Experimental Study on Failure Model of Tailing Dam Overtopping under Heavy Rainfall

Wang, Guangjin; Zhao, Bing; Rong, Lan; Liu, Dianwen; Wu, Bisheng; Li, Yaoji; Li, Quanming; Hanmin, Zhou; Liu, Mingsheng; Liu, Wenlian; Liu, Xinjie

doi:10.2113/2022/5922501

Cited by 44 publications

(17 citation statements)

References 34 publications

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“…The geographic information system and the advancement of artificial intelligence (AI) technology will promote the development of more efficient and accurate studies on the sensitivity of the landslide inventory, which makes the evaluation of contributing factor system more reasonable (Ghorbanzadeh et al, 2022b). More intelligent models are applied to landslide susceptibility, and all kinds of machine learning methods, including logistic, classification and regression tree (CART), SVM, and transfer learning, have been widely used (Huo et al, 2019;Ghorbanzadeh et al, 2022c;Shi et al, 2022;Wang et al, 2022). C5.0 decision tree, random forest, and support vector machine are used to partition landslide sensitivity and compare its performance in the coal mining area.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

et al. 2023

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The main purpose of this study is to analyze the main influencing factors of the landslide in the coal mine area and, on this basis, establish the sensitivity zoning model of the landslide. Considering the difficulty to obtain the expected results by using machine learning under the condition of lacking data, the typical landslide is used as the data basis, that is, the Fenxi coal mine and Xishan Bujiu coal mine are selected as the coal mining landslide points. Various factors, such as goaf, land subsidence, slope structure, formation lithology, and various indicators are used as input data sources, and artificial neural network (ANN) datasets are used for training to establish a pre-training model. Using the pre-training model, the mining landslide sensitivity evaluation model based on transfer learning is established. In order to demonstrate the performance of transfer learning more intuitively, the neural network is introduced to evaluate the evaluation model. The test results show that transfer learning can achieve a transfer effect higher than 0.95, and the regional distributions of highest landslide sensitivity calculated based on self-transfer learning, direct push transfer learning, and inductive transfer learning are 31.33, 35.50, and 33.75%, respectively, which further deduced that inductive transfer learning can be used for evaluating an LSP model.

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Section: Introductionmentioning

confidence: 99%

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

et al. 2023

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“…The initial volume does not always determine the debris flow volume, according to the literature (Hungr et al, 2005;Lei et al, 2022). In general, debris flows with small initial volumes can rapidly increase by several or even tens of orders of magnitude in volume through source erosion and entrainment processes (Shang et al, 2003;Wang et al, 2003;Zaginaev et al, 2019;Wang et al, 2022). Source erosion and entrainment processes significantly increase the debris flow volume and impact, which should be considered in the dynamic model (Chen et al, 2006;Wang et al, 2020;Guo et al, 2022;Wang et al, 2022;Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of debris flow-induced impacting onto rigid barrier with material source erosion-entrainment process

Fang²,

Li³

et al. 2023

Front. Earth Sci.

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The dynamics of debris flow impact considering the material source erosion-entrainment process is analyzed using a coupled SPH-DEM-FEM method. A complex coupled dynamic model of a debris flow, the erodible material source, and a rigid barrier is established in this paper. The applicability of the coupled SPH-DEM-FEM method for calculating the impact force of debris flow on the rigid barrier is verified by comparing the model with the laboratory test. The strain softening model is used to simulate the process from solid state to transition state and finally to liquid state of erodible material source. The impact force caused by debris flow considering the source erosion-entrainment process and the dynamic response of a rigid barrier is also analyzed. The results show that the volume of debris fluid, impact force, and dynamic response of a rigid barrier considering source erosion–entrainment are significantly greater than those of the original model. According to the calculation results, the existing formula for the impact force of a debris flow is then modified. The coupled numerical analysis method and the calculated results help to clarify the influence of erosion-entrainment, modify the calculation of the impact force of debris flow, and optimize the design of the rigid barrier.

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“…The study of blockiness level is of great significance to avoid geological disasters such as rockfalls and tailings pond failures (Wang et al, 2019;Wang et al, 2021;Wang et al, 2022a;Lin et al, 2022;Ma and Liu, 2022). Blockiness is defined as the percentage of the volume of isolated blocks formed by fractures to the total volume of the rock (Xia et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

The influence of normal distribution dispersion of fracture size on blockiness and REV of fractured rock masses

Xia²

2023

Front. Earth Sci.

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The fractures of different sizes in rock masses are important for describing rock fragmentation. The distribution dispersion of fracture size influences the blockiness level of the rock masses. Based on a normal statistical distribution, the volume ratio of blocks to rock (B) was obtained to describe the blockiness level. For exploring the effect of the dispersion of fracture size on blockiness level and the representative elementary volume (REV) of rock masses, the laboratory model and numerical simulation were established, and the theory of statistics and the method of analytical solution were applied. In addition, 4,525 practical rock models were established to qualitatively reproduce the behavior of B with changing domain size. The results show that by comparing the degree of convergence, the REV of a rock mass is determined by the fracture size rather than the degree of fracture dispersion. The value of B increases with the distribution dispersion of fracture size, indicating a higher blockiness level. From the experimental analysis of coin tossing, when the number of trials exceeds 69, the random results are nearly stable. In this study, 100 calculations were performed. A formula to calculate the blockiness by considering the dispersion degrees of fracture size was obtained. Moreover, a positive linear correlation between B and the coefficient of variation of fracture size was obtained. The rate of increase in B has a parabolic relationship with the ratio of fracture size to fracture spacing (L).

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Experimental Study on Failure Model of Tailing Dam Overtopping under Heavy Rainfall

Cited by 44 publications

References 34 publications

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

Sensitivity study of multi-field information maps of typical landslides in mining areas based on transfer learning

Dynamics of debris flow-induced impacting onto rigid barrier with material source erosion-entrainment process

The influence of normal distribution dispersion of fracture size on blockiness and REV of fractured rock masses

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