Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

doi:10.1007/s10040-017-1614-0

Cited by 83 publications

(40 citation statements)

References 28 publications

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“…When the hydraulic pressure, mine pressure and the stability of the water-resisting strata are in a relatively balanced state, the fault has the controlling effect. According to the in-situ observation of the field, combined with the comprehensive analysis of relevant data, it was concluded that there are five main factors affecting water inrush from floor [9,30,31]. 1.…”

Section: Analysis Of Influencing Factorsmentioning

confidence: 99%

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A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

Duan

Cai

et al. 2018

Water

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Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries.

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Section: Analysis Of Influencing Factorsmentioning

confidence: 99%

“…This forms a water inrush pathway from stability to instability for rock strata [8]. As a result, the sudden water pressure discharge from the floor under confined aquifer along the pathway [9]. Effective and timely water inrush prediction is an important way to curb the occurrence of multiple accidents in coal mines [10].…”

mentioning

confidence: 99%

A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

Duan

Cai

et al. 2018

Water

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“…The mechanical properties of coal and rock are of key importance in coal mining and exploration [1][2][3], rock strata control [4][5][6], hydraulic fracturing [7][8][9], rock burst [10,11], (enhanced) coal bed methane recovery (CBM/ECBM) [12], water hazard control [13,14], geo-sequestration [15,16], and underground coal gasification in coal mines and deep unmineable coal seams [17].…”

Section: Introductionmentioning

confidence: 99%

Mechanical and Heterogeneous Properties of Coal and Rock Quantified and Mapped at the Microscale

Sun

Zhang

et al. 2020

Applied Sciences

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Due to the impossibility of obtaining intact standard experimental samples, it is difficult to test the mechanical properties of soft and broken coal and rock obtained from deep coal mines. So, an advanced experimental technology based on a small sample volume, nanoindentation technology, was introduced and used to measure the mechanical parameters of them. By using the averaging method, the hardness of shale, mudstone and coal are 1191.90 MPa, 674.95 MPa and 424.30 MPa, respectively; their elastic moduli are 20.39 GPa, 11.72 GPa and 5.47 GPa; and their fracture toughness were 1.66 MPa·m0.5, 1.28 MPa·m0.5 and 0.77 MPa·m0.5. These three mechanical parameters were used to quantify and map the heterogeneous properties of coal and rock for convenience and accuracy. For example, the inter quartile range (IQR) of the hardness of shale, mudstone, and coal are 1502.10 MPa, 1016.20 MPa and 54.64 MPa, respectively, meaning that coal has the best homogeneity among them. Nanoindentation technology provides researchers with a convenient method to conduct mechanical experiments at the microscale.

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“…According to statistics, more than 25 billion tons of coal resources are at the risk of water inrushes in China 1 . With increased mining depths in recent years, the hydrogeological conditions of mining become more and more complicated, and the water inrushes from coal-roof strata are increasingly serious 2,3 . During coal extraction, the strata overlying the coal seams move significantly downward due to the rock pressure, forming multiple fractures and fissures in the coal-roof strata.…”

Section: Introductionmentioning

confidence: 99%

“…However, these MLAs also have certain limitations in practical applications. For instance, numerous data pre-treatment is required in the DT model, and it tends to fall into local optimum; as for the ANN model, it has the shortcomings of over learning, slow convergence speed and local minimum value 2 .…”

Section: Introductionmentioning

confidence: 99%

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

Zhao

2018

Sci Rep

Self Cite

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Water inrushes from coal-roof strata account for a great proportion of coal mine accidents, and the height of fractured water-conducting zone (FWCZ) is of significant importance for the safe production of coal mines. A novel and promising model for predicting the height of FWCZ was proposed based on random forest regression (RFR), which is a powerful intelligent machine learning algorithm. RFR has high prediction accuracy and is robust in dealing with the complicated and non-linear problems. Also, it can evaluate the importance of the variables. In this study, the proposed model was applied to Hongliu Coal Mine in Northwest China. 85 field measured samples were collected in total, with 60 samples (70%) used for training and 20 (30%) used for validation. For comparison, a support vector machine (SVM) model was also constructed for the prediction. The results show that the two models are in accordance with the field measured data, and RFR shows a better performance on good tolerance to outliers and noises and efficiently on high-dimensional data sets. It is demonstrated that RFR is more practicable and accurate to predict the height of FWCZ. The achievements will be helpful in preventing and controlling the water inrushes from coal-roof strata, and also can be extended to various engineering applications.

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Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

Cited by 83 publications

References 28 publications

A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

Mechanical and Heterogeneous Properties of Coal and Rock Quantified and Mapped at the Microscale

An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

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