Research and Application of Rock Burst Hazard Assessment of the Working Face Based on the CF-TOPSIS Method

Zhu, Feng; Song, Haowei; Huang, Bingxiang; Hu, Binxin; Li, Siqi; Song, Guangdong; Zhang, Hua; Liu, Tongyu; Ma, Fuwu

doi:https://doi.org/10.1155/2023/5546795

Shock and Vibration

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Abstract Introduction Conclusion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 5546795 | https://doi.org/10.1155/2023/5546795

Research and Application of Rock Burst Hazard Assessment of the Working Face Based on the CF-TOPSIS Method

Feng Zhu,^1,2,3,4Haowei Song ,¹Bingxiang Huang,²Binxin Hu,¹Siqi Li,⁵Guangdong Song,¹Hua Zhang,¹Tongyu Liu,^1,3and Fuwu Ma^4,6

Academic Editor: Pengfei Wang

Received30 Mar 2023

Revised28 May 2023

Accepted06 Jun 2023

Published22 Jun 2023

Abstract

Coal mining activities have intensified, and underground mining depths have increased, posing significant challenges for mitigating rock burst hazard. To address this issue, this paper proposes an improved comprehensive weighting prediction (CF-TOPSIS) method that predicts weight and grade indices for rock burst evaluation. The study introduces a novel variable coefficient method to optimize the conflict response problem and employs correlation forward and other techniques to minimize errors caused by large data differences. Based on these, the CRITIC objective weight algorithm model is established, and FAHP is used to optimize weighting factors to make the index weight of rock burst more reasonable, thus enhancing the accuracy of hazard assessment for working faces. Results from engineering examples show that the prediction results combined with field drill cutting methods and microseismic monitoring data verify the accuracy of the proposed method, indicating its feasibility and effectiveness in predicting rock burst hazard in underground coal mines.

1. Introduction

Coal is a pivotal source of energy in China’s energy portfolio [1]. However, with the gradual exhaustion of shallow coal seams, deep mining has become increasingly prevalent, leading to a higher probability of deep rock bursts [2, 3]. Thus, to ensure the safety of coal mining operations, accurate and effective prediction of rock bursts in deep mining must be based on scientific and rigorous principles [4].

Numerous scholars have conducted extensive research on predicting rock bursts [5]. Their proposed measures can be categorized into mine-wide prediction (intelligent hazard-level judgement), regional prediction (utilizing three spatialtemporal monitoring methods), and local prediction (employing a multiparameter monitoring and early warning index system for rock bursts) [6–10]. Given the complexity of factors contributing to rock bursts, evaluating the level of rock bursts in mines can be challenging. Wu et al. [11] judged rock burst hazard levels by utilizing a rock comprehensive prediction model, which involved determining the hazard index of rock bursts. To predict the static properties of coal and rock mass, Jiang et al. [12] developed a possibility index diagnosis method that considers mining stress and the burst tendency of coal seams as primary indices. Zhu and Zhang [13] introduced the Lagrange function to optimize the decision model and address the issue of determining the weight of rock burst disaster system evaluation models. Han et al. [14] established the division method of geological dynamic zones, incorporating fault structure and coal rock characteristics to enable accurate coal mine prediction. Based on the cloud model and D-S theory, Chen [15] evaluated the hazard of rock burst, while Cai et al. [16] further developed the spatiotemporal forecasting method for rock bursts using multidimensional microseismic information. To address the challenge of predicting dynamic disasters caused by various factors and the heterogeneity of coal and rock in mining operations, Meng et al. [17] studied the general patterns and hazard control factors of rock bursts. Additionally, in regional prediction methods that involve “strong time and space” considerations, several techniques such as the generalized artificial neural network [18], particle swarm optimization KNN, cloud model, and decision tree [19, 20] have yielded promising results in rock burst prediction. However, each prediction method has its limitations. For instance, the convergence speed of intelligent algorithms like generalized neural networks may be slow. Decision tree algorithms can be prone to overfitting and may overlook correlations, while the analytic hierarchy process may exhibit strong subjectivity. Similarly, the conventional objective weighting method may fail to account for interindicator correlations [21–25].

Given that rock bursts can be affected by numerous factors [26, 27], this study optimizes the correlation and conflict of CRITIC and constructs the CF comprehensive weight evaluation index through a combination of the FAHP’s subjective weight approach [28]. The improved TOPSIS rock burst closeness evaluation and prediction model yields more accurate and reasonable predictions of rock burst grade. The accuracy of these predictions is verified through a practical example, highlighting the effectiveness of the proposed approach.

2. Comprehensive Weighted Prediction Model

Rock bursts occur due to the influence of multiple factors, and the rock burst of each factor on rock bursts can vary. As such, determining a reasonable comprehensive evaluation model for rock bursts and assessing the hazard level of the working face are of paramount importance.

2.1. Improved Objective Weighting Method

The entropy weight method (EWM) is an important means of reducing subjective factors in the evaluation process. However, EWM has limitations in reflecting the interrelationship between criteria, which may affect the accuracy of the evaluation results. To address this issue, a new approach called criteria importance through intercriteria correlation (CRITIC) was proposed by Diakoulaki in 1995. CRITIC serves to optimize the weighting procedure and improve the objectivity of the evaluation process by taking into account the interrelationship between criteria [29, 30]:(1)The index data matrix is established, and indicator types are unified. where is the original data of the influencing factors of rock burst and is the positive value of the index.(2)Standardized processing of forwarded matrices is(3)The correlation coefficient between the influencing factors is obtained by the processed matrix. There is a connection between the influencing factors, and the deviation product can well measure the degree of correlation between the two variables: indicates that is the correlation coefficient between the ith indicator and the jth indicator, and the value of is between (0, 1). The greater the value of , the greater the correlation between the two factors.(4)The information amount of the comprehensive measurement index is as follows: Information measure is defined based on contrast strength and conflict concepts: where represents the definition of information measure, represents variance, and represents the correlation coefficient. In the present study, an optimized criteria importance through intercriteria correlation (CRITIC) approach is proposed to enhance the objectivity of weight determination. While Zhang and Xiao [31] proposed the use of CRITIC, this method has not been applied in the field of rock burst hazard assessment. The significant differences in influencing factors of rock burst pressure are addressed by optimizing the CRITIC approach in order to account for these differences. Due to the large variations in rock burst pressure influencing factors, the value of the consistency coefficient () may become too large, leading to errors in the determination of weight factors. The coefficient of the variation method is utilized to enhance the CRITIC approach and address errors stemming from excessively large values. Furthermore, the method is optimized to accommodate situations where notable variations in indicator values pose challenges in implementing the coefficient of the variation approach: where is the number of index values and is the variance. In Zhao et al. [32] evaluation of power quality classification, the value reflects the conflict between different evaluation indicators. However, when the relationship between indicators is negative, weighting may become disproportionately large. To address this issue and account for the conflicts among coefficient values, a positive conflict transformation method is introduced to optimize the weight determination process. By applying this approach, the issue of disproportionately large weights resulting from negative correlations between indicators is effectively mitigated: where is the introduced coefficient of variation and is the correlation coefficient.(5)Calculation of the index weight of the information measure index is where represents the weight value of the index j.

2.2. Determining Subjective Weight by FAHP

The traditional AHP method fails to maintain consistency in thinking when facing multiple evaluation indicators. It is difficult to test the consistency of the judgment matrix, which fails to provide a strong scientific basis. In order to further optimize the reliability of the subjective weight, the author uses the fuzzy analytical hierarchy process (FAHP) to establish a multiobjective, multilevel structure of the subjective weight of the rock burst subjective decision model [33].

A number of experts with rich work experience compare and judge the two factors of rock burst, construct the judgment matrix , and calculate the average value of each obtained by many experts to obtain . The subjective weight is obtained by summing the fuzzy judgment matrix A by rows and exchanging them mathematically:

The subjective factor weight calculation equation is as follows:

2.3. CF-TOPSIS Regional Static Prediction before Mining

The technique for order preference by similarity to ideal solution (TOPSIS) can better describe the strength of multifactor comprehensive influence of rock burst without the objective function and test [34]. TOPSIS avoids errors caused by subjective factors in data and is often used for multiple indicators and evaluation units [35]. In this paper, through the optimized CF combination weight, according to the distance between the positive and negative ideal solutions, the rock burst pressure level and the working face to be measured are sorted to realize the prediction of the rock burst hazard level of the working face before rock burst pressure mining.

The optimized objective weight and the subjective weight are combined to obtain the CF comprehensive weight:where is the comprehensive index weight coefficient and and represent the values of the objective weight and subjective weight after optimization, respectively.

2.3.1. Original Judgment Matrix

The rock burst level scheme set is set as , and the index set to be evaluated is . The binary comparison decision matrix, as presented in equation (10), serves as a means of identifying the internal correlated significance of factors [36]. The evaluation index represents the jth evaluation index of the ith scheme.

The initial evaluation matrix is as follows:

2.3.2. Standardized Decision Matrix

The evaluation index can be divided into the consumption index and profitability index. For the consumption index, the smaller the value, the better. For the profitability index, the larger the value, the better. Because each evaluation index has different dimensions and units, it has no comparability, so is standardized to get the decision matrix :where is the profitability index and is the consumption index.

2.3.3. Weighted Standardized Decision Matrix

The weighted standardized decision matrix E is obtained by multiplying the column vector of matrix B with the total ranking weight of the determined comprehensive weight index layer:

2.3.4. Close Degree Analysis

The positive ideal solution of the profitability index set is the maximum value of the row vector, the negative ideal solution is the minimum value of the row vector, and the value of the consumption index set is the opposite:where and are the positive ideal solution and the negative ideal solution, respectively. In order to better optimize the distance between the evaluation object and the ideal solution, the influence of each influencing factor on the rock burst of low pressure is different. The greater the weight, the greater the possibility of rock burst.

Zhu and Zhang [13] incorporated the weighting coefficients into the positive and negative ideal solutions to develop the CF-TOPSIS model. Through this approach, they were able to optimize the rock burst of the weight coefficients on the evaluation of the rock burst of ground pressure while also conducting proximity analysis. The deviation equation resulting from the weighting coefficients and the Euclidean distance of the ideal solutions were also examined:where and are the element corresponding to the positive and negative ideal solutions and and represent the distance between the positive and negative ideal solutions.

The calculation equation of closeness analysis is as follows:where is the value of the closeness degree of the evaluation object, and the range is (0, 1), which reflects the degree of closeness of the evaluation object to the positive ideal solution.

The evaluation matrix is constructed by the close degree analysis of the TOPSIS method. The comprehensive evaluation result vector Q of the CF-TOPSIS model is calculated as follows:where Q is the comprehensive evaluation result vector, H is the evaluation matrix formed by the closeness degree between each evaluation object and the positive ideal solution, and is the criterion layer weight calculated by the comprehensive weight method. The CF-TOPSIS comprehensive evaluation model is shown in Figure 1.

3. Comprehensive Prediction of Rock Burst Hazard in Working Face

3.1. General Situation of Working Face

Taking the 8003 working face of a coal mine in Shanxi as an example, the coal seam thickness ranges from 94 to 104 meters and contains multiple interbedded gangues, which are extracted through layered mining. The ground elevation of the 8003 working face ranges from +925 to +945 meters, with an average coal seam thickness of 5.67 meters and a dip angle of −2° to −5° along the roadway. Within the range of 440 meters and 554 meters ahead of the return airway and roadway of 8003 comprehensive caving, there exist SF14 and SF15 normal faults. The geological conditions of the 8005 working face are similar to those of the 8003 working face, and rock burst and frequent mine tremors occurred during mining.

According to the indicators in Table 1, the FAHP criteria layer can be divided into the following levels First, the stability level of the goaf support layer, which includes indicators such as the distance between the hard and thick rock layers and the coal seam inside the overlying fracture zone, mining depth, and thickness of the remaining coal seam, is used to evaluate the stability of the goaf support layer. Second, the stability level of the working face, which includes indicators such as the historical number of occurrences of the same level of coal seam rock bursts, the ratio between the stress increment of the structure and the normal stress %, the uniaxial compressive strength/MPa, and the elasticity index are used to evaluate the stability of the working face. Third, the level of mining technology, which includes indicators such as the working face length, the width of the segment coal pillar, and the relationship between the working face and the adjacent goaf, is used to evaluate the rationality, efficiency, and safety of mining technology. Therefore, the basic parameters of the 8003 working face are entered in Table 1, and the CF-TOPSIS evaluation model is employed to predict its rock burst hazard.

3.2. Comprehensive Weighting Prediction and Rock Burst Hazard Assessment

Table 1 is imported into the improved CRITIC objective weighting model and then substituted into equation (7) together with the subjective weight obtained by FAHP, which yields the ideal evaluation index for rock burst factor assessment, as presented in Table 2.

Comparison of the weights in Table 1 reveals differences in the degree of influence of different rock burst criteria on rock burst events. Specifically, the relationship between the primary and secondary factors affecting rock burst is A3 > A2 > A4 > A1. This analysis is of significant value in guiding the optimal selection of rock burst criteria.

Equations (1)–(4) are utilized for the creation of a line chart showcasing index weights for the unoptimized CRITIC model. In order to optimize said model, the integration of equations (5) and (8) is carried out to obtain the optimal CRITIC objective weight. Once the optimized objective weight and the FAHP subjective weight are substituted into equation (12), the weight curve is derived and compared with real-life conditions. Figure 2 is obtained by comparing the outcomes of the unoptimized and optimized CRITIC models with the optimized CRITIC + FAHP model.

Through comparison of weight values before and after optimization, as illustrated in Figure 2, slight increases in the weight values of indicators A2, A4, and A5 are observed after FAHP optimization, while the weight of indicator A3 decreases slightly. The CF weight evaluation model enables a more reasonable weight index to be applied that aligns with actual site conditions, surmounting limitations encountered by conventional models that exhibit significant differences. Verification of the optimized results through field analysis confirms their alignment with staff subjective evaluations.

In the comprehensive index method, rock burst events are categorized into four grades: no, weak, medium, and strong. Weak, medium, and strong critical values of rock burst correspond to S1, S2, and S3, respectively, with the value of S representing the working surface value to be measured. When S < S1, there is no rock burst hazard, and when S1 ≤ S < S2, it indicates weak rock burst hazard. For values where S2 ≤ S < S3, it represents medium rock burst hazard, and for values where S3 ≤ S, it represents strong rock burst hazard.

The CRITIC method is conducted without the inclusion of the coefficient of the variation method and the conflict forward optimization method. Furthermore, the total ranking of the TOPSIS hierarchy is not considered, and weight and equation (17) with are not added. The obtained TOPSIS model is unoptimized, as illustrated in Figure 3(a). Subsequently, a comprehensive evaluation model is established by combining FAHP with the unoptimized TOPSIS model, as depicted in Figure 3(b). Ultimately, the comprehensive evaluation results for the CF-TOPSIS model are presented in Figure 3(c).

(a)

(b)

(c)

Figure 3(a) shows that the comprehensive evaluation index of traditional CRITIC and traditional TOPSIS methods for rock burst is = (0, 0.3933, 1, and 0.5807) and that the evaluation grade of the rock burst index is medium. In contrast, Figure 3(b) shows that, with the combination of FAHP and unoptimized TOPSIS, the evaluation index of rock burst is weak and = (0, 0.4594, 1, and 0.2056). Both models exist, and when S < S1, it is difficult to accurately predict the hazard of rock burst in the working face to be measured, since the index cannot be negative. Moreover, only when S = 1, a strong level of rock burst can be determined. Therefore, the critical value interval of traditional models has certain limitations.

The CF-TOPSIS evaluation model produces an evaluation index of = (0.0569, 0.3638, 0.8360, and 0.5352), which is medium in 8003 coal mine conditions. When no rock burst danger exists, the result of the working face prediction indicates no rock burst hazards when . Conversely, when , a strong rock burst hazard is predicted, as shown in Figure 3(c). This method is only suitable for research at coal mine experimental sites, and there may be some special conditions or factors at other sites that may not be applicable to other sites. Therefore, this method cannot simply be applied to other sites and needs to be adapted and improved according to the specific situation.

3.3. Prediction of Rock Burst under Microseismic Monitoring Technology

The process of microseismic event evolution, from a dispersed pattern in space to self-organized concentration, is a distinct characteristic of rock burst precursors. The energy level of a single event per day is classified as moderate hazard, ranging between – J, with the potential for a strong rock burst hazard exceeding J.

Based on the microseismic monitoring data obtained from the 8003 working face, as illustrated in Figures 4 and 5, the majority of microseismic events recorded near the working face possessed an energy range of – J, which accounted for 88% of the total events within the considered time period. The weaker events with energy levels below J comprised 11.75% of the total events observed. There was only one instance of a microseism event with an energy level exceeding J, which occurred during directional blasting for decompression in the roadway at the site. Consequently, based on a comprehensive assessment, the rock burst hazard level for the 8003 working face can be classified as medium.

3.4. Characteristic Analysis of Large Energy Signals

The coal mine in Shanxi, China, has a mining depth ranging from +925 to +945 meters. As multistage large-scale mining methods are employed and mining depth deepens, safety hazards such as rock burst become increasingly prominent. To ensure mine safety, an independently developed microseismic monitoring system with a sensor sampling frequency of 2000 Hz is installed in the mine. During the safety monitoring process, significant energy microseismic events are detected. Through eliminating irrelevant interference signals, the collected on-site microseismic signals, coupled with large energy vibration signals, are analyzed as depicted in Figures 6 and 7.

It is evident from the decomposition results presented in Figure 8 that the frequency of the microseismic signal persists in the time domain. Additionally, the time-frequency diagram illustrated in Figure 9 reveals that the large energy signal displays traits such as short time duration, discontinuity, and high frequency. These attributes are indicative of a typical blasting signal. It is noteworthy to remark that the findings presented in this study provide valuable insights into the characteristics of microseismic signals, which can be leveraged for optimizing blasting processes and enhancing safety measures in mining operations. From the perspective of a trigger mechanism, microseismic events are the release of rock accumulated energy, and the process is relatively slow, mostly in the form of shear failure [37–39]. The blasting signal mainly produces longitudinal waves, with strong energy and fast attenuation, which is a typical rock burst response [40–43]. Therefore, it is preliminarily judged that the large energy signal is a blasting signal. Through subsequent field verification, it is further confirmed that the large energy signal is a blasting signal [44].

3.5. Drill Cutting Method

The current study focuses on the solid coal side of the 8003 working face and aims to determine and analyze the size of stress and the degree of rock burst danger. To achieve this, the pressure relief blasting method was utilized to measure the amount of pulverized coal (in kilograms) in the borehole. Additionally, the amount of drill cutting was measured at intervals of one meter, and the drill cutting absorption index was measured at intervals of two meters. If the measured amount of pulverized coal at the test site exceeds the critical value or the pressure of (drill cutting absorption index) exceeds 200 Pa, it indicates an increased hazard of rock burst occurrence. The actual and critical values of the cuttings absorption index are depicted in Figure 10.

The value serves as a comprehensive indicator of the coal damage degree. To obtain this value, it is necessary to extract drill cuttings at predetermined locations and subsequently sieve them with apertures of 1 mm and 3 mm before transferring the resultant particles to the coal sample bottle. After waiting for 3 minutes from the time of particle generation to sampling, the coal sample bottle is started. The absorption reading, recorded 2 minutes thereafter, equates to the value. The 8003 working face is subjected to measurement of an average of once every three days. Based on the August data analysis, 84% of the drill cuttings’ absorption index was lower than the critical value of 200 MPa, while 16% of the drill cutting absorption index (200 MPa on August 9th and 220 MPa on August 22nd) reached the critical absorption index value, necessitating further attention and assessment. Subsequent testing revealed that the actual drill cuttings did not achieve peak values on August 9th and August 22nd. As such, it was concluded that there was no imminent hazard of strong rock bursts. Overall, the 8003 working face poses a rock burst hazard.

4. Conclusion

(1)The present study proposes the CF-TOPSIS model for predicting rock burst hazard. To avoid any bias resulting from a single model, FAHP and CRITIC weight models are employed to determine the weight of rock burst factors. By incorporating the TOPSIS prediction model, the CF-TOPSIS prediction model for rock burst is obtained.(2)The optimization of the traditional model is required to address its inadequacy in assessing the nonrock burst hazard phenomenon under rock burst conditions. Additionally, there is a need for enhanced accuracy in predicting the rock burst hazard associated with the working face being measured. Furthermore, it is important to compare the moderate rock burst hazard with the traditional model.(3)The combination weighting method was utilized to obtain the total ranking of the CF evaluation index, revealing that distinct rock burst criteria exert varying degrees of influence on rock burst. The primary and secondary factors influencing the rock burst of the 8003 working face were determined as follows: the distance between the hard and thick strata in the overlying fractured zone and the coal seam (A3) possesses the greatest impact, followed by mining depth (A2), the ratio of stress increment to normal stress in the mining area (A4), and the number of rock bursts in the same horizontal coal seam (A1).(4)This study examines the factors influencing rock burst from multiple perspectives, optimizes the traditional critical value interval, and proposes an effective prediction method for rock burst. Field practice is used to verify the feasibility and accuracy of this method, offering a novel approach to rock burst prediction.

Data Availability

All data generated or analyzed during this study are included within this article.

Disclosure

Feng Zhu and Haowei Song are the co-first authors.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Feng Zhu and Haowei Song contributed equally to this work.

Acknowledgments

This work was partly supported by the Jining Key Research and Development Program Project (Grant no. 2021AQGX001), the Shandong Province Natural Science Foundation Key Project (Grant no. ZR2020KC012), the Shandong Province Deep Rock Bust Disaster Assessment Engineering Laboratory Open Project (Grant No. LMYK-2021-010), and the Jinan City “New 20 High Schools” Funding Project (Grant no. 2021GXRC037).

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Copyright © 2023 Feng Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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