Subsidence over room and pillar retreat mining in a low coal seam

Zingano, André Cezar; Weiss, Anderson Luis

doi:10.1016/j.ijmst.2018.11.022

Cited by 28 publications

(17 citation statements)

References 2 publications

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“…In the northern Shaanxi Province Yushenfu mining area of China, the old room-pillar mining and the "narrow strip" coal mining methods have been used for a long time [1]. Although the purpose of controlling surface subsidence and water retention has been achieved within a period of time, the recovery rate can only reach about 50%, which has caused serious waste of resources, and roof disasters have occurred frequently in recent years [2]. Strip mining areas also have hidden dangers of overburden disasters due to long-term creep of coal pillars [3].…”

Section: Introductionmentioning

confidence: 99%

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Study on Rheological and Mechanical Properties of Aeolian Sand-Fly Ash-Based Filling Slurry

Shao

Wang

et al. 2020

Energies

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Backfill mining is the most environmentally friendly mining method at present, which can effectively control the surface subsidence, improve the recovery rate, and has good social and economic benefits. The purpose of this study is to solve the environmental problems caused by solid waste, combined with the rich geographical advantages of aeolian sand in the Yushenfu mining area of China. The rheological properties of the aeolian sand-fly ash-based filling slurry with different fly ash content are studied by experiments, and the strength development law of the filling body under different age and fly ash content are studied from the macroscopic and microscopic points of view. The rheological experiments showed that the increase of the amount of fly ash has a significant effect on the thixotropy, plastic viscosity, and yield stress of the filling slurry. Additionally, rheological properties of aeolian sand-fly ash-based filling slurry conform to the Bingham model. With the increase of the amount of fly ash, the performance of the filling slurry has been significantly improved. Uniaxial test and scanning electron microscope observation showed that the influence of fly ash on the strength of the filling body was mainly reflected in the late stage of maintenance, but was not obvious in the middle stage. Fly ash particles mainly bear the role of “water reduction” and a physical filling effect, which makes the filling slurry thicker and the internal structure more closely spaced. The volcanic ash reaction of fly ash is lagging behind the hydration reaction of cement; the secondary product of the delayed reaction is filled in the pores of cement hydrates, which can greatly reduce the porosity of the backfill body and increase the later strength of the backfill body. It provides a guarantee for the safe replacement of coal pillars in the working face.

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Section: Introductionmentioning

confidence: 99%

“…Energies 2020, 13, 1266 11 of 13 (3) The fly ash content and the shear yield force meet a quadratic function relationship of y = 17.14 − 0.8x + 0.11x 2 . As the fly ash content increases, the backfill body's resistance to deformation increases and its plasticity increases.…”

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confidence: 99%

Study on Rheological and Mechanical Properties of Aeolian Sand-Fly Ash-Based Filling Slurry

Shao

Wang

et al. 2020

Energies

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“…Although this model performed reasonably well in the assessment of potential subsidence at various locations, the sample size was relatively small; thus, additional analyses should be performed at other mine locations and in a larger variety of ground conditions to more clearly verify the model. (5) It is thought that more reliable results can be obtained when various types of analyses by numerical, statistical, and theoretical approaches are combined. This study can be used as an initial method for screening the specific locations that are at high risk of subsidence and thus require immediate additional investigation, from an extensive mine area for subsequent detailed analyses by the other approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, many attempts have been made to more precisely predict subsidence and validate the stability of mining and land areas by numerical analyses [1][2][3][4][5], theoretical and experimental solutions [6][7][8][9][10], and/or statistical approaches [11,12], but the results have been limited because abandoned mines are generally too large and the goafs are too complex for effective analysis using the aforementioned approaches.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

Kim

Lee

2020

Applied Sciences

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Subsidence at abandoned mines sometimes causes destruction of local areas and casualties. This paper proposes a mine subsidence risk index and establishes a subsidence risk grade based on two separate analyses of A and B to predict the occurrence of subsidence at an abandoned mine. For the analyses, 227 locations were ultimately selected at 15 abandoned coal mines and 22 abandoned mines of other types (i.e., gold, silver, and metal mines). Analysis A predicts whether subsidence is likely using an artificial neural network. Analysis B assesses a mine subsidence risk index that indicates the extent of risk of subsidence. Results of both analyses are utilized to assign a subsidence risk grade to each ground location investigated. To check the model's reliability, a new dataset of 22 locations was selected from five other abandoned mines; the subsidence risk grade results were compared with those of the actual ground conditions. The resulting correct prediction percentage for 13 subsidence locations of the abandoned mines was 83-86%. To improve reliability of the subsidence risk, much more subsidence data with greater variations in ground conditions is required, and various types of analyses by numerical and empirical approaches, etc. need to be combined. ANN provides tools for optimising operations, equipment selection, and problems involving large amounts of information that humans cannot easily assimilate in the process of decision-making. Hence, the neural network can serve as a tool for determining the relative importance of the factors influencing the stability of underground objects [18].Since the 1990s, when a number of ANN-based systems were introduced in the mining industry [19], ANN has significantly improved the current approach towards predicting abandoned mine subsidence risk [20][21][22][23]. In particular, Oh et al. [24] evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility maps. Kim et al. [25] also constructed a hazard map for possible ground subsidence around abandoned underground coal mines in Korea using an ANN together with a geographic information system (GIS). Zhao and Chen [26] used an ANN to predict ground subsidence at a metal mine because ground subsidence is influenced by many factors which are fuzzy and nonlinear, and the ANN has a great function to be able to handle such nonlinear problems, without knowing specific mine conditions. Meanwhile, Mine Reclamation Corporation (MIRECO) who is responsible for the safety and management of abandoned mines and implements mine reclamation projects in Korea has made several attempts to reliably predict subsidence by numerical, statistical, probabilistic, and/or model approaches [27-31] prior to this study. However, the prediction results were not up to par with MIRECO's standards because they were not appropriate for the assessment of many as well as extensive abandoned mines.Finally, MIRECO decided to adopt ANN as well to predict subsidence consideri...

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