Stability analysis of rib pillars in highwall mining under dynamic and static loads in open-pit coal mine

Wu, Haoshuai; Chen, Yanlong; Lv, Haoyan; Xie, Qihang; Chen, Yuanguang; Gu, Jun

doi:10.1007/s40789-022-00504-1

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…Some mines can reach 80 m (Figure 2a). Although the region's favorable geological characteristics have brought substantial economic benefits [3], the coal resources below the end-slope cannot be excavated owing to the peculiarities of surface mining [4][5][6]. This phenomenon is more prominent in Xinjiang, where thick-seam open-pit mines dominate.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Multi-Layer Highwall Mining: A Sustainable Approach for Thick-Seam Open-Pit Mines

Tian

Lü

et al. 2023

Sustainability

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Open-pit mining is a common method for extracting coal, but considerable resources are often left unrecovered at the bottom of end-slopes, leading to a permanent waste of resources. This research presents a sustainable approach of multi-layer highwall mining at different levels to address the issue of abundant resources left unrecovered at the bottom of the end-slope in thick-seam open-pit mines. The interlayer between the upper and lower entries is simplified into a beam structure model, the bending moment distribution characteristics of the beam under a load of highwall miner are analyzed, and a method for calculating the thickness range of the interlayer is proposed. The web pillar width and interlayer thickness, obtained theoretically, are verified through a numerical simulation, and the results of mining a single layer are compared to those of mining multiple layers. The results show that the web pillar width and interlayer thickness derived from the numerical simulation are basically the same as those of the theoretical analysis. Compared with single layer mining, the vertical stress on the web pillar in the lowest panel is reduced by 14.83~18.25%, and the safety factor of the web pillar is increased to 0.27. The web pillars and interlayers at different elevations are stable during multi-layer highwall mining. These findings support the feasibility of multi-layer highwall mining for resource recovery, which is conducive to sustainable mining.

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Section: Introductionmentioning

confidence: 99%

Stability Analysis of Multi-Layer Highwall Mining: A Sustainable Approach for Thick-Seam Open-Pit Mines

Tian

Lü

et al. 2023

Sustainability

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“…Therefore, the water content significantly influences the mechanical behavior of rock surrounding tunnels or caves. Numerous experimental studies conducted by academics worldwide have focused on investigating spalling and rock bursts in hard-rock tunnels and caverns [33,[39][40][41][42][43][44][45][46][47][48]. Rock burst is directly linked with the brittleness of the rock: the higher the brittleness, the higher the rock burst, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

“…The properties of IR undergo notable changes during rock deformation, sliding friction, fracture development, and dilatation under various loads, as demonstrated by a series of studies [53][54][55][56][57][58]. IR indices, such as infrared radiation variance (IRV) and average infrared radiation temperature (AIRT), may be used to indicate these variations in IR properties throughout the induced fracturing process [40,[42][43][44]59,60]. Compared to IRV [61], the AIRT is less able to quantitatively describe and analyze the features of IR during rock fracture.…”

Section: Introductionmentioning

confidence: 99%

Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational Techniques

Khan,

Ma,

Emad

et al. 2023

Water

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The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock bursts, which can cause damage to miners and lead to the catastrophic failure of engineering structures. Therefore, developing a new brittleness index capable of effectively predicting rock bursts is essential for the safe and efficient execution of engineering projects. In this research study, a novel mathematical rock brittleness index is developed, utilizing factors such as crack initiation, crack damage, and peak stress for sandstones with varying water contents. Additionally, the brittleness index is compared with previous important brittleness indices (e.g., B1, B2, B3, and B4) predicted using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), along with various artificial intelligent (AI) techniques such as k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF), providing comprehensive insights for predicting rock bursts. The experimental and AI results revealed that: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease with an increase in water content; (2) the brittleness indices such as B1, B3, and B4 show a positive linear exponential correlation, having a coefficient of determination of R2 = 0.88, while B2 shows a negative linear exponential correlation (R2 = 0.82) with water content. Furthermore, the proposed brittleness index shows a good linear correlation with B1, B3, and B4, with an R2 > 0.85, while it shows a poor negative linear correlation with B2, with an R2 = 0.61; (3) the RF model, developed for predicting the brittleness index, demonstrates superior performance when compared to other models, as indicated by the following performance parameters: R2 = 0.999, root mean square error (RMSE) = 0.383, mean square error (MSE) = 0.007, and mean absolute error (MAE) = 0.002. Consequently, RF stands as being recommended for accurate rock brittleness prediction. These research findings offer valuable insights and guidelines for effectively developing a brittleness index to assess the rock burst risks associated with rock engineering projects under water conditions.

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“…In addition, the field determination technique for rock mass deformation may include in situ uncertainty and rock surface damage resulting from test blasts [9,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Hence, based on the complexity of in situ testing methods, researchers now prefer to use alternate methods in predicting or estimating rock mass deformation compared to in situ methods [35][36][37][38][39][40][41][42][43][44][45][46]. Therefore, it is essential to use less time-consuming, efficient, and reliable alternate methods in the prediction of rock mass deformation modulus.…”

Section: Introductionmentioning

confidence: 99%

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

et al. 2022

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The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field.

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Stability analysis of rib pillars in highwall mining under dynamic and static loads in open-pit coal mine

Cited by 22 publications

References 36 publications

Stability Analysis of Multi-Layer Highwall Mining: A Sustainable Approach for Thick-Seam Open-Pit Mines

Stability Analysis of Multi-Layer Highwall Mining: A Sustainable Approach for Thick-Seam Open-Pit Mines

Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational Techniques

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

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