The role of discontinuities in rock slope stability: Insights from a combined finite-discrete element simulation

Sun, Lei; Grasselli, Giovanni; Liu, Quansheng; Tang, Xuhai; Abdelaziz, Aly

doi:10.1016/j.compgeo.2022.104788

Cited by 59 publications

(8 citation statements)

References 50 publications

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“…Several researchers highlighted the significance of comprehending the instability mechanisms impacting various rock formations [3][4][5][6]. They emphasize the necessity of gathering accurate geological, geomorphological, geophysical, and geomechanical data to ensure a robust model of the instability phenomena, e.g., [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

Cirillo,

Zappa,

Tangari

et al. 2024

Drones

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The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the geomechanical properties and rockfall potential of several rock scarps within a wide area of 50 ha. Traditional methods for evaluating geomechanical parameters on rock scarps involve time-consuming field surveys and measurements, which can be hazardous in steep and rugged environments. By contrast, drone photogrammetry offers a safer and more efficient approach, allowing for the creation of detailed 3D models of a cliff area. These models provide valuable insights into the topography, geological structures, and potential failure mechanisms. This research processed the acquired drone imagery using advanced geospatial software to generate accurate orthophotos and digital elevation models. These outputs analysed the key factors contributing to rockfall triggering, including identifying discontinuities, joint orientations, kinematic analysis of failures, and fracturing frequency. More than 8.9 × 107 facets, representing discontinuity planes, were recognised and analysed for the kinematic failure modes, showing that direct toppling is the most abundant rockfall type, followed by planar sliding and flexural toppling. Three different fracturation grades were also identified based on the number of planar facets recognised on rock surfaces. The approach used in this research contributes to the ongoing development of fast, practical, low-cost, and non-invasive techniques for geomechanical assessment on vertical rock scarps. In particular, the results show the effectiveness of drone-based photogrammetry for rapidly collecting comprehensive geomechanical data valid to recognise the prone areas to rockfalls in vast regions.

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Section: Introductionmentioning

confidence: 99%

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

Cirillo,

Zappa,

Tangari

et al. 2024

Drones

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show abstract

“…The choice of slope stability analysis methods depends on several factors such as the level of detail required, data availability, and available resources. In general, a combination of quantitative and qualitative factors is employed for the slope stability analysis, providing a more comprehensive assessment [9,10].…”

Section: Introductionmentioning

confidence: 99%

Slope Deformation Mechanisms and Stability Assessment under Varied Conditions in an Iron Mine Waste Dump

Yang,

Ding,

Liu

et al. 2024

Water

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The instability of geological slopes in mining environments poses a significant challenge to the safety and efficiency of operations. Waste Dump#2 at the Ziluoyi Iron Mine in China is a notable case study that highlights the challenges associated with sizable base slopes and large step heights. To address hidden hazards in the mine and the above issues, an inclusive investigation is carried out to examine the physical and mechanical properties of the soil–rock slope through indoor testing and analyze the deformation mechanisms of the slope using numerical simulations, taking various factors into account. The study reveals that the stability of Waste Dump#2 is deeply affected by weight, groundwater conditions, earthquake loading, and rainfall. To this end, the cohesion and internal friction parameters of the soil–rock slope are first determined through direct shear tests, which show a cohesion of 6.215 kPa at the top of the slope and an internal friction angle of 34.12°. By adopting GEO-SLOPE, 3D Mine, and AutoCAD software, stability calculations of the slope are performed, which give stability coefficients of 1.547 under normal conditions, 1.276 in rainfall, and 1.352 in seismic conditions. These results meet safety standards and ensure the safe and efficient operation of the mine.

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“…With the rapid development of numerical simulation methods, finite element method (FEM) [19], boundary element method [20], discrete element method [21], numerical manifold method [22], and other methods have been widely used in slope stability analysis. Sun et al [23] simulate the progressive failure process of jointed rock slopes based on the combined finite-discrete element method. Ma et al [24] analyzed the slope stability under a complex stress state with saturated and unsaturated seepage using the fast Lagrangian analysis of continua.…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

2023

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Slope instability can lead to catastrophic consequences. However, predicting slope stability effectively is still challenging because of the complex mechanisms and multiple influencing factors. In recent years, machine learning (ML) has received great attention in slope stability prediction due to its strong nonlinear prediction ability. In this study, an optimum-path forest algorithm based on k-nearest neighbor (OPFk-NN) was used to predict the stability of slopes. First, 404 historical slopes with failure risk were collected. Subsequently, the dataset was used to train and test the algorithm based on randomly divided training and test sets, respectively. The hyperparameter values were tuned by combining ten-fold cross-validation and grid search methods. Finally, the performance of the proposed approach was evaluated based on accuracy, F1-score, area under the curve (AUC), and computational burden. In addition, the prediction results were compared with the other six ML algorithms. The results showed that the OPFk-NN algorithm had a better performance, and the values of accuracy, F1-score, AUC, and computational burden were 0.901, 0.902, 0.901, and 0.957 s, respectively. Moreover, the failed slope cases can be accurately identified, which is highly critical in slope stability prediction. The slope angle had the most important influence on prediction results. Furthermore, the engineering application results showed that the overall predictive performance of the OPFk-NN model was consistent with the factor of safety value of engineering slopes. This study can provide valuable guidance for slope stability analysis and risk management.

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The role of discontinuities in rock slope stability: Insights from a combined finite-discrete element simulation

Cited by 59 publications

References 50 publications

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

Slope Deformation Mechanisms and Stability Assessment under Varied Conditions in an Iron Mine Waste Dump

Slope Stability Prediction Using k-NN-Based Optimum-Path Forest Approach

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