The Feasibility of Mining Under a Water Body Based on a Fuzzy Neural Network

Guo, Hongjun; Ji, Ming; Chen, Kai; Zhang, Zongliang; Zhang, Yidong; Zhang, Minglei

doi:10.1007/s10230-018-0521-5

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…The interaction between water body and coal rock mass is a complex physical and chemical process. Chemical actions such as dissolution, hydration, dissolution, and oxidation-reduction may occur between the water body and coal rock mass, which will not only change the composition and structure of coal rock mass but also affect the mechanical properties of coal rock mass [38] The infiltration of water into the rock and soil will also change its physical properties. The water in the fractured rock mass will produce lubrication at the fracture contact surface, reduce the friction resistance at the fracture surface, enhance the shear stress effect, and induce the shear movement along the fracture surface.…”

Section: Impact Of Water On Mining Safetymentioning

confidence: 99%

Numerical Study on Characteristics of Bedrock and Surface Failure in Mining of Shallow-Buried MCS

2022

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Coal is one of the important energy sources for industry. When it is mined, it will cause the destruction of bedrock and surface. However, it is more severe in mining shallow-buried multi coal seams (SBMCS). To better reveal the characteristics of the bedrock and surface damage, we have carried out a theoretical analysis, as well as used numerical simulations and field monitoring methods to study the surface and bedrock damage caused by the mining of SBMCS. The characteristics of bedrock and surface failure structure, settlement, and stress distribution were studied and analyzed. The findings show that the collapsed block, formed by the rupture of the overlying stratum, interacts with the surrounding rock to form large cavities and gaps, and the stress concentration occurs between them. The maximum downward vertical concentration stress is about 9.79 MPa. The mining of the lower coal seam can lead to repeated failure of the upper bedrock and goaf. The settlement of bedrock presents gradient change, and the settlement of upper bedrock is large, about 8.0 m, and the maximum settlement is 8.183 m, while that of lower bedrock is small and about 3.5–4.0 m. The weak rock stratum in the bedrock is crushed by the change stress of repeated mining, and formed a broken rock stratum. The cracks in the bedrock develop directly to the ground. On the ground, tensile cracks, compression uplift, stepped cracks, and even collapse pits are easy to cause in mining SBMCS. Affected by repeated mining, the variation of surface vertical stress is complex and disorderly in the middle of the basin, and the variation of horizontal stress is mainly concentrated on the edge of the basin. The maximum stress reaches 100 KPa, and the minimum stress is about 78 KPa. Through theoretical analysis and discussion, the size of the key blocks is directly related to the thickness and strength of the rock stratum.

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Section: Impact Of Water On Mining Safetymentioning

confidence: 99%

Numerical Study on Characteristics of Bedrock and Surface Failure in Mining of Shallow-Buried MCS

2022

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“…Rezaei and Guo et al used a neural network intelligent prediction model to determine the height of the water flowing fracture zone and evaluate the performance of the model by using a variety of performance indicators (correlation coefficients, variances, etc.) [20,21]. Ruan et al proposed a prediction model for the water inrush based on the AHP and the Dempster-Shafer evidence theory, and the feasibility and applicability of the model were also verified [22].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation for the Threat Degree of a Thermal Reservoir to Deep Coal Mining

et al. 2020

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Taking the Suiqi coalfield located in North China as the object, where the coal seam burial depth is more than 1100 m, the water abundance of the roof pore thermal storage aquifer is better than average, the ground temperature is abnormally high, and hydrogeological data are relatively lacking, this paper selects and determines eight index factors that influence the mining of the coalfield. Based on the analytic hierarchy process (AHP), the index factor weight is defined, and then, the threat degree of the roof thermal storage aquifer to the coal mining is quantitatively evaluated and divided by using the fuzzy variable set theory. The evaluation results show that the threat degree of the roof in the eastern region is generally greater than that in the western region and that the closer it is to the coal seam outcrop line, the higher the threat degree is; near the boreholes, in the areas Qs1,Qs5, Qs8, Sx1, Tk5, Zc4, and Zc7, which are close to the hidden outcrop line of the coal seam, the classification characteristic value of the threat degree is greater than 3.5, which is in the high-threat zone for disasters caused by roof thermal storage aquifers during coal seam mining. The area above the medium-threat zone accounts for 71.82% of the total study area, indicating that deep coal mining is affected by multiple factors and that roof water and heat disasters are more likely to occur.

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“…In particular, neural networks such as recurrent neural networks (RNNs) [24], [25], deep belief networks [26], and radial basis function networks [27] have demonstrated excellent performance in sequence prediction problems. Therefore, neural networks are widely used in the field of mine water damage prediction, including the prediction model of flow height in a fractured zone [28] and the framework of probability assessment of mine water inrush accidents [29]. Bahrami et al [30] designed two hybrid methods coupling artificial neural networks with genetic algorithms and simulated annealing methods to predict the head of an open-pit mine.…”

Section: Introductionmentioning

confidence: 99%

A Gated Recurrent Unit Network Model for Predicting Open Channel Flow in Coal Mines Based on Attention Mechanisms

Gao

Guo³

et al. 2020

IEEE Access

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The prediction of water inflow during coal mining is an important issue. There are many factors that can affect the water inflow in mines. The intercoupling of these factors makes it difficult for current water inflow forecasting methods to meet the needs of real-time forecasting. Open channels are the main devices used for mine drainage, and their flow rate reflects the water inrush of a mine to some extent. This paper uses a hybrid neural network model combining attention mechanisms and a gated recurrent unit network to make real-time predictions of open channel flow. First, attention mechanisms are used to learn the interdependence between multisource hydrosensor data, and then, a gated recurrent unit network is employed to capture the dependencies on different time scales to improve the prediction accuracy of the neural network model. Finally, we design a series of comparative experiments to verify and analyse the performance of the hybrid neural network model. The experimental verification shows that the proposed model can learn the dependency relationships among multisource sensors, and the modelling of these dependencies can greatly improve the prediction accuracy of real-time flow in open channels. INDEX TERMS Mine water damage, Flow prediction, Neural network, Attention mechanism.

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The Feasibility of Mining Under a Water Body Based on a Fuzzy Neural Network

Cited by 10 publications

References 3 publications

Numerical Study on Characteristics of Bedrock and Surface Failure in Mining of Shallow-Buried MCS

Numerical Study on Characteristics of Bedrock and Surface Failure in Mining of Shallow-Buried MCS

Quantitative Evaluation for the Threat Degree of a Thermal Reservoir to Deep Coal Mining

A Gated Recurrent Unit Network Model for Predicting Open Channel Flow in Coal Mines Based on Attention Mechanisms

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