Floor water inrush analysis based on mechanical failure characters and microseismic monitoring

Ma, Kai; Sun, Xiaotong; Tang, Chun An; Yuan, Fu-zhen; Wang, S.J.; Chen, T.

doi:10.1016/j.tust.2020.103698

Cited by 49 publications

(21 citation statements)

References 26 publications

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“…Li and Wang [22] reproduced the formation and evolution process of water-inrush channels through numerical simulation using FLAC (3D), which provides insights into formation and evolution characteristics of water-inrush channels in coal floors under high pressure. Ma et al [23] combined theoretical analysis and numerical simulation with rock failure process analysis and microseismic monitoring to study failure characteristics and damage depth of coal floors above a confined aquifer. The method overcomes the limitations of a single method in previous research and also provides a reference for solving the water-inrush problems in roadways and selection of proper reinforcement measures.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

et al. 2022

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The research was aimed at predicting floor water-inrush risk in coal mines and forewarn of such accidents to guide safe production of coal mines in practice. To this end, a prediction method for floor water inrush combining the chaotic fruit fly optimization algorithm (CFOA) and the generalized regression neural network (GRNN) is proposed. Floor water inrush is predicted by virtue of the robust nonlinear mapping capability of the GRNN. However, because the prediction effect of the GRNN is influenced by the smoothing factor, the CFOA is adopted to optimize this factor. In this way, influences of human factors during parameter determination of the GRNN prediction model are decreased, and the prediction accuracy and applicability of the model are improved. Results show that the CFOA–GRNN prediction model has an accuracy of 93.2% for whether floor water inrush will occur or not. Compared with the BPNN, RNN, and GRU network prediction model, the CFOA–GRNN model is superior in the prediction accuracy and generalization, and it can more accurately predict floor water inrush.

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Section: Introductionmentioning

confidence: 99%

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

et al. 2022

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“…Currently, remarkable theory and practice bases have been achieved by research carried out on the failure law, water inrush mechanism, and water inrush prediction [10][11][12][13][14][15][16][17][18]. Yin et al [19] developed a numerical model to predict the time and the longwall locations of flood occurrences.…”

Section: Introductionmentioning

confidence: 99%

Design of Longwall Coal Pillar for the Prevention of Water Inrush from the Seam Floor with Through Fault

et al. 2021

Geofluids

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Setting up a waterproof coal pillar is an important measure to prevent water inrush from the Weibei mining through fault floor. Based on the plastic slip line field theory, a mechanical model of floor water inrush induced by confined water in the through fault zone was established. The mechanical expressions of confined water pressure and the width of the waterproof coal pillar under the state of limit equilibrium were derived. Combining the laws of floor deformation, failure and fault activation under two kinds of coal pillar width, the safety width of the waterproof coal pillar was determined. Furthermore, the safety threshold is better than the empirical value mentioned in the “coal mine safety regulations.” Following this, grouting transformation was carried out on the K2 sand layer of the cut roadway floor. This provided a theoretical basis and engineering practice for water disaster prevention and the control of the structural floor under similar conditions in the Weibei mining area for future benefit.

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“…Therefore, based on the research traditional roof structure and overburden movement [22,23]， by means of microseismic monitoring [24][25][26][27], the breaking process of entry with RCN-P is monitored on site to clarify its breaking process and structural form. Being both theoretically and practically significant, the research results can provide references for the mechanism of entry failure, the relationship between "support and surrounding rock", the selection of support equipment, the design of entry retention parameters and the optimization of entry retention process.…”

Section: Introductionmentioning

confidence: 99%

Research on Roof Fracture Characteristics of Gob-Side Entry Retaining with Roof Cutting and Non-Pillar Mining in Thick Coal Seam, China

Zhao

Huang

et al. 2021

Preprint

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Based on the S1201-2 large height mining in the 2-2 coal seam of Ningtiaota colliery with on-site microseismic measurement, physical simulation and theoretical analysis methods, this paper explores the rule of roof movement in thick coal seams with roof cutting and non-pillar (hereinafter referred to as RCN-P) mining, so as to obtain scientific and effective theoretical basis for entry support and to summarize the regional structural characteristics and dynamic periodic fracture characteristics. As can be seen from microseismic events, the entry roof is featured by "two zones and one line" along the horizontal direction, namely, the crack generation area, the roof movement area. Additionally, and the obvious lateral breaking of the entry roof on the coal wall is a typical feature of the thick coal seam with RCN-P mining. The roof is vertically divided into "three zones", the crack generation area, the roof movement area and the crack development area. The roof cutting activity mainly affects the overburden activity within the basic roof height range, which is also the roof movement area. In addition, the distribution frequency and the intensity of microseismic events indicate the roof periodic breaking characteristics. The "breaking pressure relief,” “advanced crack development,” and “the limit breaking state” of roof breaking corresponds to the initial, middle, and final stage of breaking in the periodic weighting process, respectively. Compared with the normal mining, the RCN-P mining reduces the periodic weighting length and increases the pressure strength. As is shown in the physical simulation experiment, the basic roof and the cutting control layer in the "regional structural characteristics" constitute the “large” and “small” structures with RCN-P mining. The basic roof key layer is the core to control the stability of the strata, and the breaking process from the cantilever beam to the short masonry beam of the roof-cutting control layer is the main cause of the entry stress. Correspondingly, the basic structure model of “short masonry-hinged” roof was proposed and the calculation method of support was established for the entry with RCN-P mining in thick coal seam, providing a research foundation for scientific and effective rock formation control.

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Floor water inrush analysis based on mechanical failure characters and microseismic monitoring

Cited by 49 publications

References 26 publications

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

Design of Longwall Coal Pillar for the Prevention of Water Inrush from the Seam Floor with Through Fault

Research on Roof Fracture Characteristics of Gob-Side Entry Retaining with Roof Cutting and Non-Pillar Mining in Thick Coal Seam, China

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