The stability of deep “three-soft” coal seam roof has always been a key issue in coal mining. There are a lot of factors affecting the stability of deep three-soft coal seam outburst roof. However, there is currently no definite method able to draw an accurate assessment conclusion on roof stability. In order to accurately determine the main influencing factors of the stability of deep three-soft coal seam outburst roof and reduce the loss of coal production, this paper performed three-soft coal seam risk identification on Lugou Mine based on the introduction of the fuzzy analytic hierarchy process theory. 23 main risk factors were identified. Then, it established a hierarchical structure model of coal seam roof stability in accordance with experts’ opinions. The analytic hierarchy process was used to calculate the weights of indicators at all levels. Next, the paper used the fuzzy comprehensive evaluation method and expert scoring to evaluate various risk factors in the indicator system, as well as the overall safety level. The results showed that the deep three-soft coal seam stability of Lugou Mine ranks the third hazard level. The main risk and harmful factors include safety awareness, safety monitoring system, roof weakness, ventilation system, fire-fighting system, and rock bolt quality. In response to the evaluation results, this paper formulated corresponding control measure in terms of ventilation risk, safety monitoring risks, construction personnel risks, and fire protection risk to reduce losses in the mining process, providing a new evaluation method for the stability assessment of deep outburst coal seam roof.
China is a country with a huge coal resource storage. The total proven coal reserves are 5.57 trillion tons, ranking first in the world. In recent years, with the rapid development of the social economy, the demand for energy has increased in China [
At present, there is not much analysis of the stability of three-soft coal seam outburst roof. Coal seams are just evaluated by a single qualitative or quantitative evaluation method. The analysis results often have large errors. The fuzzy analytic hierarchy process combines qualitative and quantitative methods, making evaluation results more reasonable and scientific. Laarhoven and Pedrycz [
On the basis of the above analysis, although scholars at home and abroad had applied the fuzzy analytic hierarchy process to many aspects, no one has analyzed the stability of deep three-soft coal seam roof in coal mines. Fuzzy mathematics can be used for qualitative analysis. The analytic hierarchy process can solve problems quantitatively. The fuzzy analytic hierarchy process combines the qualitative and quantitative analyses to make results more scientific and reasonable.
The fuzzy analytic hierarchy process is a systematic analysis method combining qualitative and quantitative analysis. It is a comprehensive application of the analytic hierarchy process and the fuzzy evaluation method [
For specific operations of the fuzzy analytic hierarchy process, different items have diverse operations. The risk assessment of the stability of deep three-soft coal seam outburst roof can be implemented in four stages [
Specific operation process.
Create a hierarchical structure diagram.
Establish an analytic hierarchy process diagram to identify the risks of the project.
Calculate the weights of primary and secondary indicators.
Compare each factor in the criterion layer with each factor (scheme) in the scheme layer and score the importance with the number within the interval [
The values of
A matrix
Values of randomness indicators.
Matrix order | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|
0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 |
Determine the fuzzy vector and comment set.
Each factor in the indicator layer was scored by 10 experts. The score vector
Perform the comprehensive risk assessment.
The weight of each risk factor is
Then, the comprehensive evaluation grade score of
After the grade score corresponding to each factor of the criterion layer is calculated, the corresponding risk level can be obtained in accordance with the score interval in the comment set
From the identification of main risk factors in Section
Stability risk assessment indicators of Lugou Mine’s three-soft coal seam outburst roof.
From the risk assessment indicator system diagram of the three-soft coal seam outburst roof stability of the Lugou Mine, it can be known that there are 4 primary indicator factors. After the weights of these 5 indicators are calculated by the analytic hierarchy process, the judgment matrix
Target layer A | |||||
---|---|---|---|---|---|
1 | 1 | 1/3 | 1/2 | 1/2 | |
1 | 1 | 1/3 | 1/3 | 1 | |
3 | 3 | 1 | 1/2 | 1 | |
2 | 3 | 2 | 1 | 1/2 | |
2 | 1 | 1 | 2 | 1 |
The square root method is used to solve the approximate value
After the normalization of
Get
Python is used to obtain the value of
It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid.
The analytic hierarchy process is still used to calculate the weight of indicators. First, establish the judgment matrix for Similarly, the following can be obtained from the data calculation in Table From Table It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid. Establish a judgment matrix for The following can be obtained from the data calculation in Table From Table It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid. Establish a judgment matrix for The following can be obtained from the data calculation in Table From Table It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid. Establish a judgment matrix for The following can be obtained from the data calculation in Table From Table It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid. Establish a judgment matrix for The following can be obtained from the data calculation in Table From Table It shows that the discriminant matrix satisfies the consistency requirement, that is, the obtained eigenvector is valid.
1 | 2 | 1 | 3 | 4 | 1.8882 | 0.3243 | |
1/2 | 1 | 1/2 | 1 | 2 | 0.8706 | 0.1495 | |
1 | 2 | 1 | 3 | 4 | 1.8882 | 0.3243 | |
1/3 | 1 | 1/3 | 1 | 2 | 0.7402 | 0.1271 | |
1/4 | 1/2 | 1/4 | 1/2 | 1 | 0.4353 | 0.0748 |
B2 | ||||||
---|---|---|---|---|---|---|
1 | 6 | 3 | 2 | 2.4495 | 0.5032 | |
1/6 | 1 | 1/2 | 1/3 | 0.4082 | 0.0839 | |
1/3 | 2 | 1 | 1 | 0.9036 | 0.1856 | |
1/2 | 3 | 1 | 1 | 1.1067 | 0.2273 |
B3 | |||||||
---|---|---|---|---|---|---|---|
1 | 3 | 1 | 2 | 3 | 1.7826 | 0.3332 | |
1/3 | 1 | 1 | 2 | 1 | 0.9221 | 0.1723 | |
1 | 1 | 1 | 1/2 | 3 | 1.0845 | 0.2027 | |
1/2 | 1/2 | 2 | 1 | 2 | 1.0000 | 0.1870 | |
1/3 | 1 | 1/3 | 1/2 | 1 | 0.5610 | 0.1048 |
1 | 1/3 | 1/2 | 1 | 1/2 | 0.6084 | 0.1094 | |
3 | 1 | 2 | 3 | 2 | 2.0477 | 0.3682 | |
2 | 1/2 | 1 | 2 | 1 | 1.1487 | 0.2065 | |
1 | 1/3 | 1/2 | 1 | 1/2 | 0.6084 | 0.1094 | |
2 | 1/2 | 1 | 2 | 1 | 1.1487 | 0.2065 |
B5 | ||||||
---|---|---|---|---|---|---|
1 | 1/4 | 1/2 | 1 | 0.5946 | 0.1250 | |
4 | 1 | 2 | 4 | 2.3784 | 0.5000 | |
2 | 1/2 | 1 | 2 | 1.1892 | 0.2500 | |
1 | 1/4 | 1/2 | 1 | 0.5946 | 0.1250 |
From the calculation above, it can be known that the weight of each factor on the safety of gas tunnel construction is
According to the total weight of the
Total ranking of risk factors.
Specific risk factor | Total weight |
---|---|
Safety awareness | 0.1232 |
Safety monitoring system | 0.0984 |
Roof weakness | 0.0841 |
Ideological and political qualities | 0.0616 |
Ventilation system | 0.0606 |
Fire-fighting system | 0.0553 |
Rock bolt quality | 0.0552 |
Thickness of immediate roof | 0.0511 |
Stratum combination | 0.0472 |
Rock mass strength | 0.0435 |
Gas emission | 0.0368 |
Coal outburst characteristics | 0.0368 |
Physical condition and professional competence | 0.0308 |
Personnel arrangement | 0.0308 |
Emergency drilling | 0.0292 |
Tunnel support factor | 0.0292 |
Roof management system | 0.0274 |
Development degree of coal seam cracks | 0.0264 |
Safety education | 0.0223 |
Deep crustal stress concentration | 0.0167 |
Stratum gas content | 0.0144 |
Safety rules and regulations | 0.0101 |
Geological structure | 0.0085 |
The ranking above shows the evaluation results of risk factors of Lugou Mine’s three-soft coal seam outburst roof stability. During the evaluation process, 10 experts were invited to score various risk factors in the Anping Gas Tunnel Construction Safety Project. Statistical methods were used to calculate the final weight value of different factors. Based on the weight value, it can be concluded that safety awareness, safety monitoring system, roof weakness, ideological and political qualities, fire-fighting system, and rock bolt quality dominate the whole project; among them, the riskiest factors are safety awareness and safety monitoring system. In other words, if gas emission cannot be effectively controlled and detected, the safety awareness of workers cannot be improved, and coal mines will have accidents such as poisoning and gas outbursts, which may eventually cause coal mines to stop production. Moreover, roof weakness is also a risk factor, which means that if the tunnel support measures are not done well, roof fall accidents may occur.
After on-site survey by experts, the Anping Gas Tunnel single-factor score comment set is
Relationship between scores and safety grades.
Grade | Very good | Good | Average | Poor | Very bad |
---|---|---|---|---|---|
Score | 95 | 85 | 65 | 45 | 30 |
Relationship between scores and safety grades.
Grade | Grade I | Grade II | Grade III | Grade IV | Grade V |
---|---|---|---|---|---|
Description | Little influence | Small influence | General influence | Large influence | Significant influence |
Score range | >90 | 80–90 | 60–79 | 40–59 | <40 |
Generally, basic methods such as expert survey method, CIM method, and Monte Carlo simulation method are used for risk assessment [
The expert evaluation method was adopted. 10 experts were invited to make judgments on possible risks and their grades, and then they scored according to indicators. The statistics based on the judgment values given by the experts are shown in Table Establish the fuzzy evaluation matrix Obtain the fuzzy evaluation matrix Solve the evaluation matrix According to the formula, The evaluation matrix of each factor is as follows: Obtain the total evaluation matrix Comprehensive factor evaluation matrix: the following can be obtained from the formula Grading: From the above calculations, it can be known from Table Evaluation results: from the fuzzy analytic hierarchy process, it can be concluded that safety awareness, safety monitoring system, roof weakness, ventilation system, fire-fighting system, and rock bolt quality are dominant in the entire project. The safety grade of the entire system is Grade III.
Scoring statistics of weights and expert safety grades.
Primary evaluation factors | Weight of primary evaluation factors | Secondary evaluation factors | Weight of secondary evaluation factors | Very good | Good | Average | Poor | Very bad |
---|---|---|---|---|---|---|---|---|
Geological environment risks | 0.1136 | 0.3243 | 0 | 0.2 | 0.3 | 0.4 | 0.1 | |
0.1495 | 0.1 | 0.4 | 0.2 | 0.1 | 0.2 | |||
0.3243 | 0.1 | 0.2 | 0.2 | 0.3 | 0.2 | |||
0.1271 | 0 | 0.3 | 0.2 | 0.3 | 0.2 | |||
0.0748 | 0.2 | 0.4 | 0.2 | 0.2 | 0 | |||
Safety management risks | 0.1204 | 0.5032 | 0.4 | 0.3 | 0.2 | 0.1 | 0 | |
0.0893 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | |||
0.1856 | 0 | 0.3 | 0.2 | 0.4 | 0.1 | |||
0.2273 | 0.1 | 0.3 | 0.4 | 0.2 | 0 | |||
Roof own risks | 0.2523 | 0.3332 | 0 | 0.2 | 0.3 | 0.3 | 0.2 | |
0.1723 | 0.3 | 0.5 | 0.1 | 0.1 | 0 | |||
0.2027 | 0.1 | 0.3 | 0.4 | 0.1 | 0.1 | |||
0.1870 | 0.3 | 0.4 | 0.2 | 0.1 | 0 | |||
0.1048 | 0.2 | 0.4 | 0.3 | 0.1 | 0 | |||
Safety facility risks | 0.2673 | 0.1094 | 0.6 | 0.2 | 0.2 | 0 | 0 | |
0.3682 | 0.4 | 0.2 | 0.2 | 0 | 0.2 | |||
0.2065 | 0.2 | 0.3 | 0.3 | 0.2 | 0 | |||
0.1094 | 0.2 | 0.4 | 0.2 | 0.2 | 0 | |||
0.2065 | 0.4 | 0.2 | 0.3 | 0.1 | 0 | |||
Construction personnel risks | 0.2464 | 0.1250 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | |
0.5000 | 0 | 0.3 | 0.2 | 0.4 | 0.1 | |||
0.2500 | 0.1 | 0.3 | 0.3 | 0.3 | 0 | |||
0.1250 | 0.3 | 0.2 | 0.2 | 0.3 | 0 |
According to the risk analysis result, in the analysis of Lugou Mine’s three-soft coal seam outburst roof stability, the following five major risks have the highest probability of occurrence, including ventilation risks, safety monitoring risks, roof own risks, safety management risks, and fire-fighting risks, which have become the key control objects for Lugou Mine’s three-soft coal seam outburst roof stability. In order to reduce coal seam risks and ensure the smooth completion of the project, attention should be focused on risk prevention from the following aspects. Corresponding control measures have been formulated. During the extraction period, the air distribution on the working face should not be less than 1500 m3/min. The ventilation system is independent and the return air side is unblocked. The inlet and return air does not pass through the goaf or roof falling area. At the same time, the wind measurement should be strengthened and the personnel should be fixed. If the gas exceeds the limit, the power supply of all nonintrinsically safe electrical equipment in No. 32081 working face as well as the upper and lower suballeys can be disconnected; the power-off is sensitive and reliable. A total of 2 feed sensors are installed on the load side of the controlled switch, which is maintained by a dedicated person every day. The sensors are portable for comparison. The calibration is performed every 15 days with a full-time gas inspector. Set up backup power supply and ventilator: when one power supply stops working or the ventilator fails, the backup power supply or the ventilator can be quickly used to ensure the normal ventilation in tunnels. It is strictly forbidden to allow people to enter the overlimit area. The variable air volume supply is used to control the air intake, and the gas in the overlimit area is discharged gradually. When the gas is discharged, it is necessary to ensure that the gas concentration in the working area does not exceed the limit. Moreover, it should be ensured that the gas discharged from the coal mine does not exceed the limit. The gas inspectors must frequently inspect the gas concentration in the return air flow. When the gas concentration is less than 0.75%, the air supply should be reduced. After the normal ventilation is restored, the electromechanical equipment in the power-off area shall be inspected first. The power supply can be manually restored for normal construction only after the equipment is confirmed to be in good condition. Conduct safety education and training regularly. Safety education training can improve people’s safety production knowledge and enhance their safety awareness, which can effectively prevent from unsafe behaviors. Safety education training must be comprehensive with prominent focus and strong systemicity. Conduct physical examinations on employees regularly. After the fire report is received, the on-site team leader and technical person in charge should first cut off the power supply and actively organize personnel to extinguish the fire. When the on-site personnel are threatened by disasters, they should be organized to evacuate in time in accordance with the gas combustion accident plan. In the process of fire-fighting and disaster relief, the concentration, wind direction, and air volume of various toxic and harmful gases should be detected in time. Safety measures should be taken to prevent gas and coal dust explosion, as well as personnel poisoning timely when the situation is identified. The electrical equipment should be inspected frequently to avoid electrical sparks, thus ensuring the continuous normal operation of the mechanical equipment and avoiding friction sparks.
Based on the engineering background of stability assessments of three-soft coal seam roof outburst coal seams, this paper used the fuzzy analytic hierarchy process combining the analytic hierarchy process and the fuzzy mathematics to conduct risk analysis of Lugou Mine’s No. 32141 working face. It built an analytic hierarchy model. The influencing factors of primary and secondary indicators were determined and ranked by scores. The conclusions are as follows: The analytic hierarchy process is a method combining human subjective judgment and quantitative calculation. It has the characteristics of systematization and hierarchicalization. The quantitative information needed for the analysis is relatively less; the fuzzy mathematics evaluation method combines the fuzzy transformation principle and the maximum membership principle. The comprehensive evaluation of all factors related to the thing to be evaluated focuses on all relevant factors considered. The fuzzy analytic hierarchy process is a combination of the analytic hierarchy process and the fuzzy mathematics method. It has the advantages of qualitative analysis and quantitative evaluation, which is a new method of evaluating coal seam roof. It can obtain a high accuracy of three-soft coal seam roof stability assessments. Based on the characteristics of the coal seams of the Lugou Mine, combined with historical data and after discussions with experts, the risks in each layer are divided into five categories: geological environment risks, safety management risks, roof own risks, safety facility risks, and construction personnel risks. Each category contains several basic risk factors. The analytic hierarchy process can be used to solve the weight of each factor. It can be concluded that safety awareness, safety monitoring system, roof weakness, ventilation system, fire-fighting system, rock bolt quality, and other risk factors dominate the entire project. After the analytic hierarchy process was used to obtain the weight of each risk factor, based on the fuzzy mathematics analysis, it is concluded that the safety grade of Lugou Mine’s three-soft coal seam outburst roof stability is Grade III: “average.” The main risks and harmful factors include ventilation risks, safety monitoring risks, roof own risks, safety management risks, and fire-fighting risks. According to the conclusion drawn from the fuzzy analytic hierarchy process, corresponding measures are proposed for factors with high risks, such as ventilation problems, safety monitoring systems, and safety management, to reduce the probability of accidents and ensure the safe production of coal mines. The fuzzy analytic hierarchy process can also be used to evaluate the stability of deep roof in other similar coal mines to reduce the probability of coal mine accidents.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This study was supported by the Postdoctoral Innovation Project of Hebei Province (no. B2019005005), Key Project of Hubei Provincial Department of Education (no. D20201506), Wuhan Institute of Technology Science Fund (no. K201855), and Guiding Project of Hubei Provincial Department of Education (no. B2020056).