Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

Ke, Bo; Khandelwal, Manoj; Asteris, Panagiotis G.; Skentou, Athanasia D.; Mamou, Anna; Armaghani, Danial Jahed

doi:10.1109/access.2021.3089205

Cited by 41 publications

(9 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The elastic strain energy index (W ET ) is defined as the ratio of the accumulated elastic strain energy to the consumed plastic strain energy when the coal sample is completely unloaded after being loaded to 70%-80% of the UCS during the uniaxial compression process. [22][23][24] The greater the index, the stronger the burst proneness of coal. According to the stress-stain curve, it can be calculated as:…”

Section: Burst Proneness Indexesmentioning

confidence: 99%

Study on the evolutionary characteristics of coal burst proneness under the action of multiple mining disturbances

Liu,

Ouyang,

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

Coal burst is a serious hazard in deep coal mining. To investigate the coal burst proneness evolution during multiple mining disturbances (MMD), this paper implemented a range of uniaxial cyclic loading–unloading tests coupled with acoustic emission (AE) measurement to simulate the loading–unloading effects of the MMD. The maximum load was used to simulate the highest load that the coal has endured in history, the loading–unloading rate was used to simulate the mining speed, and the loading–unloading cycles were used to simulate the number of mining disturbances. Finally, the burst proneness indexes of the damaged coal sample are measured and compared with the origin coal sample. Analysis on the experiment results suggest that, the burst proneness increases when the maximum load is 30% of the uniaxial compressive strength (UCS), interestingly, when the low loading–unloading rate is applied, 50% of the UCS enlarge the burst proneness as well. However, it decreases when the maximum load is 70% of the UCS. Sensitivity analysis indicates that the maximum load, loading–unloading rate, and loading–unloading cycles exhibit decreasing influence sensitivity to the elastic modulus, as well as to the UCS and duration of dynamic fracture. Similarly, the influence sensitivity of maximum load, loading–unloading cycles, and loading–unloading rate to the elastic strain energy index decreases, as well as to the bursting energy index. To ensure the safety of coal mining, it is crucial to avoid the disturbances that would put the coal under low loading and low loading rate.

show abstract

Section: Burst Proneness Indexesmentioning

confidence: 99%

Study on the evolutionary characteristics of coal burst proneness under the action of multiple mining disturbances

Liu,

Ouyang,

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…Deep mining faces dynamical disasters, such as rockburst, coal bump, mine earthquake, large-scale destabilization of the goaf, roof collapses, water surges, and so on. Many scholars (Feng et al 2017;Shukla et al 2021;Ke et al 2021;Zhou et al 2021b; Kishore and Khandelwal 2022;Chen and Zhou 2023;He et al 2023;Qiu and Zhou 2023;Jiang et al 2017a, b;Wang et al 2023;Wei et al 2022;) have researched rockburst, coal bump and mine earthquake, and have achieved encouraging results. However, in English, rockburst and coal bump have different names.…”

Section: Introductionmentioning

confidence: 99%

Occurrence mechanism and prevention technology of rockburst, coal bump and mine earthquake in deep mining

Du,

Bi,

Khandelwal

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

Self Cite

View full text Add to dashboard Cite

Rockburst, coal bump, and mine earthquake are the most important dynamic disaster phenomena in deep mining. This paper summarizes the differences and connections between rockburst, coal bumps and mine earthquakes in terms of definition, mechanism, phenomenon, evaluation index, etc. The definition and evolution progress of the three disaster categories are summarized, as well as the monitoring, early warning, and prevention measures are also presented. Firstly, by combining theoretical research with specific technologies and engineering field cases, the main categories and failure mechanisms of rockburst, coal bumps and mine earthquakes are introduced. Then, the evaluation indexes of coal bump and mine earthquake are summarized, and a new evaluation index of rockburst is given. Finally, the characteristics of monitoring, early warning technologies and prevention methods of rockburst, coal bumps, and mine earthquakes are discussed in technology and application. At last, the future directions of rockburst, coal bump and mine earthquake are put forward.

show abstract

“…From a monetary perspective, the application of ML techniques is also profitable, because it reduces the costs related to lab tests for ascertaining the UCS. It is important to note that the mentioned ML techniques have been used and applied to solve science and engineering problems [ 21 , 23 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

et al. 2023

View full text Add to dashboard Cite

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson’s Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

show abstract

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

Cited by 41 publications

References 67 publications

Study on the evolutionary characteristics of coal burst proneness under the action of multiple mining disturbances

Study on the evolutionary characteristics of coal burst proneness under the action of multiple mining disturbances

Occurrence mechanism and prevention technology of rockburst, coal bump and mine earthquake in deep mining

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

Contact Info

Product

Resources

About