Fuzzy fault tree analysis for coal burst occurrence probability in underground coal mining

Fault coal bursts is a common type of coal bursts in coal mining. With increases in mining depths and mining intensities, the occurrence frequency and disaster degree of coal bursts in fault‐controlled areas are increasing in severity. Therefore, in this article, the mechanism and control method of deep mining‐induced fault coal bursts are analysed on the No. 3402 longwall panel controlled by two faults in the Shandong Mine, China. Based on the microseismic monitoring data of the longwall panel, the fault activation process induced by mining can be divided into four stages. The second energy release and fault activation stages are risk stages of fault coal bursts. Using numerical simulation, the evolution laws of vertical stress, elastic strain energy in front of the longwall panel, and the ratio of shear stress to normal stress on fault plane are investigated. The phenomenon of a ‘high elastic energy section‐low elastic energy section’ coal pillar between faults is revealed, and the mechanism of coal pillar coal bursts induced by high elastic energy is discussed. Third, based on the above research results, coal burts risk areas of the longwall panel in the fault‐controlled area are determined. The method of ‘coal seam‐roof‐floor coupling destressing with the reduction in mining speed’ to control fault coal bursts is introduced. Field test results show that the method effectively destroys the integrity and bearing capacity of the coal seam, significantly reduces the energy storage capacity of the superposition area of mining stress and fault tectonic stress, so that the coal bursts risk of the coal seam in the fault control area is relieved, ensuring safe and efficient mining of the longwall panel. The method can provide a reference for other coal mines with similar geological conditions.

A new empirical distribution for the uncertainty analysis of the top event of a fault tree

ABSTRACT: Coal bursts are challenging to predict due to their highly complex and dynamic nature. In recent years, efforts to integrate established indices, such as Burst Potential Index (BPI), Energy Storage Rate (ESR), and Strain Energy Density (SED), with numerical computation modeling have shown potential. This study aims to develop a novel burst risk assessment tool for individual coal pillars in a longwall gate-road system (LGS). A modified pillar-specific BPI (mBPI) is proposed and couples analytical indices with numerical modeling. The modified index expands the classic BPI by removing the limitations stemming from considering only the elastic region of the material constitutive model and the stress-strain behavior under uniaxial loading. The effect of various geometrical and geomechanical parameters on the mBPI is evaluated through a parametric analysis. A total of 47 numerical models were generated to calculate the mBPI of an LGS pillar for different conditions. The results indicate that coal strength and coal stiffness, in-situ horizontal stresses, overburden depth, and pillar width have a substantial effect on the mBPI, while the width of the LGS yield pillar has a minimal impact on the mBPI. 1. INTRODUCTION Coal bursts remain a longstanding, disrupting phenomenon in underground coal mines that can lead to high safety risks and have the potential to cause fatal injuries. Coal bursts are described by Zhang et al. (2017) as "dynamic rock failure involving a sudden release of strain energy stored within the rock mass". Since 1872, there have been more than 1,200 reported cases worldwide (i.e., in the United States, Germany, Czech Republic, Russia, China, and India), causing more than 1,000 fatalities (Sabapathy et al., 2019). It is evident that coal bursts present a substantial hazard in deep-mining coal operations, as they can cause serious damage to equipment and facilities, as well as loss of life. Despite the extensive database of coal burst incidents available, a comprehensive scientific understanding of this phenomenon still eludes the international mining community. The causes and failure mechanisms of coal bursts constitute an extremely complex and dynamic engineering problem. Geotechnical engineering revolves around rock mass behavior, which cannot be fully described mathematically because of the multiple, and/or potentially impossible-to-determine factors involved. The discontinuous nature of the rock masses, the inherently heterogeneous and anisotropic engineering properties, the presence of bedding planes, faults, joints, folds, micro-fractures, the varying field stress conditions and pore pressure conditions, and other conditions allow only for approximate estimations of the rock mass behavior.

Effect of faulting on coal burst – A numerical modelling study

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Investigation of sudden faults instability induced by coal mining

With the coal mines in western China entering the field of deep mining, the problem of coal burst is becoming more and more serious. According to the characteristics of deep mining, it is an urgent problem that requires the development of an efficient and reasonable coal burst prevention and control plan to guide project practices. This study takes the typical deep mining area in Ordos as the research background, according to the stress state of the coal mining area and the load form of induced coal burst, which, in Ordos deep mining, is divided into the typical and atypical type. The former is caused by the superposition of high in situ stress and strong mining-induced stress, while the latter is due to the combination of high in situ stress, strong mining-induced stress, and external stress disturbances. Combined with theoretical analysis, numerical simulation, and field measurement, it is shown that the stress level of the Ordos deep mining area is higher than that of the shallow original rock, and the difference of the three-dimensional stress between coal and rock mass is greater. The concentration degree and influence range of mining-induced stress obviously increase. Coal and rock mass are more prone to instability and failure due to external disturbances. Based on the stress control theory, the prevention and control strategies of coal burst in different types of deep mining are put forward. In addition, the prevention and control technology system of coal burst in the Ordos deep mining area is established. The field engineering practice has been carried out to realize the efficient prevention and control of coal burst.

A quantitative pre-warning for coal burst hazardous zones in a deep coal mine based on the spatio-temporal forecast of microseismic events

There are many available techniques which are widely used for failure probability analysis. Fault tree analysis (FTA) is a well-known method to identify the basic events (BEs) to reach top event. However, the FTA method in real circumstances is limited because of the many unknown and the vagueness of the situations. Thus, fuzzy set theory with respect to subjective expert opinion is employed to cope with the uncertain knowledge of BEs including randomness, ignorance, and shortages of data. In addition, to gain this purpose, much subjectivity may appear; as an example, the main one is the expert weighting. This study highlights the utility of fuzzy set theory and analytic hierarchy process to failure probability analysis in a case study. A chemical process plant has been selected to illustrate the application of proposed model with a comparison of the results with conventional model.

Fault tree analysis is regarded as a powerful technique for assessing system reliability. The conventional approach of solving fault trees is based on applying probability theory by assigning probability distributions to basic events and propagating the probabilities to calculate the overall probability of the top event. However, applying this technique, especially in construction projects, entails several difficulties attributed to the lack of sufficient data to estimate the probability distributions for basic events. Thus, risk analysts tend to assume probability distributions of basic events, which adds further uncertainty to the analysis. To overcome these difficulties, this paper presents a simplified approach in which basic events are represented by a possibility distribution, and the possibility distribution of the top event probability is calculated by applying fuzzy arithmetic operations. The method presented herein is demonstrated using a case study to quantify the probability of failure of horizontal directional drilling (HDD). The proposed approach is able to overcome the difficulties of using the conventional approach by relying on linguistic terms rather than assuming probability distributions for basic events.

Fuzzy probability on reliability study of nuclear power plant probabilistic safety assessment: A review

Uncertainty in fault tree analysis: A fuzzy approach

Experimental study of coal burst risk prediction using fractal dimension analysis of AE spatial distribution

The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions.