Research on Catastrophic Pillar Instability in Room and Pillar Gypsum Mining

Zhou, Yuejin; Li, Meng; Xu, Xiaoding; Li, Xiaotong; Ma, Yongdong; Ma, Zhuang

doi:10.3390/su10103773

Cited by 12 publications

(11 citation statements)

References 17 publications

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“…Room and pillar mining methods are used in ore mines and also in coal and chemical resources mines [18,19]. Pillars of various shapes and cross-sections (continuous, square, round, rectangular) are placed between rooms.…”

Section: Room and Pillar Methods In Hard Rock Miningmentioning

confidence: 99%

Flexibility and Load-Bearing Capacity of Roof Bolting as Functions of Mounting Depth and Hole Diameter

et al. 2019

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This paper presents the results of laboratory tensile testing of segmentally-installed glue-in roof bolting. We studied roof bolting of the type Olkusz-16A (Boltech Sp. z o.o., ZGH Bolesław S.A., Bukowno, Poland), additionally equipped with a steel rod coil, which was mounted in steel cylinders filled with a concrete mixture using multi-part resin cartridges with a diameter of 0.024 m and length of 0.045 m. The mounting depths were 0.1 m and 0.2 m, respectively. Our main purpose was to determine the effect of the bolt hole diameter, which assumed the values 0.028 m, 0.032 m, 0.035 m, and 0.037 m, respectively, on the load-bearing capacity of the roof bolting in relation to the mounting depth. We found that the mounting depth of 0.2 m was sufficient for the roof bolting to exhibit its full load and displacement properties for all four diameters of the bolt hole. To determine whether the roof bolting was capable of transferring the load in situ, we presented the results of the predicted load on the roof bolting applied in a room and pillar mining method in an underground mine of zinc and lead ore deposits. Our objective was to determine the influence of the room and pillar mining method geometry on the range of the fault zone of rocks around pits. We designed the deposit excavation model using the Examine3D numerical modeling software, which is based on the boundary element method. We created three-dimensional models for three variants of working space opening widths: featuring two, three, and four rows of rooms. The geometry of rooms and pillars corresponded to the mine conditions; the width, height, and length parameters were all 5 m. We determined the strength, strain, and structural parameters of the rock mass on the basis of laboratory studies of the drill core and rock forms collected from the room longwall. We used the strength factor to specify the maximum range of the fault zone of rocks around pits. In the last stage of research, we compared the load value obtained based on numerical testing with the maximum load obtained in the tensile strength tests of the roof bolting and determined the safety factor of the segmentally-installed roof bolting.

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Section: Room and Pillar Methods In Hard Rock Miningmentioning

confidence: 99%

Flexibility and Load-Bearing Capacity of Roof Bolting as Functions of Mounting Depth and Hole Diameter

et al. 2019

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“…The increased load may exceed the strength of adjacent pillars, and lead to their failure. Like the domino effect, rapid and large-scale collapse can be induced [4]. In addition, with the increase of mining depth, the ground stress is larger and pillar instability accidents become more frequent [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

et al. 2020

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Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

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“…Cording observed the pillars of shale reservoirs and concluded that the instability of pillars is mainly caused by the combination of stress slabbing and slaking on the surface of pillars [8]. Based on the energy catastrophe theory, Zhou et al studied the influence of the constitutive equation and shape of the pillar in the goaf of gypsum mine on its stability and put forward the necessary and sufficient conditions for the instability of the pillar [9]. Zhou et al analyzed the influence of geometric parameters in the roof-pillar support system and obtained the classification standard of goaf stability [10,11].…”

Section: Introductionmentioning

confidence: 99%

Application and Effect of Loading Rates on Coal Sample Failure

Liu

et al. 2021

Shock and Vibration

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In order to evaluate the coal pillar stability in recovery of residual room pillars under different mining rates, this paper studies the influence of loading rate on the mechanical properties of the coal body. The uniaxial compression tests of coal samples in Yangcheng area at different loading rates were carried out with the MTS815 electrohydraulic servo rock mechanics test system. The stress-strain curves and the evolution characteristics of AE signals were analyzed. At same time, the mechanism of damage and failure of specimens are also discussed. The results show the following. (1) With the increase in loading rate, the ultimate stress and ultimate strain of specimens decrease first and then increase. (2) Loading rate has a significant effect on the stability adjustment of specimens. With the decrease in loading rate, the earlier the stress adjustment is, the larger the adjustment range is, and the failure mode changes from shear failure to tensile failure. (3) In addition, when the loading rate increases, the AE evolves from continuous dense to discrete catastrophe, which indicates that the failure of the sample at a larger loading rate is sudden, which is not conducive to the maintenance of the stability of the coal pillar. (4) Finally, the failure mechanism of the specimen structure under different loading rates is obtained, and the improvement measures for the effect of mining velocity of working face on the stability of coal pillar are put forward. The results reveal the loading rate effect of mechanical properties of coal and provide a reference for controlling the stability of the residual coal pillar.

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Research on Catastrophic Pillar Instability in Room and Pillar Gypsum Mining

Cited by 12 publications

References 17 publications

Flexibility and Load-Bearing Capacity of Roof Bolting as Functions of Mounting Depth and Hole Diameter

Flexibility and Load-Bearing Capacity of Roof Bolting as Functions of Mounting Depth and Hole Diameter

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Application and Effect of Loading Rates on Coal Sample Failure

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