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Article

Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees

1
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2
Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
3
Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, KFUPM Box 5055, Dhahran 31261, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(18), 6486; https://doi.org/10.3390/app10186486
Received: 16 August 2020 / Revised: 12 September 2020 / Accepted: 14 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Techniques for Sustainable Processing of Natural Resources II)
Predicting pillar stability in underground mines is a critical problem because the instability of the pillar can cause large-scale collapse hazards. To predict the pillar stability for underground coal and stone mines, two new models (random tree and C4.5 decision tree algorithms) are proposed in this paper. Pillar stability depends on the parameters: width of the pillar (W), height of the pillar (H), W/H ratio, uniaxial compressive strength of the rock (σucs), and pillar stress (σp). These parameters are taken as input variables, while underground mines pillar stability as output. Various performance indices, i.e., accuracy, precision, recall, F-measure, Matthews correlation coefficient (MCC) were used to evaluate the performance of the models. The performance evaluation of the established models showed that both models were able to predict pillar stability with reasonable accuracy. Results of the random tree and C4.5 decision tree were also compared with available models of support vector machine (SVM) and fishery discriminant analysis (FDA). The results show that the proposed random tree provides a reliable and feasible method of evaluating the pillar stability for underground mines. View Full-Text
Keywords: pillar stability; underground mines; random tree; C4.5 decision tree; prediction pillar stability; underground mines; random tree; C4.5 decision tree; prediction
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MDPI and ACS Style

Ahmad, M.; Al-Shayea, N.A.; Tang, X.-W.; Jamal, A.; M. Al-Ahmadi, H.; Ahmad, F. Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees. Appl. Sci. 2020, 10, 6486. https://doi.org/10.3390/app10186486

AMA Style

Ahmad M, Al-Shayea NA, Tang X-W, Jamal A, M. Al-Ahmadi H, Ahmad F. Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees. Applied Sciences. 2020; 10(18):6486. https://doi.org/10.3390/app10186486

Chicago/Turabian Style

Ahmad, Mahmood, Naser A. Al-Shayea, Xiao-Wei Tang, Arshad Jamal, Hasan M. Al-Ahmadi, and Feezan Ahmad. 2020. "Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees" Applied Sciences 10, no. 18: 6486. https://doi.org/10.3390/app10186486

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