Design charts for reliability assessment of rock bedding slopes stability against bi-planar sliding: SRLEM and BPNN approaches

Chen, Longlong; Zhang, Wengang; Gao, Xuecheng; Wang, Lin; Li, Zheng; Böhlke, Thomas; Perego, U.

doi:10.1080/17499518.2020.1815215

Cited by 24 publications

(8 citation statements)

References 33 publications

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“…Since the values of the parameters considered in the experiment are relatively limited (for example, only −8, −6, and −4 °C are considered for the temperature), conventional fitting methods are associated with great uncertainty in the fitting. On the other hand, it is noted that the Back Propagation (BP) neural network method has been successfully applied in many similar studies in the literature (Tang et al, 2012;Chen et al, 2020;Xing et al, 2021, to name a few). It is indicated that the BP neural network is particularly suitable for solving problems with complex internal mechanisms, and is associated with a great nonlinear mapping capability (Zhou and Kang, 2004).…”

Section: Prediction Of Shear Strength For the Ice-roofing Materials I...mentioning

confidence: 99%

Experimental Study on Shear Strengths of Ice-Roof Interface Aiming the Study of Roof Snow Sliding

Cao

Zhang

et al. 2022

Front. Earth Sci.

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Snow sliding of roofs is a threat to the structural safety of roof structures by causing a surcharge of local snow load on the lower roof and may pose a serious threat to pedestrians and vehicles on the ground. Therefore, it is of great significance to study the mechanism of snow sliding on roofs. Under the action of ambient temperature, solar radiation, internal heating of the building, etc., the bottom layer of roof snow is often presented in the form of porous ice after a complicated phase transition of the snow particles. Under this circumstance, the interface between roof snow and the roof can be seen as the contact between porous ice and the roofing material. In order to investigate the initialization of roof snow sliding from the material point of view, the shear performance of the interface between different roofing materials and ice with different porosities was carried out in this study. The effects of different ambient temperatures (−4, −6, and −8°C), different normal stress (0, 50, and 100 kPa), and different ice porosities (represented by the contact area ratio of 1.0, 0.8, and 0.6) on this shear performance were analyzed by direct shear tests. The test results showed that the shear failure between the ice and the roofing material was brittle, and the interface shear strength was affected by the ambient temperature, normal stress, contact area ratio, as well as the material type: the strength increases with the decrease of ambient temperature, the increase of normal stress, the increase of contact area ratio, and the increase of surface roughness of the material. Based on the test results, a quantitative relationship between the interface shear strength and the related factors was given in the article, which provides a useful reference for future experimental or simulation studies on roof snow sliding.

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Section: Prediction Of Shear Strength For the Ice-roofing Materials I...mentioning

confidence: 99%

Experimental Study on Shear Strengths of Ice-Roof Interface Aiming the Study of Roof Snow Sliding

Cao

Zhang

et al. 2022

Front. Earth Sci.

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“…In certain situations, this effect may cause new failures to arise or reactivate previously existing ones (Dijkstra and Dixon 2010, Peethambaran et al 2022, Wong et al 2022. The design and construction of optimal slopes entail not only stability and safety concerns but also a wide range of economic issues (Chen et al 2022 that need to be assessed using a variety of criteria, such as the Factor of Safety (FoS), Reliability Index (β), and Sensitivity Analysis. A variety of methodologies, including the application of empirical equations, have been suggested by academics for assessing slope failures (Bieniawski 1989, Taheri andTani 2010).…”

Section: Introductionmentioning

confidence: 99%

“…A variety of methodologies, including the application of empirical equations, have been suggested by academics for assessing slope failures (Bieniawski 1989, Taheri andTani 2010). Some researchers investigated slope stability using artificial intelligence techniques (Bui et al 2020, Chen et al 2022, Taheri and Tani 2010. For this reason, careful slope construction and in-depth study are essential.…”

Section: Introductionmentioning

confidence: 99%

Static and seismic stability investigation of a bamboo grid reinforced slope with different stepped reinforcing arrangements

Samal,

Sahoo,

Badavath

2024

Eng. Res. Express

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Bamboo is primarily used as a soil reinforcement material to build roadways and stabilize slopes and river banks. The current study aims to examine how bamboo grid reinforcing slopes behave in the form of various stepped reinforcing arrangements. The static and seismic characteristics of a slope reinforced with a bamboo grid were examined through 3D numerical analyses employing the finite element program MIDAS GTS NX (340) 2023 v1.1. In this study, the Strength Reduction Method (SRM), a crucial technique in finite element analysis, is utilized to compute the factor of safety and displacement in static stability analysis. Seismic stability analysis is conducted using non-linear time history analysis, examining the changes in critical slope parameters in response to seismic excitation.Three distinct arrangements of bamboo grids were employed to enhance slope stability. Static stability analysis, considering the factor of safety and deformation, was conducted for various bamboo grid-reinforced slope arrangements. Additionally, a comprehensive seismic analysis was performed specifically for the type 2 bamboo grid reinforcement arrangement, using the seismic record from the 1971 San Fernando Down earthquake. The safety factor increases by 52.10%, 68.67%, and 62.65% for type 1, 2, and 3 arrangements, respectively, compared to the unreinforced slope. Consequently, the type 2 bamboo grid-reinforced slope arrangement exhibits superior stability in terms of the safety factor. The lateral displacement of the type 2 arrangement is minimal compared to other types. Furthermore, a bamboo grid-reinforced slope with a vertical spacing of 1.5 m demonstrates less deformation than slopes with different vertical spacings. The seismic stability of the slope body surpasses other sections of the bamboo grid-reinforced slope.

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“…in the early 1990s, Artificial neural networks (Anns) were introduced as a multi-criteria assessment tool [22]. Since then, Anns and other machine learning algorithms have been applied to study the stability of pit slopes and dump slopes in opencast mines [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. While these studies have contributed to our understanding of dump slope stability, they often focus on a limited set of parameters, neglecting crucial variables like weather conditions, blasting vibrations, and others [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Efficient and Reliable Prediction of Dump Slope Stability in Mines using Machine Learning: An in-depth Feature Importance Analysis

2023

Archives of Mining Sciences

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EfficiEnt and REliablE PREdiction of dumP SloPE Stability in minES uSing machinE lEaRning: an in-dEPth fEatuRE imPoRtancE analySiSthis study rigorously examines the pressing issue of dump slope stability in indian opencast coal mines, a problem that has led to significant safety incidents and operational hindrances. Employing machine learning algorithms such as random Forest (rF), k-nearest neighbors (Knn), Support vector Machine (SvM), Logistic regression (Lr), decision tree (dt), and gaussian naive bayes (gnb), the study aims to achieve a scientific goal of predictive accuracy for slope stability under various environmental and operational conditions. Promising accuracies were attained, notably with rF (0.98), SvM (0.98), and dt (0.97). to address the class imbalance issue, the Synthetic Minority Oversampling technique (SMOtE) was implemented, resulting in improved model performance. Furthermore, this study introduced a novel feature importance technique to identify critical factors affecting dump slope stability, offering new insights into the mechanisms leading to slope failures. these findings have significant implications for enhancing safety measures and operational efficiency in opencast mines, not only in india but potentially globally.

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Design charts for reliability assessment of rock bedding slopes stability against bi-planar sliding: SRLEM and BPNN approaches

Cited by 24 publications

References 33 publications

Experimental Study on Shear Strengths of Ice-Roof Interface Aiming the Study of Roof Snow Sliding

Experimental Study on Shear Strengths of Ice-Roof Interface Aiming the Study of Roof Snow Sliding

Static and seismic stability investigation of a bamboo grid reinforced slope with different stepped reinforcing arrangements

Efficient and Reliable Prediction of Dump Slope Stability in Mines using Machine Learning: An in-depth Feature Importance Analysis

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