Providing accurate prediction methods for mining-induced water fractured zones over the longwall goaf has become increasingly significant because of the grand economic and technological challenges of deeper extraction. Taking the No. 839 coalface in the Qingdong mining area as a case study, a comprehensive study of the water-flowing fractured zone (WFFZ) formation process and peak height were carried out by the radial basis function (RBF) neural network model, parallel electrical monitoring (PEM) technology, numerical simulation model and the empirical formula method. The results show that the destruction does not occur simply layer-by-layer from the bottom up. Rather, the fractured areas appear first in soft layers above the goaf. With the advance of footage, the destruction gradually extends from soft layers to hard layers, and the WFFZ is formed when the fractured areas fit closely together. Also, the change process of the WFFZ shape is closely related to rock mass heterogeneity. In addition, the results of the comparison suggest that the RBF neural network model had more accuracy in predicting the height of the WFFZ than the numerical simulation model and empirical formula method.

  • The change law of the shape of the WFFZ is revealed by means of field investigation and numerical simulation.

  • The prediction method of the height of the WFFZ is established based on the neural network model.

  • The RBF model has more accuracy than the numerical simulation model and empirical formula method.

In China, coal accounts for a large proportion of energy production and consumption (Fan et al. 2019; Liu et al. 2021; Sun et al. 2021). In recent years, mining-induced eco-geological environment and public health problems, such as water shortages, water pollution, vegetation death, desertification, and high human health risk (Kallioras & Ruzinski 2011; Liu et al. 2018; Wang et al. 2018; Xue et al. 2018), have received more and more attention. Engineering practice indicates that underground mining causes the deformation, separation, and fracture of overburdened strata (Liu et al. 2018; Wang et al. 2019; Fan et al. 2022a). Fractures interconnect with each other, forming a water-flowing fractured zone (WFFZ) in which water can flow (Figure 1) (Palchik 2010; Huang et al. 2018; Chi et al. 2022). Water-inrush accidents and ecological environment damage will occur in the mine when the WFFZ reaches the phreatic aquifer and there is serious water loss, as shown in Figure 1. The evolution rule of the WFFZ is of important practical significance for eco-geological environment protection and safety production evaluation (Wei et al. 2017; Xue et al. 2020).
Figure 1

Schematic diagram of the WFFZ.

Figure 1

Schematic diagram of the WFFZ.

Close modal

Numerous works have been done on the monitoring and evaluation of the height of the WFFZ. In terms of monitoring the WFFZ in a coalfield, Palchik (2010) found that the ratio between the maximum height of the WFFZ and the thickness of the extracted coal seam increases with the increasing number of rock layer interfaces and decreases with the increasing stiffness of the immediate roof. Miao et al. (2011) believed that borehole photography and borehole TV camera techniques are the most effective methods to detect fractures that have developed inside the overlying strata. On the basis of the quantity and energy distributions of the microseismic events recorded using the microseismic monitoring technique, a zoning method was established by Cheng et al. (2017) for the roof strata movement in the vertical and horizontal directions. Liu et al. (2018) and Du et al. (2021) used the Brillouin optical time-domain reflectometry to detect the mining-induced fractures of rock and soil layers, respectively. What is more, an in-site monitoring method based on formation micro-scanner image technology for the WFFZ penetrating into N2 laterite was carried out first by Liu et al. (2020) in western China. Using borehole video observation equipment, Wu et al. (2021) successfully observed the maximum height of the fissure zone and the location of bed separations at a depth of more than 600 m at a China coal mine. However, although field data are valuable, it is an expensive, time-consuming, and challenging task to measure on site the deformation characteristics of all overlying strata within the WFFZ. In addition, before coal seam mining, the in situ test cannot evaluate the height of the WFFZ due to the lag of the testing process.

From the perspective of evaluating the height of the WFFZ, the common methods include empirical formula, analytical calculation of mechanical model, physical model, and numerical simulation. For example, according to regression analysis of measured results of numerous coalfields in north China, an empirical formula was proposed by Liu (1981), which has been widely used in China. Key stratum theory is a mechanical model which takes into account both the layered characteristics of overlying strata and the difference in deformation characteristics of different rock formations (Qian et al. 1996). The key stratum theory believed that the entire or partial overburden strata movement after mining is controlled by strata structures of the ‘key stratum’, and the formation process of the WFFZ is closely related to the structural characteristics of the key stratum. Additionally, for the accurate estimation of the WFFZ height, the theoretical prediction method was introduced by Huang et al. (2018) based on the overlying composite strata structure and an empirical equation. Based on the failure characteristics of different rocks, Wang et al. (2012) established a mechanics model to predict the height of the WFFZ using strain and stress as the indices of water flow in soft and hard rock strata. Based on the plate and shell theory, a model to calculate the height of the WFFZ was established by Zhu et al. (2020), and the development of the WFFZ in the bedrock and loess layer was discussed in detail. Several important discoveries were obtained in the previous research and helped to strengthen the prediction of the WFFZ height. However, the development of the WFFZ is a very complicated nonlinear issue that is influenced by many factors, mainly geological factors and mining factors (Fan et al. 2022b). Those above have several defects in practical application, such as complex calculation and large prediction errors.

Reasonable consideration of the contribution discrepancy of various factors in the WFFZ height is the premise of accurate prediction. In this paper, taking the No. 839 coalface in the Qingdong mining area, Anhui, China, as a case study, an RBF model is established based on 39 collected groups of measured data. In this RBF model, besides the mining thickness, mining depth, and inclined length of the coalface (LC), the proportion coefficient of hard rock is introduced to quantify the overburden structure. Furthermore, parallel electrical monitoring (PEM) technology and numerical simulation method are also used to study the entire process of WFFZ changes, including the evolution of height and shape. Finally, the accuracy of the RBF model was verified by measured data. The results are significant for mining subsidence control, methane drainage, and preventing water inrush from the roof.

The research was conducted at the Qingdong Coal Mine located in the Huaibei mining area of Anhui Province, China (Figure 2(a)). This area belongs to the monsoon warm temperate semi-humid climate, with the northeast wind in spring and autumn, east-southeast wind in summer, and north-northwest wind in winter. The average wind speed is 3 m/s and the maximum is 18 m/s. The average annual precipitation is 811.8 mm, mostly concentrated in July and August. The annual average temperature is 14.6 °C, the highest temperature (June 11, 1972) was 41.1 °C, and the lowest temperature (February 5, 1969) was −21.3 °C. Most rivers in the region are in the Huaihe River system, mainly including the Guo River, Wujia River, Beifei River, Bao River, and Tuo River (Figure 2(b)). Most rivers flow from the northwest to the southeast and join the Huaihe River. All rivers are small and medium-sized seasonal rivers, and the water volume is controlled by atmospheric precipitation. The average annual flow of each river is 3.52–72.10 m3/s, and the average annual water level elevation is 14.73–26.56 m.
Figure 2

(a) Location and (b) regional geological map of the Huaibei coalfield.

Figure 2

(a) Location and (b) regional geological map of the Huaibei coalfield.

Close modal

The Huaibei coal mining area is a concealed coalfield covered by loose layers (Figure 2(b)). The Subei fault divides the Huaibei coalfield into north and south parts. The thickness of the loose layer in the north of Huaibei Coalfield is relatively thin, with an average of 50–150 m. The thickness of the loose layer in the south is relatively thick, with an average of 150–350 m, and the thickest part is 667 m. According to the pumping test data over the years, the maximum water inflow at the bottom of the loose layers is 2.389 L×(m×s)−1, which is the main source of roof water inrush in the working face near the loose layers.

The paper takes the No. 839 coal mine in the Qingdong mining area as an example for studying the WFFZ formation process and peak height. As shown in Figure 3(a), the range of burial depth of the coal seam floor is −325 to −520 m and the original structure of No. 839 coal mine is simple. The mining coal seam belongs to the Lower Shihezi Formation, with a dip angle of 15°–20°. The roof of the mining coal seam is mainly fine sandstone and mudstone (Figure 3(b)). The length of the No. 839 working face along the incline is 160 m, and its strike length is 640.83 m. The average mining thickness during the monitoring period is 5.7 m.
Figure 3

(a) Plane layout and (b) histogram of panel 839 of the Qingdong Coal Mine.

Figure 3

(a) Plane layout and (b) histogram of panel 839 of the Qingdong Coal Mine.

Close modal

PEM technology

In recent years, with the development of electrical exploration techniques and monitoring equipment, PEM technology, one of the direct current methods, had been widely applied and popularized in the evaluation of surrounding rock deformation and failure, groundwater seepage monitoring, grouting detection, and effect evaluation, water-rich abnormal area identification, etc. Compared with the other direct current methods, PEM has huge advantages of boasting faster data collection with parallel, efficient, and massive data collection and processing (Sun et al. 2021; Bharti et al. 2022).

The basic principle of PEM is that the electrodes are placed inside the borehole at intervals of 1 m and the initial apparent resistivity of overlying strata is determined before coal mining. During coal mining, the resistivity change of the overlying strata around the borehole due to deformation and fracture can be reflected by electrodes located on the drilling wall. In this process of coal seam mining, on the one hand, many empty cavities form inside overlying strata after fracture and collapse of the roof due to redistribution of stress, increasing the apparent resistivity of the surrounding rock strata. On the other hand, for the water-rich area, these cavities provide a channel for water, causing the water to flow freely and the corresponding apparent resistivity to reduce because water has a strong conductive effect. So, it is feasible through monitoring the change in rock mass apparent resistivity to measure the range that is affected by the coal mining.

The monitoring hole was located in the 1# chamber at the air roadway along the corresponding advancing direction (Figure 3(a)), and the schematic map of PEM in this study is shown in Figure 4. The initial apparent resistivity and lithology of overlying strata are determined when DB (the distance between the working face and monitoring hole) is about 150 m, as shown in Figure 5. The apparent resistivity test results of the overlying strata are mainly distributed unevenly in the range of 5–1,000 Ωm, reflecting the water abundance characteristics of sandstone and mudstone layers. The test values provide a basis for the comparison of subsequent analysis.
Figure 4

Layout of the PEM system for coal seam roof mining failure.

Figure 4

Layout of the PEM system for coal seam roof mining failure.

Close modal
Figure 5

Profile of background value of apparent resistivity of borehole in coal seam roof.

Figure 5

Profile of background value of apparent resistivity of borehole in coal seam roof.

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RBF model

Compared with other prediction methods, the artificial neural network has the characteristics of nonlinear mapping and self-learning, and can recognize patterns in complex data by simulating the structure and operational mechanism of the human neural system. The artificial neural network has been successfully applied to wind-speed forecasting (Liu et al. 2012), predicting unconfined compressive strength of rocks (Momeni et al. 2015), stability of rubble mound breakwaters (Gedik 2018), predicting floor failure depth under deep seam mining (Hu et al. 2019) and data gap filling (Silva et al. 2018). The back propagation neural network and radial basis function (RBF) neural network are probably the most widely used models. In the identification of neural network systems, the main difference between the back propagation and RBF neural network is how the excitation function is used. The excitation function of the back propagation neural network is global, and the excitation function of the RBF neural network is local. For the global excitation function, all the weights will be constantly corrected in each calculation process, resulting in a large amount of calculation and slow convergence speed, and it easily falls into a local minimum due to its own defects. In contrast, the convergence rate of the RBF neural network is significantly better than that of the back propagation neural network, and the problem of falling into a local minimum is avoided.

Selection of influencing factors

Providing accurate influencing factors on overlying strata failure height is an important prerequisite for success in RBF forecasting (Dai et al. 2020). According to production practices and the research results of engineering geology and hydrogeology at China's coalfields, there are four factors that directly affect the height of the mining-induced water fractured zone under normal conditions:
  • Mining thickness (M): This index reflects the influence of the underground excavation height on the stress redistribution and fracture of roof rock mass, so it is one of the main indices. In the traditional empirical formula, at the ‘specification for coal pillar and coal mining in buildings, water bodies, railways, and main roadways’, it is the only influencing parameter for predicting the WFFZ height.

  • Mining depth (d): According to ground pressure theory, with the increase of mining depth, the original stress in the roof increases accordingly, and the degree of roof damage increases at the same time.

  • Inclined length (LC): A coalface with large-inclined length will have a greater degree of roof rock fracturing according to some research, therefore, the factor was selected in the RBF input layer for this study.

  • Proportional coefficient of hard rock (α): The structure characteristics of overburden play an important part in the height of the mining-induced water fractured zone. The commonly used method now divides the roof structure, depending on the uniaxial compressive strength, into four types: hard, medium-hard, weak, and very weak. This method cannot quantitatively reflect the structural characteristics of overburden, especially in composite strata containing various soft and hard rocks. So, a quantitative index, the proportional coefficient of hard rock in the overburden, was selected in the RBF input layer instead of traditional taxonomy results. The proportional coefficient of hard rock is calculated:
    (1)
    where is the cumulative thickness of hard rock strata, mainly refering to fine sandstone, medium sandstone, coarse sandstone, limestone, migmatite, and igneous rock, within the estimated WFFZ height; M is the mining thickness.

    As Table 1 shows, the measurement data of 39 coal mining faces in the north China mining area were collected. The statistical value of inputs and output and distribution figures of the variables used for developing the RBF model in the learning and testing phase are given in Figure 6.

Figure 6

Distribution and histogram of measurement data used for learning and testing: (a) mining thickness (m), (b) mining depth (m), (c) inclined length of the coalface (m), (d) proportional coefficient of hard rock (m), (e) Hw (m).

Figure 6

Distribution and histogram of measurement data used for learning and testing: (a) mining thickness (m), (b) mining depth (m), (c) inclined length of the coalface (m), (d) proportional coefficient of hard rock (m), (e) Hw (m).

Close modal
Table 1

Measurement data used for learning and testing

No.M (m)d (m)LC (m)αhw (m)Name of the coalface
450 170 0.55 86.8 Xinlongzhuang mine 4320 coalface 
8.13 409 193 0.52 72.9 Xinlongzhuang mine 1301 coalface 
2.8 269 156 0.68 50.34 Xinlongzhuang mine 2308-2 coalface 
2.8 264.5 156 0.93 44.34 Xinlongzhuang mine 2306-1 coalface 
2.6 265 147 0.6 43.43 Xinlongzhuang mine 2306-2 coalface 
2.8 264.5 148.5 0.26 40.35 Xinlongzhuang mine 2306-3 coalface 
2.6 290 168 46.22 Xinlongzhuang mine 2302-1 coalface 
2.6 290 168 0.37 38.41 Xinlongzhuang mine 2302-2 coalface 
2.6 290 168 0.18 39.14 Xinlongzhuang mine 2302-3 coalface 
10 2.5 265 192 0.93 40.21 Xinlongzhuang mine 2300-1 coalface 
11 2.7 265 192 0.56 42.81 Xinlongzhuang mine 2300-2 coalface 
12 2.6 295 185 0.64 40.5 Xinlongzhuang mine 2301-1 coalface 
13 433 168 0.52 72.97 Xinlongzhuang mine 5306 coalface 
14 7.4 331 160 0.55 64.25 Xinlongzhuang mine 4314 coalface 
15 5.3 312 145.7 0.24 44.2 Xinlongzhuang mine 2303-2-3 coalface 
16 5.7 283.9 177.9 0.63 51.4 Xinlongzhuang mine 2301-2-3 coalface 
17 7.5 665 222 0.19 53.7 Yangcheng mine 1305 coalface 
18 8.7 434.6 153 0.62 71 Baodian mine 1303 coalface 
19 8.7 418.6 198 0.45 83 Baodian mine 1310 coalface 
20 7.5 367 173.5 0.47 75.5 Baodian mine 1314 coalface 
21 7.53 357 170 0.38 61.9 Baodian mine 1316 coalface 
22 7.52 367 190 0.41 61.77 Baodian mine 5306 coalface 
23 520 200 0.35 58.46 Xieqiao mine 1211 coalface 
24 520 180 0.33 67.86 Xieqiao mine 1221 coalface 
25 5.8 570 178 0.34 65.25 Panyi mine 2622 coalface 
26 4.5 370 135 0.45 57.47 Zhangji mine 1221 coalface 
27 3.9 370 200 0.42 49.05 Zhangji mine 1212 coalface 
28 8.1 329 134 0.45 83.9 Xinjiyi mine 1301 coalface 
29 6.1 475 170 0.37 64.6 Jiningsan mine 1301coalface 
30 649.1 186 0.23 42.99 Liangbaosi mine 3202 coalface 
31 6.7 272 120 0.53 64.99 Yangcun mine 301 coalface 
32 9.5 450 123 0.65 78 Zhuxianzhuang mine 863 coalface 
33 13.43 490 123 0.7 130.78 Zhuxianzhuang mine 865 coalface 
34 5.1 255 78 0.5 51.3 Xuchang mine 1302 coalface 
35 3.8 270 168 0.65 54.6 Jiangzhuang mine 803 coalface 
36 5.77 400 154 0.81 70.7 Nantun mine 63-10 coalface 
37 4.8 485 175 0.36 62.5 Nantun mine 93-01 coalface 
38 7.69 207 240 0.51 62.31 Pingshuo mine S4104 coalface 
39 4.7 368 297 0.39 56 Renlou mine 7212 coalface 
No.M (m)d (m)LC (m)αhw (m)Name of the coalface
450 170 0.55 86.8 Xinlongzhuang mine 4320 coalface 
8.13 409 193 0.52 72.9 Xinlongzhuang mine 1301 coalface 
2.8 269 156 0.68 50.34 Xinlongzhuang mine 2308-2 coalface 
2.8 264.5 156 0.93 44.34 Xinlongzhuang mine 2306-1 coalface 
2.6 265 147 0.6 43.43 Xinlongzhuang mine 2306-2 coalface 
2.8 264.5 148.5 0.26 40.35 Xinlongzhuang mine 2306-3 coalface 
2.6 290 168 46.22 Xinlongzhuang mine 2302-1 coalface 
2.6 290 168 0.37 38.41 Xinlongzhuang mine 2302-2 coalface 
2.6 290 168 0.18 39.14 Xinlongzhuang mine 2302-3 coalface 
10 2.5 265 192 0.93 40.21 Xinlongzhuang mine 2300-1 coalface 
11 2.7 265 192 0.56 42.81 Xinlongzhuang mine 2300-2 coalface 
12 2.6 295 185 0.64 40.5 Xinlongzhuang mine 2301-1 coalface 
13 433 168 0.52 72.97 Xinlongzhuang mine 5306 coalface 
14 7.4 331 160 0.55 64.25 Xinlongzhuang mine 4314 coalface 
15 5.3 312 145.7 0.24 44.2 Xinlongzhuang mine 2303-2-3 coalface 
16 5.7 283.9 177.9 0.63 51.4 Xinlongzhuang mine 2301-2-3 coalface 
17 7.5 665 222 0.19 53.7 Yangcheng mine 1305 coalface 
18 8.7 434.6 153 0.62 71 Baodian mine 1303 coalface 
19 8.7 418.6 198 0.45 83 Baodian mine 1310 coalface 
20 7.5 367 173.5 0.47 75.5 Baodian mine 1314 coalface 
21 7.53 357 170 0.38 61.9 Baodian mine 1316 coalface 
22 7.52 367 190 0.41 61.77 Baodian mine 5306 coalface 
23 520 200 0.35 58.46 Xieqiao mine 1211 coalface 
24 520 180 0.33 67.86 Xieqiao mine 1221 coalface 
25 5.8 570 178 0.34 65.25 Panyi mine 2622 coalface 
26 4.5 370 135 0.45 57.47 Zhangji mine 1221 coalface 
27 3.9 370 200 0.42 49.05 Zhangji mine 1212 coalface 
28 8.1 329 134 0.45 83.9 Xinjiyi mine 1301 coalface 
29 6.1 475 170 0.37 64.6 Jiningsan mine 1301coalface 
30 649.1 186 0.23 42.99 Liangbaosi mine 3202 coalface 
31 6.7 272 120 0.53 64.99 Yangcun mine 301 coalface 
32 9.5 450 123 0.65 78 Zhuxianzhuang mine 863 coalface 
33 13.43 490 123 0.7 130.78 Zhuxianzhuang mine 865 coalface 
34 5.1 255 78 0.5 51.3 Xuchang mine 1302 coalface 
35 3.8 270 168 0.65 54.6 Jiangzhuang mine 803 coalface 
36 5.77 400 154 0.81 70.7 Nantun mine 63-10 coalface 
37 4.8 485 175 0.36 62.5 Nantun mine 93-01 coalface 
38 7.69 207 240 0.51 62.31 Pingshuo mine S4104 coalface 
39 4.7 368 297 0.39 56 Renlou mine 7212 coalface 

RBF neural network model

In this study, a three-layered RBF neural network consisting of an input layer, hidden layer, and output layer was used (Figure 7). The input nodes make a transition of input variables from the input layer to the hidden layer, in which a Gaussian activation function shapes the hidden layer nodes. This neural network reacts to the input signals close to the center of the base function. The resulting output of the hidden layer is transmitted to the output layer, which mainly employs a simple linear function. As shown in Figure 7, a 4 × 10 × 1 network structure was designed, in which x1, …, x4 are the network inputs and y1, …, y10 are the center of the Gaussian function in the hidden layer.
Figure 7

A three-layered RBF neural network. W is weighting, Y is the inverse normalized data of the output.

Figure 7

A three-layered RBF neural network. W is weighting, Y is the inverse normalized data of the output.

Close modal
According to the previous study (Wu 2017), the sample data need to be normalized (–1, 1) in order to remove the interference caused by different amplitudes and sizes, and the regularization equations are as follows:
(2)

In this equation, xi, xmin, xmax are the ith input parameter of the network, minimum input, and maximum input, respectively.

The regularized values of the four WFFZ height impact factors were used as the input layer, and the quantified result values were the output layer. Based on the proportion 1:6, the network model was trained and verified by normalizing 33 groups of data and then tested with six groups of data. The validation samples were regularized and treated as input values for the neural network; the corresponding output values were then obtained through the trained network. The actual output values were then compared with the values of the goal output. If the errors of the computations are within the permissible range, the constructed network is then acceptable. Otherwise, the network has to be retrained until this requirement has been satisfied.

The correlation between outputs and targets can be measured by regression values R. As shown in Figure 8, the R values for training, validation, and testing in this RBF model are 97.91%, 95.27%, and 92.41%, respectively. In addition, the error statistics of Nash–Sutcliffe (NS) coefficient of efficiency, mean absolute error (MAE), and mean absolute percentage error (MAPE) were calculated to evaluate the accuracy and stability of the RBF model, which can be determined by the following equations, respectively:
(3)
(4)
(5)
where n is the measured number of targets; hs is the predicted value of the WFFZ height, m; hsf is the measured value of the WFFZ height, m; is the mean of the measured value of the WFFZ height, m.
Figure 8

Training results of the RBF model.

Figure 8

Training results of the RBF model.

Close modal

In the training phase, the values of NS, MAE, and MAPE are 0.971, 1.961, and 3.326 m (Figure 8), respectively, which indicated that the RBF model was highly precise. This treatment is also true in the testing phase, with NS, MAE and MAPE at 0.908, 3.536, and 5.237 m (Figure 8). Therefore, it is practicable to predict the WFFZ height in deep coal excavation through this RBF model.

Numerical simulation

Numerical simulation is a common method for studying the deformation and failure of overburden strata in mining. Continuum-mechanics-based numerical simulation methods are widely used in engineering geology. In this study, numerical modeling was performed using the finite difference method implemented through the three-dimensional Fast Lagrangian Analysis of Continua (FLAC3D, version 5.0) software. Based on the stratum structure of No. 839 coalface at the PEM monitoring location (Figure 5), a 3D analysis model was established as shown in Figure 9. Strike direction was plotted on the y-axis against the dip direction on the x-axis. Model dimensions were 160 m × 220 m × 150 m. The mining space was 100 m × 160 m × 5.7 m. There was a 30 m protective coal pillar on both the x- and y-axis to minimize boundary effects. The excavation was in 5 m steps for a total of 20 steps. A Mohr–Coulomb model was selected for this model. On the basis of the laboratory test results, the mechanical parameters of the rock and coal used in this numerical simulation are listed in Table 2. The vertical displacement at the base and the horizontal displacement along the sides are limited. The upper boundary has no restriction. A vertical load (P = ∑ρgh = 5.5 MPa) is applied on the top to simulate the 120 m unconsolidated layers and 180 m overlying rock weight.
Table 2

Physico-mechanical parameters of rock mass

LithologyBulk modulus (GPa)Shear modulus (GPa)Cohesion (MPa)Internal friction angle (°)
Mudstone 4.05 2.62 1.85 31 
Coal seam 3.65 1.86 1.25 20 
Mudstone 4.58 3.10 1.68 30 
Siltstone 4.33 3.05 1.57 35 
Fine sandstone 3.08 2.24 2.00 32 
Mudstone 4.58 3.10 1.68 30 
Fine sandstone 3.67 2.74 1.85 33 
Mudstone 6.41 4.03 2.15 30 
Fine sandstone 5.91 5.02 2.35 33 
Mudstone 4.83 2.10 2.18 31 
Fine sandstone 5.20 3.20 2.08 33 
Mudstone 3.45 1.88 1.94 29 
Coal seam 3.25 1.78 1.05 21 
Siltstone 5.21 4.05 1.94 35 
Mudstone 3.83 1.95 1.88 29 
Coal seam 3.71 1.90 1.25 22 
LithologyBulk modulus (GPa)Shear modulus (GPa)Cohesion (MPa)Internal friction angle (°)
Mudstone 4.05 2.62 1.85 31 
Coal seam 3.65 1.86 1.25 20 
Mudstone 4.58 3.10 1.68 30 
Siltstone 4.33 3.05 1.57 35 
Fine sandstone 3.08 2.24 2.00 32 
Mudstone 4.58 3.10 1.68 30 
Fine sandstone 3.67 2.74 1.85 33 
Mudstone 6.41 4.03 2.15 30 
Fine sandstone 5.91 5.02 2.35 33 
Mudstone 4.83 2.10 2.18 31 
Fine sandstone 5.20 3.20 2.08 33 
Mudstone 3.45 1.88 1.94 29 
Coal seam 3.25 1.78 1.05 21 
Siltstone 5.21 4.05 1.94 35 
Mudstone 3.83 1.95 1.88 29 
Coal seam 3.71 1.90 1.25 22 
Figure 9

A numerical simulation model.

Figure 9

A numerical simulation model.

Close modal

Traditional empirical formula method

According to more than 200 group field measurement results from 27 mines, Liu (1981) presented an empirical formula for calculating WFFZ height based on mining thickness and strata lithology:
(6)
where hw is the maximum WFFZ height, m; a and b are coefficients that depend upon the strata lithology; and σ is the mean square deviation. According to Liu ‘s suggestion, in this study, the parameters of a, b and σ are 1.6, 3.6 and 5.6, respectively.

PEM field investigation

Figure 10 illustrates the resistivity distributions of roof rock mass measured from the monitoring hole during the coal mining. One important discovery is that the failure of roof rock did not occur simply layer-by-layer from the bottom up. Rather, at the initial stage, the fractured areas appeared first in and around the soft stratum which may have multiple layers above the goaf. As the coalface continued to advance, the fracture areas were expanding persistently and fused together, and a complete mining-induced water fractured zone was formed finally. For example, when DB is about 80 m, in the detection region, resistance anomaly areas first appear in two areas, with the names of A and B abnormal areas, which are 46–56 m and 0–18 m above the coal seam, respectively (Figure 10(a)). The A and B areas expand rapidly and the range of undamaged areas, between A and B areas, is increasingly narrowed with the advance of the working face (Figure 10(b)). When DB is about 60 m, a connection with A and B areas is established, and the WFFZ first appears in the detection region with a height of about 61 m (Figure 10(c)). After the DB is less than 33.5 m, the highest point of the WFFZ appears to be stable at a height of about 67.5 m (Figure 10(e)).
Figure 10

Detection results of the PEM method: (a) DB = 80 m, (b) DB = 73 m, (c) DB = 60 m, (d) DB = 45 m, (e) DB = 33.5 m, (f) DB = 15 m.

Figure 10

Detection results of the PEM method: (a) DB = 80 m, (b) DB = 73 m, (c) DB = 60 m, (d) DB = 45 m, (e) DB = 33.5 m, (f) DB = 15 m.

Close modal

Another discovery is about the shape of the WFFZ. Limited by traditional monitoring methods, previous studies have focused on the final shape of the WFFZ rather than the evolution process. According to previous research results, the final shape of the WFFZ presented several patterns, mainly consisting of the ‘rectangle shape’, ‘arched shape’ and ‘saddle shape’ (Huang et al. 2018). In this study, the evolution process of WFFZ shape has been proved. The lateral boundary of the WFFZ had undergone many changes in the process of mining. The initial shape of the WFFZ lateral boundary was near saddle-shaped vertically when DB was 60 m (Figure 10(c)). Then, the neck part of the boundary gradually disappeared with the development of the WFFZ (Figure 10(d)). Next, the shape of the WFFZ lateral boundary was near half-saddle shape when DB was 33.5 m (Figure 10(e)). Finally, the lateral boundary of the WFFZ tended to be U-shaped (Figure 10(f)). The main reasons for the shape changes are the inhomogeneous structure of strata and that the rock destruction apparently lags behind the advanced distance of the working face, especially for the hard stratum.

RBF model

The height of the WFFZ in this study was predicted using the RBF neural network model. The mining thickness is 6.4 m, the inclined LC is 160 m, the mining depth is 402.96 m, and the proportional coefficient of hard rock is 0.6; then, the RBF neural network model gives a predicted height of the WFFZ of No. 839 coalface of 67.32 m.

Numerical simulation

The development process and height of the WFFZ can be reflected by changes in plastic zones (Figure 11). The first failure of the immediate roof appeared when the advancing distance was 10 m (Figure 11(a)). With excavation advancing, the influence of coal mining on the roof expanded in the vertical and advanced directions, and plastic zone I reached a height of 15.87 m when the working face advanced to 30 m (Figure 11(b)). With the coal mining going on, the state of the overlying strata above plastic zone I was differentiated. When the face advanced to 50 m, plastic zone II appeared 10.78 m above zone I, and the rock masses between zone I and II were less affected by disturbance (Figure 11(c)). In the following excavation, the range of zone I and II expansion increased in the vertical and advanced directions. The WFFZ formed as the excavation reached about 70 m, where the two plastic zones fit closely together and overlap one another. At this point, the height of the WFFZ was 55.79 m (Figure 11(d)).
Figure 11

FLAC3D simulation results: (a) advancing distance 10 m, (b) advancing distance 30 m, (c) advancing distance 50 m, (d) advancing distance 70 m, (e) advancing distance 85 m, (f) advancing distance 100 m.

Figure 11

FLAC3D simulation results: (a) advancing distance 10 m, (b) advancing distance 30 m, (c) advancing distance 50 m, (d) advancing distance 70 m, (e) advancing distance 85 m, (f) advancing distance 100 m.

Close modal

When the excavation reached 70–85 m, the height of the WFFZ increased in the advanced direction but changed less in the vertical direction (Figure 11(e)), and after the excavation was 85 m, the height of the WFFZ tended to stabilize, as shown in Figure 11. The height of the WFFZ was 65.36 m when the excavation was 100 m (Figure 11(f)).

Traditional empirical formula method

According to the conditions of working face 839, the mining thickness was 5.7 m, and the height of the WFFZ can be calculated as follows:

Comparative analysis

The comparison between the predictions from the four models is shown in Table 3. The table shows that the predicted values obtained by the RBF neural network model and by numerical simulation coincided with an observed result by PEM, with a relative error of 0.27% and 3.17%, respectively. In comparison, the results from the RBF neural network model are seen to be closer to the measured values than the results from the calculation based on numerical simulation or empirical formula, with less error and higher precision, which meets practical needs.

Table 3

Comparison of the WFFZ height by different ways

MethodsWFFZ height (m)Relative error compared with measured value (%)
PEM 67.5 – 
RBF model 67.32 0.27 
Numerical simulation 65.36 3.17 
Empirical formula 39.41–50.41 25.3–41.6 
MethodsWFFZ height (m)Relative error compared with measured value (%)
PEM 67.5 – 
RBF model 67.32 0.27 
Numerical simulation 65.36 3.17 
Empirical formula 39.41–50.41 25.3–41.6 

The table also demonstrates a significant difference between the predicted result by empirical formula and the observed result by PEM, with a relative error of 25.3%–41.6%. It proves that the predicted results of the existing empirical formula cannot effectively predict the height of the WFFZ. Also, the inapplicability of the empirical formula is attributed to the limited mining technology, outdated measured data, different geological conditions, and coal seam characteristics (Dai et al. 2020).

In this study, the development process and height of the mining-induced water fractured zone were researched by PEM technology, neural network model, numerical simulation model and the empirical formula methods. The major results are:

  • The results by PEM and numerical simulation have shown that the WFFZ did not develop upwards simply layer-by-layer from the bottom up. Rather, the fractured areas appear first in soft layers above the goaf. With the advance of footage, the destruction gradually extends from soft layers to hard layers, and the WFFZ is formed when the fractured areas are connected with each other. Also, the change process of the WFFZ shape is closely related to rock mass heterogeneity.

  • Providing accurate influencing factors is a key factor for building the RBF neural network model. Besides the mining thickness, mining depth, and inclined LC, the proportion coefficient of hard rock was introduced to quantify the overburden structure. Finally, an effective RBF model was established through learning and training based on the 39 collected groups of measured data.

  • Based on the detection results by PEM, the rationality of the RBF model was proved. Also, the RBF model is a more effective method to predict the height of the WFFZ in comparison with numerical simulation and the traditional empirical formula method.

Financial support for this work is provided by the State Key Program of the National Natural Science Foundation of China under Grant No. 41931284 and the Natural Science Foundation of Jiangsu Province under Grant BK20190646.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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