Gray algebraic curve model-based roof separation prediction method for the warning of roof fall accidents

Xie, Jianlin; Xu, Jialin; Zhu, Weibing

doi:10.1007/s12517-016-2541-4

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Based on the theory of "up three zones" and the theory of mine pressure, the deformation characteristics of overburden strata in thick and loose thick coal seams are analyzed (He et al 2016;Jiang et al 2016;Zhu et al 2016). Compared with the thin coal seams, as the working face advances, the thick loose seam and thick coal seam roof directly collapses before the working face reaches the falling step (Xie et al 2016;Zhang et al 2016). When the ceiling area continuously expands, the direct roof collapse area continuously expands.…”

Section: Mining Damage Analysismentioning

confidence: 99%

Study on overburden failure characteristics in deep thick loose seam and thick coal seam mining

Liu

Pang

et al. 2020

Geomatics, Natural Hazards and Risk

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In order to solve the related problems of roof collapse in deep thick loose seam, taking the actual geological conditions of 1302 working face of Zhaolou Coal Mine as the geological background. A mechanical calculation and analysis model for a deep thick loose seam is established. A mining dynamic simulation was carried out using the numerical simulation software FLAC3D. Fracture development height was observed using a multi-stage plugging and leak detection system. Sublevel solid compaction filling technology was used to fill the goaf. Research shows that the caving zone, fissure development zone, and bending subsidence zone are formed during thick loose seam mining. The caving zone area is larger when the ground stress on the roof is larger, and fracture zones are better developed because the density of the loose seam area is smaller. Numerical simulation results are slightly larger than the measured results, so the roof overburden activity can be effectively predicted using the numerical simulation. The problem of serious roof collapse and surface subsidence in goafs can be effectively controlled using sectional compact filling technology.

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Section: Mining Damage Analysismentioning

confidence: 99%

Study on overburden failure characteristics in deep thick loose seam and thick coal seam mining

Liu

Pang

et al. 2020

Geomatics, Natural Hazards and Risk

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“…Therefore, the gray theory analysis method was used to predict the roof separation distance. Combined with the gray algebraic curve model, the early warning system could be established to evaluate the occurrence of roof accidents [25]. The classification of roof grade was closely related to the geological conditions (e.g., faults) and roof strength (e.g., roof caving), which further affected the roof risk assessment [26].…”

Section: Introductionmentioning

confidence: 99%

The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

et al. 2021

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Roof accidents seriously affect the safe and efficient mining of the working faces. Therefore, it is necessary to assess and identify the possible and influencing factors on the occurrence of roof risk in a fully mechanized mining workface. In this study, based on the analytic hierarchy process and fuzzy comprehensive evaluation, a comprehensive standard cloud model was established through constructing a quantitative grade interval and calculating the weight of each index to achieve the aim of a roof risk assessment and identification. The accuracy of risk assessment was ensured by using the comprehensive analyses of various aspects, such as cloud digital features, risk assessment cloud image and standard cloud image. This showed that the main influencing factors on the occurrence of roof accidents were roof separation distance, weighting intensity and rib spalling followed by the coal body stress concentration, initial support force and geological conditions. Taking 42,115 fully mechanized working faces in the Yushen coal mining area as an engineering background, this model was adopted to assess and identify the risk of roof accidents through generating comprehensive assessment cloud images and introducing the Dice coefficient to calculate the similarity degree. The results showed that the overall risk of roof accidents in 42,115 working faces was regarded as grade II (general risk) through the overall index of comprehensive risk evaluation and a similarity degree of 0.8606. The impact of roof condition was mainly influenced by the risk of roof accidents, while the support status, personal working status and coal body condition had a limited effect on the risk of roof accidents. The comprehensive standard cloud model proposed in this study had strong visibility and discovered the key parts of risk indexes easily to solve the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods. Therefore, this model was worth promoting, because it laid the foundation for the intelligent identification and early warning system of roof accident risk in a fully mechanized mining workface.

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“…However, there are also two outstanding problems in the bolt support of the production mines in the mining area. On the one hand, the bolt support design is too conservative; on the other hand, the roof accidents of the bolt support coal roadway also occur frequently [4][5][6], which becomes the restrictive factor for the widespread application of the bolt support technology in the coal roadway. According to the statistics of coal mine accidents in the country from 2007 to 2016, the number of roof accidents accounts for about 50% of the total coal mine accidents, and the death toll of roof accidents accounts for 35.5% [7].…”

Section: Introductionmentioning

confidence: 99%

Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

Ren

Zhu

Wang

et al. 2020

Advances in Civil Engineering

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In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

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Gray algebraic curve model-based roof separation prediction method for the warning of roof fall accidents

Cited by 12 publications

References 9 publications

Study on overburden failure characteristics in deep thick loose seam and thick coal seam mining

Study on overburden failure characteristics in deep thick loose seam and thick coal seam mining

The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

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