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Abstract Background Rock slope instability is a complex geotechnical issue that is affected by site-specific rock properties, geological structures, groundwater, and earthquake load conditions. Numerous studies acknowledge these aleatory uncertainties in slope stability assessment; however, understanding the rock behaviour could still be improved. Therefore, this paper aims to summarise the probability methods applied in rock slope stability analysis in mining and civil engineering and develop new probabilistic design and assessment methodologies for four methods, namely empirical/rock mass classifications techniques, kinematic analysis, limit equilibrium (LE), and numerical methods and introduces how to integrate all methods to determine the total probability of failure. The case studies have been conducted based on slopes from Indonesia, a seismically active country, utilising the proposed design methods. Results Regarding the probabilistic empirical/rock mass classification (RMC) technique, this study has identified that seven of the ten most involved input parameters in RMC naturally exhibit aleatory uncertainty. Thus, the optimal way to present the output probability of RMC is as a confidence interval (CI) or total and conditional probability associated with each rock mass class. In probabilistic kinematic analysis, this study presents a systematic method to compute the probabilities of different types of failure alongside the total probability of occurrence (P tK ). The probability of failure (PoF) for jointed generalized Hoek-Brown (GHB) numerical modelling was lower than that obtained through the probabilistic LE approach for a similar slope. However, the PoF of jointed GHB is higher than the LE approach when loaded with 0.1 and 0.15 earthquake coefficients. Conclusions The variation of PoF across different failure criteria determines how epistemic uncertainty is apparent in the modelling process, while the aleatory uncertainty arises from input parameters. Furthermore, this study introduces the total probability of failure equation as a combination of kinematic and kinetic probabilities (limit equilibrium and numerical modelling).

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.

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.

Slope failures are an inevitable aspect of economic pit slope designs in the mining industry. Large open pit guidelines and industry standards accept up to 30% of benches in open pits to collapse provided that they are controlled and that no personnel are at risk. Rigorous ground control measures including real time monitoring systems at TARP (trigger-action-response-plan) protocols are widely utilized to prevent personnel from being exposed to slope failure risks. Technology and computing capability are rapidly evolving. Aerial photogrammetry techniques using UAV (unmanned aerial vehicle) enable geotechnical engineers and engineering geologists to work faster and more safely by removing themselves from potential line-of-fire near unstable slopes. Slope stability modelling software using limit equilibrium (LE) and finite element (FE) methods in three dimensions (3D) is also becoming more accessible, user-friendly and faster to operate. These key components enable geotechnical engineers to undertake site investigations, develop geotechnical models and assess slope stability faster and in more detail with less exposure to fall of ground hazards in the field. This paper describes the rapid and robust process utilized at BHP Limited for appraising a slope failure at an iron ore mine site in the Pilbara region of Western Australia using a combination of UAV photogrammetry and 3D slope stability models in less than a shift (i.e. less than 12 h).

Strata failure is a principal hazard in open cut coal mining as it has the potential to cause multiple fatalities. Prior to the excavation of any slope, a geotechnical assessment should review the likely slope performance, including the risk of slope failure. Controls to manage this risk to an acceptable level should accompany the geotechnical analysis. A survey of 43 practising geotechnical engineers indicated that kinematic and 2D limit equilibrium analyses were the methods most commonly applied to analyse excavated slope stability. While these methods are well established and widely applied in the broad rock engineering disciplines (e.g. civil, hard rock), a recent review of over 60 slope failures suggests these methods have limited suitability for modelling the complex failure mechanisms observed in excavated coal mine slopes. Kinematic techniques do not adequately capture the rock mass component of excavated slope failure and do not provide a geospatial location of potential failure, while 2D limit equilibrium techniques do not adequately capture the 3D mechanisms of excavated slope failures. Methods which do consider the 3D mechanisms of slope failure are summarized for industry consideration and application. Keywords: Slope stability, 2D, 3D, Open cut coal mine, Limit equilibrium

The Q-slope classification system is used to assess the stability of excavated rock slopes and provide an indication of long-term stable, reinforcement-free slope angles. Q-slope is based on over 500 rock slope case studies from mines, road and rail cuttings hosted in igneous, sedimentary and metamorphic rocks around the world. Q-slope can be applied for slopes ranging from less than 5 m to more than 250 m in height in both civil and mining environments.This paper describes the application of Q-slope classification system to 38 failed and intact slopes from Australian open cut coal mines. It further describes the relationship between Q-slope and Slope Stability Assessment Methodology (SSAM) ratings for stable slopes based on the available case studies.

This paper presents a new risk assessment methodology for coal mine excavated slopes. This new empirical-statistical slope stability assessment methodology (SSAM) is intended for use by geotechnical engineers at both the design review and operational stages of a mine’s life to categorise the risk of an excavated coal mine slope. A likelihood of failure is determined using a new slope stability classification system for excavated coal mine slopes developed using a database of 119 intact and failed case studies sourced from open cut coal mines in Australia. Consequence of failure is based on slope height and stand-off distance at the toe of the excavated slope. Results are presented in a new risk matrix, with slope risk being divided into low, medium and high categories. The SSAM is put forward as a new risk assessment methodology to assess the potential for, and consequence of, excavated coal mine slope failure. Unlike existing classification systems, assumptions about the likely failure mode or mechanism are not required. Instead, the SSAM applies an approach which compares the conditions present within the excavated slope face, with the known past performance of slopes with similar geotechnical and geometrical conditions, to estimate the slope’s propensity for failure. The SSAM is novel in that it considers the depositional history of strata in an excavated slope and how this sequence affects slope stability. It is further novel in that it does not require explicit measurements of intact rock, rock mass and/or defect strength to rapidly calculate a slope’s likelihood of failure and overall risk. Ratings can be determined entirely from visual observations of the excavated slope face. The new SSAM is designed to be used in conjunction with existing slope stability assessment tools. Keywords: Risk assessment, Slope stability, Open cut coal mine, Excavated slope, Likelihood of failure