VR technology is gaining increasing importance in engineering, offering new ways to support research, analysis, and training. This article examines its applications in four key areas: surveying and geomatics, mining, environmental protection, and occupational safety. The study is based on a review of the scientific literature indexed in the Scopus database, with the aim of highlighting both the potential of VR and directions for its future development. The findings indicate that VR provides effective tools for analyzing, interpreting, and visualizing complex geospatial data. It enables realistic simulations of mining processes, supports the monitoring of environmental impacts, and facilitates environmental education by creating engaging, immersive experiences. In occupational safety, VR allows hazard scenarios and accident events to be reproduced in a safe yet highly realistic environment, significantly enhancing the effectiveness of training. This is made possible through the integration of sensors with virtual reality, further enhancing immersion in the environment. Despite these advantages, several barriers have been identified. They include technological challenges, insufficient numbers of trained specialists, health and ergonomics concerns, resistance to organizational change, ethical considerations, and limited funding. It is clear that the future of VR in engineering will be shaped by continuous technological progress combined with growing attention to behavioural aspects of training and user interaction. These trends are expected to drive the creation of increasingly advanced and effective tools. The article thus provides a foundation for further exploration of VR as an integral part of engineering practice.

Innovative technologies have been supporting the improvement of comfort and safety at work in construction and mining, which are classified as high-risk sectors, for many years. A two-part study (Part I and Part II) was conducted in which the implementation of innovative technologies was analysed and evaluated (opportunities and limitations). In Part II, the technologies employed in the work environment by employees to enhance their comfort and safety at work were analysed. These technologies encompass virtual and augmented reality, innovative personal and collective protective equipment, and exoskeletons. Following a thorough analysis of the extant scientific literature from the Scopus database, it was determined that there were research gaps that required attention. In addition to the evident advantages of enhancing the safety of workers, innovative technological solutions also engender numerous economic benefits for employers, which impact sustainable development in enterprises. In order to fully exploit the potential of modern technologies, it is necessary to continue their integration and overcome implementation barriers, such as the need for changes in education and training, adequate funding, and the development of safety awareness and culture in companies.

Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons. Due to the extensive nature of the obtained materials, the research description has been divided into two articles (Part I and Part II). This article presents the first three technologies. After the scientific literature from the Scopus database was analysed, some research gaps that need to be filled were identified. In addition to the obvious benefits of increased occupational safety for workers, innovative technological solutions also offer employers several economic advantages that affect the industry’s sustainability. Innovative technologies are playing an increasingly important role in improving safety in mining and construction. However, further integration and overcoming implementation barriers, such as the need for changes in education, are needed to realise their full potential.

The necessity to ensure safety in the work environment is a priority for the functioning of mines as enterprises with a high level of exposure to hazards. The basis for this is the identification of hazards to which mine workers are exposed. This article aims to identify and systematize safety hazards present in quarries. Based on a review of the literature, hazards were identified and classified into six groups: natural, environmental, technical and technological, human, organizational, and others. Additionally, a correlation was established between the identified hazards and the stages of the mine’s life cycle, as well as the methods of deposit extraction in quarries. The results presented in the article systematize existing knowledge on industry-specific hazards. The results can serve as a source of information when developing occupational risk documentation and its analysis, and form the basis for research on the causes and consequences of accidents in mining. Identifying hazards can also help effectively monitor them effectively and implement preventive measures to protect workers and reduce accident risks in quarries.