Roof Fall Research Articles

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.

Evaluating the Geo-Environmental Conditions within a Working Face Using a Hybrid Intelligent Optimization Model

Geological anomalies within the working face likely induce geological disasters, such as water, gas, and coal mine roof fall, directly impacting the rational planning and safe mining of underground resources. Constrained by the conditions of underground closed spaces, effective reconstruction under incomplete and highly sparse projection is the central challenge when evaluating geo-environmental conditions. This work proposes a new hybrid intelligent optimization model (MPGA-SIRT) that integrates a multiple-population genetic algorithm (MPGA) with the simultaneous iterative reconstruction technique (SIRT) to finely reconstruct the geo-environmental conditions within a working face based on electromagnetic wave tomography theory. MPGA-SIRT can provide a more precise initial inversion model for the conventional linear reconstruction technique of SIRT, incorporating a local search property by leveraging the robust global search capacity of MPGA. The advantages of MPGA-SIRT have been demonstrated through numerical modeling, theoretical testing, and engineering practices on the 8208 working face in the Datong mining area, Shanxi Province. In comparison to individual SIRT inversion models, MPGA-SIRT reconstruction yields more accurate and stable performance, as demonstrated by the evolution curve of the objective function and the corresponding convergence tomography results. Consequently, the geomagnetic wave absorption coefficient within the area of reconstruction can be precisely ascertained through the use of our proposed technique. This innovation represents a groundbreaking strategy for assessing geological anomaly zones within a working face. The introduced method stands as a valuable theoretical instrument for confronting the complexities associated with geo-environmental reconstruction in underground engineering.

Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review

The deep integration of computer field and coal mining field is the only way to coal mine intellectualization. A variety of artificial intelligence tools have been applied in open-pit and shallow coal mines. However, with the geometric increase of coal demand, the contradiction between supply and demand is becoming more and more serious, and the exploitation of resources from shallow layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, the harsh conditions of “three high and one disturbance” seriously threaten the safety of personnel. The superposition of complex mining environment makes the number of input factors increase sharply, which leads to the application of artificial intelligence in deep coal mine roadway engineering. The guidance is not mature, the construction of various databases is missing, and there are still some problems in universality and applicability. To this end, this paper starts with the introduction of the operating characteristics of various artificial intelligence tools, and conducts a comprehensive study of the relevant high-level articles published in top journals. It systematically sorts out the research progress that has successfully solved the five directions of rock mechanics strength, surrounding rock stability, rock-burst, roof fall risks and micro-seismic events. While objectively evaluating the comprehensive performance of different tools, it also expounds its own views on key research and results. Literature review shows that whether as a development tool or a comparative model, the application of ANN in the field of coal mining is more than 98%, and it performs extremely well in the direction of surrounding rock stability and roof fall risk, with an accuracy rate of more than 90%. As the most mature direction of AI tools application, rock mechanical strength has experienced the development process from “SVM → ANN → DL → XGBoost → RF”. The dataset is from small samples (< 100) to big samples (> 1000), and the R2 of tree-based models can be stabilized at more than 95%. The research on rock-burst prediction mainly focuses on field factors and micro-seismic monitoring data. Whether it is a small sample or a large-scale data model, the accuracy of BN remains above 85%. The prediction and evaluation of micro-seismic events is a new direction in recent years. The image processing and application of CNN is extremely important. The signal recognition and classification accounts for more than 90%, and the research potential of source location needs to be further explored. In general, the nature of the rock itself is the first choice for almost all influencing factors. At the same time, the update iteration of monitoring methods (micro-seismic, ground sound, separation, deformation, etc.) expands the development of the database, making it possible to obtain the data due to threat to life and cost of equipment, which is very difficult to obtain before. In the process of parameter selection at the input end, the method of combining lithology conditions, geological environment and monitoring data will gradually become the first choice for research. Finally, in the follow-up work collation and on-the-spot investigation, it mainly focuses on the existing problems of deep coal mines, explores the application potential of artificial intelligence in deep coal mine roadway engineering, puts forward the possible research focus and challenging problems in the future, and gives its own opinions.

Application of Research on Risk Assessment of Roadway Roof Falls Based on Combined Weight Matter Element Extension Model

Roof falls in coal mine roadways are the main causes of many casualties, shutdowns and production plan delays. To understand the relationship between the influencing factors of roadway roof fall accidents and the importance ranking of the accidents, we will reduce safety accidents in coal mines. To enable the timely prediction and control of roadway roof fall risks, based on the investigation of many roadway roof fall risk factors, 12 evaluation indexes such as the roadway roof rock thickness, geological conditions and roadway section shape were selected. An evaluation index system of roadway roof fall risks is constructed. A risk degree standard of roadway roof falls is proposed. The risk evaluation model of roadway roof falls was established by using the combination weight of the analytic hierarchy process (AHP), entropy weight method (EW) and matter element extension theory. According to the principle of the maximum membership degree, the risk degree of roadway roof falls is determined. Based on Java Web, a risk assessment system for roadway roof falls was developed. We name the system Multiple Weight-Material Element Web (MW-MEW). The MW-MEW system was used to evaluate the risk degree of roof falls in the C9 return airway of the Xingu Coal Mine. Compared with the evaluation results of the AHP matter element extension model, it is found that the evaluation results of the MW-MEW system are more in line with the actual engineering conditions. The successful application of the MW-MEW system will provide new avenues for the quantitative evaluation of roof fall risks in coal mine roadways.

The Road to Zero: The 50-Year Effort to Eliminate Roof Fall Fatalities from US Underground Coal Mines

The Road to Zero: The 50-Year Effort to Eliminate Roof Fall Fatalities from US Underground Coal Mines

An Analysis of the Impact of Mining Excavation Velocity on the Development of Gaseous and Gaseous Geodynamic Hazards in Copper Ore Mines

The hazards of gaseous geodynamic phenomena and rockbursts are among the most challenging to assess and classify. This perception arises from both a review of the literature and an examination of available instructions and regulations in underground mining facilities. The hazard of gaseous geodynamic phenomena in Polish copper ore mines only appeared in 2009, whereas these phenomena occur and are commonly described in other mining countries. In Polish copper ore mines, due to the room and pillar system in fields with lengths of about 460 m, very often parallel to neighboring fields, which together give a length of about 900 m, it is difficult to identify the location of gas traps due to the large size of the area. This paper presents an analysis of the influence of the velocity of the excavation on the possibility of escalating or reducing the described mining hazards. An analysis of the impact of excavation velocity on the state of gaseous geodynamic and roof fall hazards was conducted for two mining fields. For the considered mining fields, the hypothesis was formulated that an excavation velocity greater than or equal to 17 m/month positively influences a reduction in both gaseous geodynamic and roof fall hazards.

Application of Ranking Methods for Evaluation of Critical Factors Involved in Roof Fall Fatal Accidents in Indian Underground Coal Mines

Application of Ranking Methods for Evaluation of Critical Factors Involved in Roof Fall Fatal Accidents in Indian Underground Coal Mines

Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error , Mean-Absolute-Error, and coefficient of determination (R_2). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

Modelling the probability of roof fall using digital tools

Information on rock mass characteristics, encompassing the overall quality of the rock masses and various geological parameters, is crucial in mining operations. This data guides the selection of mining techniques and the design of roof control methods to minimize the risk of flaking. However, the lithology of roof rocks is often so variable that predicting their behaviour, especially when disturbed by mining activities, is challenging. The study begins with a detailed review of existing literature on roof fall mechanisms and predictive modelling techniques. It was described the dataset used, which includes geological, environmental, and operational parameters collected from various mining sites. This article outlines a methodology for conducting detailed studies of the lithology of coal seam roof rocks in roadways and longwalls as mining progresses. According to the research, forecasting the extent of roof rock fall during the planning stage of mining operations can inform the implementation of appropriate countermeasures to minimize this phenomenon before exploitation begins, thereby significantly reducing ongoing mining costs. Block modelling and stratigraphic (mesh) modelling were employed to predict the volume of rockfall that could contaminate the ore in the analysed mining plot. In the future, this proposed methodology could be used to analyse the progression of roof falls, estimate the mass of falling roof rocks, and calibrate a system for continuous measurement of ore quality in mine workings.

Modern technical and IT solutions in the System of Quality Control of the Run of Mine at LW “Bogdanka” S.A. coal mine

In underground coal mines, one of the primary sources of waste rock in the ore is the phenomenon of roof fall. This phenomenon increases the contamination of the ROM (Run of Mine) with waste rock, which in turn increases the operating costs of the mine. Exploitation of thinner and thinner seams, the use of plough technology, the increasing speed of mining – all these factors related to the mining technology used significantly affect the cleanliness of the coal seam exploitation. More accurate identification of the causes of increased spoil contamination by waste rock and forecasting its level at the stage of mining planning can allow the application of appropriate countermeasures, often even before the start of mining. The authors believe that monitoring the amount and sources of waste rock in longwalls and headings can provide a basis for developing a method for forecasting the mining plant’s yield. The article presents methods of modeling roof rock fall, which are the result of the work of the Team of the Division of Mineral Resources Acquisition of the Polish Academy of Sciences and LW “Bogdanka” S.A. In the future the proposed methodology can be used to analyze the course of roof rock fall, calculate the mass of roof rock fall, and calibrate a continuous system for measuring the quality of ROM in mine excavations.

Study on the Stress Evolution and Strengthening Support Timing of the Retracement Channel under the Super-Thick Nappe

The superposition effect of the advanced support pressure of the working face in the final mining stage and the lateral support stress of the roadway is a key factor affecting the stability of the retracement channel. To study the stress evolution of the retracement channel under the super-thick nappe and the timing of strengthening support, this paper takes the mining of the 360808 working face in Xinji No. 1 Mine as the engineering background, analyzes the occurrence conditions of the working face and the measured rock pressure law, and constructs a roof structure model of the retreat area. The UDEC discrete element numerical simulation software was used to analyze the evolution characteristics of concentrated stress and the failure law of surrounding rock around the retracement channel under gradual excavation conditions. Based on the relationship between the position of the main roof fracture and the stability of the surrounding rock of the retracement channel, the instability mechanism of the surrounding rock of the retracement channel was revealed. A mechanical model of the surrounding rock of the retracement channel under the condition of a gradient coal pillar was established, and the energy criterion K for the instability of the surrounding rock was obtained. The method of adding anchor cables to strengthen the support of the surrounding rock of the retracement channel was proposed. The results indicate that the accumulation of energy in the surrounding rock of the retracement channel is greater than the internal consumption of energy, which is the direct reason for the instability of the surrounding rock of the retracement channel. The time to strengthen the support of the roof is when the working face is 15 m away from the retracement channel. According to the analysis of on-site monitoring results, the roof convergence and the two-sides convergence before and after strengthening the support were reduced by 90 mm and 140 mm, respectively. Under the strengthening of support, the slope of the retracement channel in the 360808 working face is slight, without roof fall, and the surrounding rock of the channel is effectively controlled, which is of great significance for ensuring the safe application of the retracement channel. It has reference value for the safety production of surrounding mines and is conducive to promoting the sustainable development of local resource-based society and economy.

Characteristics of Energy Dissipation in T-Shaped Fractured Rocks under Different Loading Rates

T-shaped fractured rocks in the engineering rock mass with different inclination angles, quantities, and cross patterns will cause slope landslides, cavern collapse, roof fall, and other disasters under the action of external forces. Deformation evolution of the T-shaped fractured rock is also significant for monitoring the stability of rock engineering structures. In this paper, the compression test of T-shaped fracture specimens was carried out under different loading rates. By modulating both the fracture inclination angle and the loading rate, the attributes pertaining to energy dissipation in the T-shaped fractured specimen were scrupulously scrutinized and subsequently expounded upon. The difference in the energy characteristics between fractured rock and intact rock was investigated to understand the deformation evolution of T-shaped fractured rock samples. The results show that when the fracture angle is 45° and 90°, the elastic strain energy and dissipated energy decrease as the secondary fracture angle increases. At the peak point, as the secondary fracture angle increases from 0°, the total absorbed energy, elastic strain energy, and dissipated energy of the T-shaped fractured rock increase, the ratio Ue/U of elastic strain energy to total energy increases, and the ratio Ud/U of dissipated energy to total energy decreases. The increase in loading rate leads to an increase in Ue/U and a decrease in Ud/U at the peak point of the T-shaped fractured rock specimen. The increase in loading rate leads to an increase in the total absorbed energy and elastic energy at the peak point of the T-shaped fractured rock, while the dissipated energy decreases. Investigative endeavors into the mechanics and energetic attributes of T-shaped fractured rocks bestow pragmatic and directive significance upon the safety assessment and stability prognostication of sundry geological undertakings.

Evaluation of energy accumulation, strain burst potential and stability of rock mass during underground extraction of a highly stressed coal seam under massive strata-a field study

Severe geotechnical problems like strain burst, side spalling, roof fall, irregular caving, the premature collapse of rib/remnant pillars, etc. are frequently observed during the mining of highly stressed coal seams under massive overlying strata. In this study, different underground structures and adequate support systems under such complex geological conditions are designed by evaluating energy accumulation, strain burst potential, stress conditions and stability of the rock mass. An energy-based safety factor is derived and implemented through numerical modelling to identify the yield zones in the surrounding rock mass. The strain burst potential in different locations is also evaluated by the elastic energy accumulation and the Burst Potential Index (BPI) whose maximum values reach 628 kJ/m3 and 47.6% respectively during depillaring operations. These values indicate the significant strain burst and side spalling conditions which are corroborated by the field investigations and monitoring data by geotechnical instruments. Control measures have been taken by leaving the optimum size of rib/remnant pillars in the goaf and installing adequate support systems in the working areas. It is observed in the field that rib/remnant pillars, designed with a safety factor of ∼0.17 are found suitable for regular caving of overlying massive strata in the goaf. The side spalling and the strain burst are minimised by installing the glass-reinforced plastic (GRP) bolts and the wire mesh. The uncontrolled caving of the overlying massive strata in the working area is prevented by installing closely spaced two rows of rock bolts at the goaf edge. This study would be helpful to design different underground structures and to ensure the safety of working areas during the extraction of highly stressed coal seams under massive strata.

IOT Powered Smart Strata Monitoring System for early and precise detection of Convergence in Underground Coal Mines

Roof fall, bed separation and convergence are one of the serious life-threatening problems faced by the miners in all the underground coal mines since early mining age to current mining situations. Most of the statistics regarding fatal accidents and trapped miners in underground coal mines are the results of sudden roof fall due to bed separation of overlying strata in the underground mine galleries or roadways. In Indian underground coal mines, many of fatalities were seen over the years although this rate has been gradually decreasing since past decade only by implementing some of physical and mechanical methods to detect or monitor strata convergence. With the advancement of technology, sensors become an extensive part of everyday life. Various researchers are trying to monitor, detect and prevent fatalities due to roof fall in real time scenario in underground coal mines with the use of sensors, IOT and other technology. The main aim is to Install a non-invasive laser proximity sensor in the hole drilled into the roof where the necessity arises to remotely monitor the convergence in the strata under bad roof conditions in underground coal mines and monitor them remotely with an automated display showing the details of the bed separation, roof convergence and detailed information about the roof condition remotely. Key Word: Internet of Things, Strata Monitoring, Roof Fall, Convergence, Real time, Display device, Proximity sensors, Remote Monitoring.

Ontology-Based Semantic Modeling of Coal Mine Roof Caving Accidents

The frequency of roof-caving accidents ranks first among all coal mine accidents. However, the scattered knowledge system in this field and the lack of standardization exacerbate the difficulty of analyzing roof fall accidents. This study proposes an ontology-based semantic modeling method for roof fall accidents to share and reuse roof fall knowledge for intelligent decision-making. The crucial concepts of roof fall accidents and the correlations between concepts are summarized by analyzing the roof fall knowledge, providing a standard framework to represent the prior knowledge in this field. Besides, the ontology modeling tool Protégé is used to construct the ontology. As for ontology-based deep information mining and semantic reasoning, semantic rules based on expert experience and data fusion technology are proposed to evaluate mines’ potential risks comprehensively. In addition, the roof-falling rules are formalized based on the Jena syntax to make the ontology uniformly expressed in the computer. The Jena reasoning engine is utilized to mine potential tacit knowledge and preventive measures or solutions. The proposed method is demonstrated using roof fall cases, which confirms its validity and practicability. Results indicate that this method can realize the storage, management, and sharing of roof fall accident knowledge. Furthermore, it can provide accurate and comprehensive experience knowledge for the roof fall knowledge requester.

Rapid determination of fuzzy number in FAHP and assessment risk in coal mine roof fall

This study proposed a rapid approach to determine the fuzzy number to evaluate roof fall risk in the coal mine. In risk assessments by fuzzy theory, the determination of fuzzy numbers using triangular fuzzy numbers (TFN) incorporated into the analytic hierarchy process is a difficult and time-consuming task. A novel approach to determine fuzzy number in TFN was proposed. To reduce occurrence of roof fall accidents, it is necessary to identify the main risk factors in roof fall risk assessments. A nine-score table-type questionnaire was adopted to collected expert judgement, based on which language scales were determined, ranging from 0.1 to 0.9, with increments of 0.1. Each language scale corresponded to the selected value of the experts. Subsequently, a triangular fuzzy complementary judgement matrix and a consistent possibility judgement matrix were constructed. The effectiveness of this proposed method was verified via analysing a case of roof collapse at Chaidar coal mine, Qinghai Province, China. The final ranking results of the risk factors were consistent with those observed in the field.

Finite–Discrete Element Method Prediction of Advanced Fractures in Extra-Thick Coal Seams Based on a Constitutive Model of Rock Deformation–Fragmentation Failure Process

Roof fall is a frequent and destructive disaster in the working face of extra-thick coal seams. The important technology for disaster elimination is roof grouting, and the key to its success is to accurately predict the distance of the advanced fractures based on a reasonable rock constitutive relationship. In this paper, the constitutive relationship reflecting the progressive failure process of rock was established, including the elastic–plastic constitutive relation of intact rock, the fracture constitutive relation of non-penetrating fracture, and the shear friction constitutive relation of penetrating fracture. On this basis, the finite–discrete element method (FDEM) numerical calculation method was developed. Taking Yushupo Coal mine with a 16-m-thick coal seam as an example, the numerical results showed that the fractures in the roof appear 15~35 m ahead of the working face, and the maximum value of advance bearing pressure is between 16 and 30 MPa. Meanwhile the laboratory test results showed that the compressive strength of the grouted coal is 14.91 MPa after solidification for 7d. The above data mean that the grouting slurry can solidify the broken roof into a whole without roof fall disaster. At the same time, the rock pressure of the extra-thick coal seam can effectively crush the top coal, which is conducive to the top-coal caving operation. The in situ test shows that when the pre-grouting is carried out in the range of 20~30 m in front of the working face, the roof fall disaster can be effectively avoided, which is consistent with the numerical simulation results. It shows the rationality of the FDEM numerical method and the constitutive model of rock deformation–fragmentation failure process.

Numerical Simulation Study on Stability Control Technology of Large-Area Wall Caving Area (LAWCA) in Large-Inclined Face

The caving characteristics of the goaf significantly impact the mining stability of the working face. The coal-forming environment of the steeply dipping coal seam is complex. After mining, the caving rock mass rolls to the lower part of the working face due to gravity, and the caving compaction characteristics of the working face are different from those of the flat and gently inclined working face, taking Huainan Xinji No. 2 Mine 211112 high-angle composite roof working face large-area gang roof fall area as the engineering background. To safely pass through a large roof fall area, a FLAC3D numerical simulation method considering the goaf’s compaction effect and the excavation’s additional damage is proposed. The Weibull distribution function is introduced to modify the elastic modulus parameters of the rock mass after excavation, which more accurately reflects the damage process of the rock mass after hole. Based on the goaf compaction theory, the elastic model is developed for the second time, and the accurate simulation of the caving rock mass is realized. On this basis, the proposed numerical simulation method is used to simulate the large-area roof caving of the large-angle working face, a scheme to control the stability of the working face by using the paste filling technology is proposed, and the material parameters of the filling body are determined. The research results show that when the water-cement ratio of the backfill is 3 : 1, the simulation and field trial results are good, and the safe and efficient mining of the large-angle composite roof working face is realized, which verifies the feasibility and correctness of the coupled simulation method. At the same time, the research results can provide a reference for controlling the roof fall area of the working face with a large inclination angle.

Roof fall threat analysis using fractal pattern recognition and neural network over mine microseismicity in a Central Indian longwall panel overlain by massive sandstone roof

• Roof fall threat analysis using fractal pattern recognition and neural network. • Identification of stressed zones through spatial distribution of microseismic events. • Fast and automated identification of roof stress level through neural network. • Spatio-temporal forecasting of roof fall for enhancing safety in underground mines. Due to its high degree of mechanization, caved longwall mining is the most sophisticated and advanced mining method, and it is widely accepted across world's major coal producing countries. The overlying strata dynamics above goaf (void spaces behind powered supports) acts as the deciding parameter towards smooth and safe longwall operation. Usually, this overhanging load above goaf undergoes regular caving for releasing the excess accumulated stress, but sometimes the presence of massive hard strata (mostly sandstone beds) reduces the cavablity of immediate roof. The delayed caving tends to increase the hanging time and load weight of strata. The excess accumulated stress is released in form of sudden roof falls, which are massive by nature and also considered to be the most serious threat to the underground mines. The aftereffects of these untimely roof falls are often associated with catastrophic consequences in form of fatalities, non-fatal injuries, instrument loss and reduction in productivity. Therefore, fast-automated monitoring of roof stress level with higher degree of precision in real time is highly desired for safe operation of mines, which will have the ability to detect the location (in 3D) and threat level of stressed zones in the overlying strata for setting a precursory alarm towards roof fall forecasting. Similar work has been carried out in the present study, which tends to use the passive microseismicity associated with coal mine for monitoring the stress level in the overlying strata and roof fall forecasting through fractal pattern recognition and neural network over microseismic event distribution. The prime objective of the study was to enhance the safety in the working area of the underground mines through: (a) fast and automated identification of roof fall threat level in real time and to categorized it under five threat classes using fractal pattern recognition of microseismic event distribution, (b) developing a suitable predictive model in order to identify high hazard event windows, which acted as precursory signatures towards roof fall forecasting, and (c) timely identification of stressed areas in the overlying strata for providing prior information towards spatio-temporal forecasting of roof fall.

Machine Learning Approach to Roof Fall Risks Classification in UG Mines using Adaboost and XGboost Incorporating Transfer Learning Technique

Machine Learning Approach to Roof Fall Risks Classification in UG Mines using Adaboost and XGboost Incorporating Transfer Learning Technique