Abstract
The prediction of the failure depth of open stope hangingwalls (HWs) has become a crucial task for underground mines worldwide. In this paper, an evolutionary random forest (RF) technique that combines the RF algorithm and particle swarm optimization (PSO) was proposed to model the non-linear relationship between maximum failure depth (MI-D) of stope HWs and its influencing variables. The training and verification of RF models were conducted on a dataset collected from the literature, and a total number of 125 valid HW cases were analyzed. The 13 influencing variables were used as inputs. Hyper-parameters of RF models were tuned by PSO with fivefold cross-validation, and the performance of RF models was measured by root-mean-square error and correlation coefficient (R). Experimental results show that the RF algorithm had great potential for predicting the MFD of HWs. The R values between the predicted and actual MFD values were 0.93 and 0.84, respectively. The stope design method was found to be the most significant influencing variable with an importance score of 0.516 out of 1, followed by the RQD of rock mass (0.127), stope strike (0.078), and HW height (0.075).
| Original language | English |
|---|---|
| Article number | 8528310 |
| Pages (from-to) | 72808-72813 |
| Number of pages | 6 |
| Journal | IEEE Access |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2018 |
Cite this
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Evolutionary Random Forest Algorithm for Predicting the Maximum Failure Depth of Open Stope Hangingwalls. / Qi, Chongchong; Chen, Qiusong.
In: IEEE Access, Vol. 6, 8528310, 2018, p. 72808-72813.Research output: Contribution to journal › Article
TY - JOUR
T1 - Evolutionary Random Forest Algorithm for Predicting the Maximum Failure Depth of Open Stope Hangingwalls
AU - Qi, Chongchong
AU - Chen, Qiusong
PY - 2018
Y1 - 2018
N2 - The prediction of the failure depth of open stope hangingwalls (HWs) has become a crucial task for underground mines worldwide. In this paper, an evolutionary random forest (RF) technique that combines the RF algorithm and particle swarm optimization (PSO) was proposed to model the non-linear relationship between maximum failure depth (MI-D) of stope HWs and its influencing variables. The training and verification of RF models were conducted on a dataset collected from the literature, and a total number of 125 valid HW cases were analyzed. The 13 influencing variables were used as inputs. Hyper-parameters of RF models were tuned by PSO with fivefold cross-validation, and the performance of RF models was measured by root-mean-square error and correlation coefficient (R). Experimental results show that the RF algorithm had great potential for predicting the MFD of HWs. The R values between the predicted and actual MFD values were 0.93 and 0.84, respectively. The stope design method was found to be the most significant influencing variable with an importance score of 0.516 out of 1, followed by the RQD of rock mass (0.127), stope strike (0.078), and HW height (0.075).
AB - The prediction of the failure depth of open stope hangingwalls (HWs) has become a crucial task for underground mines worldwide. In this paper, an evolutionary random forest (RF) technique that combines the RF algorithm and particle swarm optimization (PSO) was proposed to model the non-linear relationship between maximum failure depth (MI-D) of stope HWs and its influencing variables. The training and verification of RF models were conducted on a dataset collected from the literature, and a total number of 125 valid HW cases were analyzed. The 13 influencing variables were used as inputs. Hyper-parameters of RF models were tuned by PSO with fivefold cross-validation, and the performance of RF models was measured by root-mean-square error and correlation coefficient (R). Experimental results show that the RF algorithm had great potential for predicting the MFD of HWs. The R values between the predicted and actual MFD values were 0.93 and 0.84, respectively. The stope design method was found to be the most significant influencing variable with an importance score of 0.516 out of 1, followed by the RQD of rock mass (0.127), stope strike (0.078), and HW height (0.075).
KW - Failure depth prediction
KW - five-fold cross validation
KW - open stope hangingwall
KW - particle swarm optimization
KW - random forest
KW - variable importance
KW - CEMENTED PASTE BACKFILL
KW - DECISION TREE
KW - STABILITY
KW - FRAMEWORK
KW - MACHINE
KW - WASTE
U2 - 10.1109/ACCESS.2018.2880009
DO - 10.1109/ACCESS.2018.2880009
M3 - Article
VL - 6
SP - 72808
EP - 72813
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8528310
ER -