共 42 条
Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
被引:34
作者:
Ahmad, Mahmood
[1
,2
]
Al-Shayea, Naser A.
[3
]
Tang, Xiao-Wei
[1
]
Jamal, Arshad
[3
]
Al-Ahmadi, Hasan M.
[3
]
Ahmad, Feezan
[1
]
机构:
[1] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116024, Peoples R China
[2] Univ Engn & Technol Peshawar, Dept Civil Engn, Bannu Campus, Bannu 28100, Pakistan
[3] King Fahd Univ Petr & Minerals, Dept Civil & Environm Engn, KFUPM Box 5055, Dhahran 31261, Saudi Arabia
来源:
APPLIED SCIENCES-BASEL
|
2020年
/
10卷
/
18期
关键词:
pillar stability;
underground mines;
random tree;
C4;
5 decision tree;
prediction;
CLASSIFICATION;
STRENGTH;
DESIGN;
D O I:
10.3390/app10186486
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
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 (sigma(ucs)), and pillar stress (sigma(p)). 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.
引用
收藏
页数:12
相关论文