A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting
- Dai, Yong, Khandelwal, Manoj, Qiu, Yingui, Zhou, Jian, Monjezi, Monjezi, Yang, Peixi
- Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
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- Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
- Full Text:
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- Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Zhou, Jian, Dai, Yong, Du, Kun, Khandelwal, Manoj, Li, Chuanqi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Du, Kun , Khandelwal, Manoj , Li, Chuanqi , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Transportation Geotechnics Vol. 36, no. (2022), p.
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- Description: Since conical pick cutting is a complex process of multi-factor coupling effects, theoretical model construction for cutting force prediction is a quite difficult task. In this paper, various novel intelligent models based on chaos-optimized slime mould algorithm (COSMA) and random forest (RF) are proposed for this task. In the proposed COSMA-RF methods, the chaos algorithms with the ergodicity and randomness are introduced to chaotically determine the initial position to form a COSMA, and the SMA and COSMA are used to tune the hyperparameters of RF and mean square error are assigned as a fitness function. Consequently, 205 data samples having seven variables (tensile strength of the rock
Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm
- Zhou, Jian, Dai, Yong, Tao, Ming, Khandelwal, Manoj, Zhao, Mingsheng, Li, Qiyue
- Authors: Zhou, Jian , Dai, Yong , Tao, Ming , Khandelwal, Manoj , Zhao, Mingsheng , Li, Qiyue
- Date: 2023
- Type: Text , Journal article
- Relation: Results in Engineering Vol. 17, no. (2023), p.
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- Description: Conical picks are widely used as cutting tools in shearers and roadheaders, and the mean cutting force (MCF) is one of the important parameters affecting conical pick performance. As MCF depends on a number of parameters and due to that the existing empirical and theoretical formulas and numerical modelling are not sufficient enough and reliable to predict MCF in a proficient manner. So, in this research, a novel intelligent model based on a random forest algorithm (RF) and a heuristic algorithm called the salp swarm algorithm (SSA) have been applied to determine the optimal hyper-parameters in RF, and root mean square error is used as a fitness function. A total of 188 data samples including 50 rock types and seven parameters (tensile strength of the rock
- Authors: Zhou, Jian , Dai, Yong , Tao, Ming , Khandelwal, Manoj , Zhao, Mingsheng , Li, Qiyue
- Date: 2023
- Type: Text , Journal article
- Relation: Results in Engineering Vol. 17, no. (2023), p.
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- Description: Conical picks are widely used as cutting tools in shearers and roadheaders, and the mean cutting force (MCF) is one of the important parameters affecting conical pick performance. As MCF depends on a number of parameters and due to that the existing empirical and theoretical formulas and numerical modelling are not sufficient enough and reliable to predict MCF in a proficient manner. So, in this research, a novel intelligent model based on a random forest algorithm (RF) and a heuristic algorithm called the salp swarm algorithm (SSA) have been applied to determine the optimal hyper-parameters in RF, and root mean square error is used as a fitness function. A total of 188 data samples including 50 rock types and seven parameters (tensile strength of the rock
Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Zhou, Jian, Dai, Yong, Khandelwal, Manoj, Monjezi, Masoud, Yu, Zhi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
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- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
Stability prediction of underground entry-type excavations based on particle swarm optimization and gradient boosting decision tree
- Zhou, Jian, Huang, Shuai, Tao, Ming, Khandelwal, Manoj, Dai, Yong, Zhao, Mingsheng
- Authors: Zhou, Jian , Huang, Shuai , Tao, Ming , Khandelwal, Manoj , Dai, Yong , Zhao, Mingsheng
- Date: 2023
- Type: Text , Journal article
- Relation: Underground Space (China) Vol. 9, no. (2023), p. 234-249
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- Description: The stability of underground entry-type excavations will directly affect the working environment and the safety of staff. Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations. Therefore, this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. Accordingly, the particle swarm optimization (PSO) algorithm is used to optimize the core parameters of the gradient boosting decision tree (GBDT), abbreviated as PSO-GBDT. Moreover, the classification performance of eight other classifiers including GDBT, k-nearest neighbors (KNN), two kinds of support vector machines (SVM), Gaussian naive Bayes (GNB), logistic regression (LR) and linear discriminant analysis (LDA) are also applied to compare with the proposed model. Findings revealed that compared with the other eight models, the prediction performance of PSO-GBDT is undoubtedly the most reliable, and its classification accuracy is up to 0.93. Therefore, this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations. In addition, each classification model is used to predict the stability category of several grid points divided by the critical span graph, and the updated critical span graph of each model is discussed in combination with previous studies. The results show that the PSO-GBDT model has the advantages of being scientific, accurate and efficient in updating the critical span graph, and its output decision boundary has strict theoretical support, which can help mine operators make favorable economic decisions. © 2022
- Authors: Zhou, Jian , Huang, Shuai , Tao, Ming , Khandelwal, Manoj , Dai, Yong , Zhao, Mingsheng
- Date: 2023
- Type: Text , Journal article
- Relation: Underground Space (China) Vol. 9, no. (2023), p. 234-249
- Full Text:
- Reviewed:
- Description: The stability of underground entry-type excavations will directly affect the working environment and the safety of staff. Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations. Therefore, this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. Accordingly, the particle swarm optimization (PSO) algorithm is used to optimize the core parameters of the gradient boosting decision tree (GBDT), abbreviated as PSO-GBDT. Moreover, the classification performance of eight other classifiers including GDBT, k-nearest neighbors (KNN), two kinds of support vector machines (SVM), Gaussian naive Bayes (GNB), logistic regression (LR) and linear discriminant analysis (LDA) are also applied to compare with the proposed model. Findings revealed that compared with the other eight models, the prediction performance of PSO-GBDT is undoubtedly the most reliable, and its classification accuracy is up to 0.93. Therefore, this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations. In addition, each classification model is used to predict the stability category of several grid points divided by the critical span graph, and the updated critical span graph of each model is discussed in combination with previous studies. The results show that the PSO-GBDT model has the advantages of being scientific, accurate and efficient in updating the critical span graph, and its output decision boundary has strict theoretical support, which can help mine operators make favorable economic decisions. © 2022
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