Search Results (14)

This study investigates the characteristics of secondary fragmentation and fines generation in cave mining through DEM simulations. The objective is not to develop a tool for accurately estimating fragmentation observed at drawpoints. Instead, the research focuses on an improved understanding of the impact of critical parameters (tensile strength, damping coefficients, and micro-defects) on secondary fragmentation characteristics. Attempting to predict outcomes without first comprehending the underlying mechanisms risks oversimplifying complex mine-scale conditions. The analysis shows that tensile failure is the dominant mechanism governing fragmentation. Size-distribution curves of fragmented blocks under impact breakage demonstrate a concave-up exponential relationship between percentage mass passing at 1/10th of the original size ($t_{10}$) and kinetic energy. Furthermore, the analysis of compression-induced breakage highlights the significant role of tensile strength and micro-defects in determining the extent of fragmentation under different conditions. By better understanding these underlying mechanisms, the research establishes a solid foundation for predicting fines generation and ultimately enhancing decision making and operational strategies in mining. Full article

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied. Full article

Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM. Full article

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering’s use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage). Full article

Over the past decades, numerical modelling has become a powerful tool for rock mechanics applications. However, the accurate estimation of rock mass input parameters remains a significant challenge. Machine learning (ML) tools have recently been integrated to enhance and accelerate numerical modelling processes. In this paper, we demonstrate the novel use of ML tools for calibrating a state-of-the-art three-dimensional (3D) finite-element (FE) model of a kinematic structurally controlled failure event in an open-pit mine. The failure event involves the detachment of a large wedge, thus allowing for the accurate identification of the geometry of the rock joints. FE models are automatically generated according to estimated ranges of joint input parameters. Subsequently, ML tools are used to analyze the synthetic data and calibrate the strength parameters of the rock joints. Our findings reveal that a relatively small number of models are needed for this purpose, rendering ML a highly useful tool even for computationally demanding FE models. Full article

In observational method projects in geotechnical engineering, the final geotechnical design is decided upon during actual construction, depending on the observed behavior of the ground. Hence, engineers must be prepared to make crucial decisions promptly, with few available guidelines. In this paper, we propose coupling numerical analysis with machine learning (ML) algorithms for enhancing the decision process in observational method projects. The proposed methodology consists of two main computational steps: (1) data generation, where multiple numerical models are automatically generated according to the anticipated range of input parameters, and (2) data analysis, where input parameters and model results are analyzed with ML models. Using the case study of the Semel tunnel in Tel Aviv, Israel, we demonstrate how this computational process can contribute to the success of observational method projects through (1) the computation of feature importance, which can assist with better identifying the key features that drive failure prior to project execution, (2) providing insights regarding the monitoring plan, as correlative relationships between various results can be tested, and (3) instantaneous predictions during construction. Full article

Volumetric fracture intensity (P₃₂) is a parameter that plays a major role in the mechanical and hydraulic behaviour of rock masses. While methods such as Ground Penetrating Radar (GPR) are available to map the 3D geometrical characteristics of the fractures, the direct measurement of P₃₂ at a resolution compatible with geotechnical applications is not yet possible. As a result, P₃₂ can be estimated from the borehole and surface data using either simulation or analytical solutions. In this paper, we use Discrete Fracture Network (DFN) models to address the problem of estimating P₃₂ using information from boreholes (1D data). When calculating P₃₂ based on Terzaghi Weighting, it is common practice to use drill run lengths and limit the minimum angle between the borehole and the intersected fractures. The analysis presented in this paper indicated that limiting the minimum angle of intersection would result in an underestimation of the calculated P₃₂. Additionally, the size of the interval has a significant impact on the variability of the calculated P₃₂. We propose a methodology to calculate P₃₂ using variable lengths, depending on the angle between the fractures and the borehole. This methodology allows the capture of the spatial variation in intensity and simultaneously avoids artificially increasing or decreasing the intensity sampled along borehole intervals. Additionally, this work has addressed the impact of boundary effects in DFN models and proposes a methodology to mitigate them. Full article

Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation. Full article

Smuggling and warfare tunnels are unique structures that have rarely been studied from an engineering perspective. A notable example is the vast networks of tunnels that were secretly constructed underneath the Gaza Strip. Particularly because these tunnels were not designed and constructed via traditional engineering practice, they constitute an interesting case study. The tunnels are supported by thin precast concrete elements, with the wall elements being the critical structural element. While some instances of structural failure and collapse have been reported in the media, a great number of the tunnels have remained stable. In this paper, we attempt to conduct a forward analysis to estimate the load and response of the wall elements. We estimate the range of problem input parameters based on multiple sources, including media accounts, geological research papers, and geotechnical reports obtained from the vicinity of the Gaza tunnels. The problem is then analyzed using two approaches: (1) a simplified structural analysis based on lateral earth-pressure theory and (2) numerical modeling. Both analysis methods show that the wall elements should fail due to compression even under the most favorable estimates of input parameters, in contrast to actual reality. We discuss possible explanations for this disparity. While it is not possible to pinpoint the exact explanation, we argue that current geotechnical practice is generally biased toward conservatism, even prior to the application of safety factors. Full article

One can only know where a rock bridge is once one measures it. In addition, to measure it, you need the rock mass to fail. This critical problem is ignored by many, and engineers continue to refer to rock bridges as geometrical distances between non-persistent fractures. This paper argues that this rather simplistic approach can lead to non-realistic failure mechanisms. We also raise the critical question of whether the inappropriate functioning of strength equations centred on the measurement of rock bridge percentages could result in misinterpreting the risk of failure. We propose a new interpterion, aptly called the Bologna Interpretation, as an analogy to the Copenhagen Interpretation of quantum mechanics, to highlight the indeterministic nature of rock bridges and to honour the oldest university in Europe (Bologna University). The Bologna Interpretation does not negate the existence of rock bridges. What rock bridges look like, how many there are, and where they are, we do not know; we can assume their existence and account for their contribution to rock mass strength using a potential analogue. Full article

In the past decade, there has been an increasing trend of digitalizing rock engineering processes. However, this process has not been accompanied by a critical analysis of the very same empirical methods that many complex numerical and digital methods are founded upon. As engineers, we are taught to use and trust numbers. Indeed, we would not be able to define the factor of the safety of a structure without numbers. However, what happens when those numbers are nothing but numerical descriptions of qualitative assessments? In this paper we present a critical review of the many attempts presented in the literature to quantify GSI (geological strength index). To the authors’ knowledge, this paper represents the first time that all the different GSI tables and quantification methods that have been proposed over the past two decades are collated and compared critically. In our critique, we argue against the paradigm whereby the quantification process adds the experience factor for inexperienced engineers. Furthermore, we discuss the limitations of the notion that GSI quantification methods could transform subjectivity into objectivity since the parameters under considerations are not quantitative measurements. Relying on empirically defined quantitative equivalences raises important questions, particularly when these quantitative equivalences are being used to define so-called accurate rock mass classification input for design purposes. Full article

This paper raises important questions about the way we approach numerical analysis in rock engineering design. The application of advanced numerical models is essential to adequately analyze and design different geotechnical aspects of pit-to-cave transitions. We present a critical review of numerical methods centered around the hypothesis that a model is not, and cannot be, a perfect imitation of reality; therefore, numerical modelling of large-scale mining projects requires the real problem to be idealized and simplified. The discussion highlights the dichotomy of continuum vs. discontinuum modelling and the important question of whether continuum models can effectively capture dynamic continuum-to-discontinuum processes typical of cave mining. The discussion is complemented by examples of hybrid continuum-discontinuum models to analyze the important problem of transitioning from surface (open pit) mining to underground mass mining (caving). The results demonstrate the hypothesis that forward modelling should be performed in the context of a risk-based approach, with numerical models becoming investigative tools to assess risk and evaluate the impact of different unknowns, thus classifying modelling outputs in terms of expected consequences. Full article

This paper presents a philosophical examination of classical rock engineering problems as the basis to move from traditional knowledge to radical (innovative) knowledge. While this paper may appear abstract to engineers and geoscientists more accustomed to case studies and practical design methods, the aim is to demonstrate how the analysis of what constitutes engineering knowledge (what rock engineers know and how they know it) should always precede the integration of new technologies into empirical disciplines such as rock engineering. We propose a new conceptual model of engineering knowledge that combines experience (practical knowledge) and a priori knowledge (knowledge that is not based on experience). Our arguments are not a critique of actual engineering systems, but rather a critique of the (subjective) reasons that are invoked when using those systems, or to defend conclusions achieved using those systems. Our analysis identifies that rock engineering knowledge is shaped by cognitive biases, which over the years have created a sort of dogmatic barrier to innovation. It therefore becomes vital to initiate a discussion on the subject of engineering knowledge that can explain the challenges we face in rock engineering design at a time when digitalisation includes the introduction of machine algorithms that are supposed to learn from conditions of limited information. Full article

The stability of high rock slopes is largely controlled by the location and orientation of geological features, such as faults, folds, joints, and bedding planes, which can induce structurally controlled slope instability. Under certain conditions, slope kinematics may vary with time, as propagation of existing fractures due to brittle failure may allow development of fully persistent release surfaces. In this paper, the progressive accumulation of brittle damage that occurred prior to and during the 2014 San Leo landslide (northern Italy) is investigated using a synthetic rock mass (SRM) approach. Mapping of brittle fractures, rock bridge failures, and major structures is undertaken using terrestrial laser scanning, photogrammetry, and high-resolution photography. Numerical analyses are conducted to investigate the role of intact rock fracturing on the evolution of kinematic freedom using the two-dimensional Finite-discrete element method (FDEM) code Elfen, and the three-dimensional lattice-spring scheme code Slope Model. Numerical analyses show that the gradual erosion of clay-rich material below the base of the plateau drives the brittle propagation of fractures within the rock mass, until a fully persistent, subvertical rupture surface form, causing toppling of fault-bounded rock columns. This study clearly highlights the potential role of intact rock fracturing on the slope kinematics, and the interaction between intact rock strength, structural geology, and slope morphology. Full article