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Article Title

Rockburst prediction in kimberlite using decision tree with incomplete data

Abstract

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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