Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings

Wojtecki, Łukasz; Iwaszenko, Sebastian; Apel, Derek B.; Bukowska, M.; Makówka, Janusz

doi:10.1016/j.jrmge.2021.10.011

Cited by 41 publications

(15 citation statements)

References 23 publications

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“…In the whole short-term rockburst dataset, only one rockburst level has been incorrectly predicted. In more detail, one level (0) is incorrectly labelled as level (1). Hence, the KNN algorithm performed well in forecasting the rockburst level in underground civil structures.…”

Section: Resultsmentioning

confidence: 99%

“…Rockburst is a dynamic phenomenon which occurs in underground excavations when immense amounts of energy are released, rocks are inelastically deformed, and rocks are thrown into the excavations [1]. As de ned by the Mine Safety and Health Administration (MSHA), "a rockburst occurs when overstressed rock collapses abruptly, releasing large amounts of energy instantly" [2].…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of microseismic monitoring data, Zhao et al [22] developed a model for prediction of rockbursts that uses a decision tree (DT) model. For assessing the rockburst hazard of an active hard coal mine, Wojtecki et al [1] used neural networks, decision trees, RF, gradient boosting, and XGBoost. In order to study the predictability of short-term rockburst, Liang et al [33] used microseismic data from Jinping-II hydropower project to examine the predictability of short-term rockburst.…”

Section: Introductionmentioning

confidence: 99%

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Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Kamran

Ullah

Ahmad

et al. 2022

Preprint

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The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbour (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Kamran

Ullah

Ahmad

et al. 2022

Preprint

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show abstract

“…In the whole shortterm rockburst dataset, only one rockburst level has been incorrectly predicted. In more detail, one level (0) is incorrectly labeled as level (1). Hence, the KNN algorithm performed well in forecasting the rockburst level in underground civil structures.…”

Section: Resultsmentioning

confidence: 99%

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

Kamran

Ullah

Ahmad

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbor (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.

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“…In the prediction of mine rockburst hazards, machine learning methods have been proposed to predict rockburst hazards with good results (Ullah et al, 2022;Wojtecki et al, 2022;Xiao et al, 2022). Machine learning is a complex and crosscutting discipline.…”

Section: Introductionmentioning

confidence: 99%

Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification

et al. 2022

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Rockburst is a complex dynamic disaster in coal mining and affected by many factors. To accurately predict the rockburst hazard among complex influencing factors, a prediction model of rockburst hazard based on the Gaussian process for binary classification (GPC) was proposed after the identification of the intrinsic relationship between multiple factors of coal mines and rockburst. Through computerized machine learning and integrated intelligent analysis, the non-linear mapping of rockburst hazard and its influencing factors was established. The multi-factor pattern recognition model was constructed using artificial intelligence. The prediction criteria of the rockburst hazard probability and the hazard probability value of the prediction area unit were determined by applying neural network and fuzzy inference methods. In addition, the rockburst hazardous zone was classified, and the corresponding technical scheme for the prevention was put forward. The validity and feasibility of the regional prediction of rockburst hazard based on GPC were verified in the engineering practice. This method is highly targeted and can improve the accuracy and precision of rockburst prediction, thus contributing to the safe and efficient production of coal mines.

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Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings

Cited by 41 publications

References 23 publications

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification

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