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Rockburst prediction in kimberlite using decision tree with incomplete data

Pu Y., Apel D. B., Lingga B.

Journal of Sustainable Mining

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2018

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Vol. 17, iss. 3

158--165

EN

A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite at an underground diamond mine, a decision tree method was employed. Based on two fundamental premises of rockburst occurrence, σθ, σc, σt, WET are determined as indicators of rockburst, which are also partition attributes of the decision tree. 132 training samples (with 24 incomplete samples) were obtained from real rockburst cases from all over the world to build the decision tree. The decision tree based on 108 complete samples was built with an accuracy of 73% for 15 validation samples while another decision tree based on 132 samples (with 24 groups of incomplete data) shows an accuracy of 93% for validation samples. Hence, the second decision tree was employed for kimberlite burst prediction. 12 samples from lab tests and a numerical model were used as test samples. The results indicate a moderate burst liability which matches real situations at the diamond mind in question.

2

Evaluation of burst liability in kimberlite using support vector machine

Pu Y., Apel D. B., Wang C., Wilson B.

Acta Geophysica

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2018

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Vol. 66, no. 5

973--982

EN

Due to the complex mechanisms of rockburst, there is no current effective method to reliably predict these events. A statistical learning method, support vector machine (SVM), is employed in this paper for kimberlite burst prediction. Four indicators σθ,σc,σt,WET are chosen as input indices for the SVM, which is trained using 108 groups of rockburst cases from around the world. Data uniformization is used to avoid negative impact of differing dimensions across the original data. Parameter optimization is embedded in the training process of the SVM to achieve optimized predictive ability. After training and optimization, the SVM reaches an accuracy of 95% in rock burst prediction for validation samples. The constructed SVM is then employed in kimberlite burst liability evaluation. The model indicated a moderate burst risk, which matches observed instances of rockburst at a diamond mine in north Canada. The SVM method ignores the focus on rockburst mechanisms, instead relying on representative indicators to develop a predictive model through self-learning. The prediction results show an excellent accuracy, which means this method has a potential application in rockburst prediction.

3

An Experimental Study of Venting Process of Gas-Air Mixture Combustion in a Cylindrical Vessel

Pu Y., Hu J., Jia F., Jarosiński J.

Archivum Combustionis

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2003

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Vol. 23 nr 1-2

13-20

EN

Experiments on gas combustion in a Vented cylindrical vessel with large aspect ratio (L/D = 5,6) show damping influence of a venting orifice on flame instability during the final stage of flame propagation, which in a closed vessel results in a sharp pressure rise. The proposed numerical model can satisfactorily simulate main feat ures of combustion in closed and vented vessels, such as pressure variation pattern, flame propagation velocity and flame configuration.