The Experts below are selected from a list of 21765 Experts worldwide ranked by ideXlab platform
Hoang Nguyen - One of the best experts on this subject based on the ideXlab platform.
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computational intelligence model for estimating intensity of blast induced Ground Vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms
Natural resources research, 2020Co-Authors: Ziwei Ding, Xuan-nam Bui, Hoang Nguyen, Jian Zhou, Hossein MoayediAbstract:In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced Ground Vibration in a mine based on extreme gradient boosting (XGBoost) and imperialist competitive algorithm (ICA). For comparison, we used another hybrid model combining particle swarm optimization and XGBoost [i.e., particle swarm optimization (PSO)–XGBoost] as well as other models, namely classical XGBoost, artificial neural network (ANN), gradient boosting machine (GBM), and support vector regression (SVR). We compared these techniques using 136 blasting events data gathered at an open-pit coal mine in Vietnam. The models’ performance evaluation criteria were the determination coefficient (R2), root-mean-square error, mean absolute error, ranking, and color intensity. Based on the results, our ICA–XGBoost model is the most robust in predicting blast-produced Ground Vibration. The PSO–XGBoost model provided a slightly poorer performance. The classical XGBoost model showed a lower performance than the hybrid models (i.e., ICA–XGBoost and PSO–XGBoost). The SVR and ANN models gave average performances, whereas the GBM model yielded the worst performance. The results also reveal that the maximum explosive charge capacity, the elevation between blast sites and monitoring points, and the monitoring distance are the most critical variables that should be used in predicting the intensity of blast-induced Ground Vibration in a mine.
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a novel artificial intelligence approach to predict blast induced Ground Vibration in open pit mines based on the firefly algorithm and artificial neural network
Natural resources research, 2020Co-Authors: Yonghui Shang, Xuan-nam Bui, Hoang Nguyen, Quanghieu Tran, Hossein MoayediAbstract:The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict Ground Vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of Ground Vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced Ground Vibration.
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novel soft computing model for predicting blast induced Ground Vibration in open pit mines based on particle swarm optimization and xgboost
Natural resources research, 2020Co-Authors: Xiliang Zhang, Xuan-nam Bui, Hoang Nguyen, Dieu Tien Bui, Quanghieu Tran, Dinhan Nguyen, Hossein MoayediAbstract:Blasting is a useful technique for rocks fragmentation in open-pit mines, underGround mines, as well as for civil engineering work. However, the negative impacts of blasting, especially Ground Vibration, on the surrounding environment are significant. Ground Vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.
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A comparison of advanced computational models and experimental techniques in predicting blast-induced Ground Vibration in open-pit coal mine
Acta Geophysica, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Hossein MoayediAbstract:Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that significantly affect the environment, especially Ground Vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k -nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced Ground Vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict Ground Vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient ( R ^2), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced Ground Vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting Ground Vibration with an RMSE of 0.508, R ^2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R ^2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.
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a new soft computing model for estimating and controlling blast produced Ground Vibration based on hierarchical k means clustering and cubist algorithms
Applied Soft Computing, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Quanghieu Tran, Ngocluan MaiAbstract:Abstract Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially Ground Vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.
Hossein Moayedi - One of the best experts on this subject based on the ideXlab platform.
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a novel artificial intelligence approach to predict blast induced Ground Vibration in open pit mines based on the firefly algorithm and artificial neural network
Natural resources research, 2020Co-Authors: Yonghui Shang, Xuan-nam Bui, Hoang Nguyen, Quanghieu Tran, Hossein MoayediAbstract:The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict Ground Vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of Ground Vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced Ground Vibration.
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computational intelligence model for estimating intensity of blast induced Ground Vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms
Natural resources research, 2020Co-Authors: Ziwei Ding, Xuan-nam Bui, Hoang Nguyen, Jian Zhou, Hossein MoayediAbstract:In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced Ground Vibration in a mine based on extreme gradient boosting (XGBoost) and imperialist competitive algorithm (ICA). For comparison, we used another hybrid model combining particle swarm optimization and XGBoost [i.e., particle swarm optimization (PSO)–XGBoost] as well as other models, namely classical XGBoost, artificial neural network (ANN), gradient boosting machine (GBM), and support vector regression (SVR). We compared these techniques using 136 blasting events data gathered at an open-pit coal mine in Vietnam. The models’ performance evaluation criteria were the determination coefficient (R2), root-mean-square error, mean absolute error, ranking, and color intensity. Based on the results, our ICA–XGBoost model is the most robust in predicting blast-produced Ground Vibration. The PSO–XGBoost model provided a slightly poorer performance. The classical XGBoost model showed a lower performance than the hybrid models (i.e., ICA–XGBoost and PSO–XGBoost). The SVR and ANN models gave average performances, whereas the GBM model yielded the worst performance. The results also reveal that the maximum explosive charge capacity, the elevation between blast sites and monitoring points, and the monitoring distance are the most critical variables that should be used in predicting the intensity of blast-induced Ground Vibration in a mine.
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novel soft computing model for predicting blast induced Ground Vibration in open pit mines based on particle swarm optimization and xgboost
Natural resources research, 2020Co-Authors: Xiliang Zhang, Xuan-nam Bui, Hoang Nguyen, Dieu Tien Bui, Quanghieu Tran, Dinhan Nguyen, Hossein MoayediAbstract:Blasting is a useful technique for rocks fragmentation in open-pit mines, underGround mines, as well as for civil engineering work. However, the negative impacts of blasting, especially Ground Vibration, on the surrounding environment are significant. Ground Vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.
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A comparison of advanced computational models and experimental techniques in predicting blast-induced Ground Vibration in open-pit coal mine
Acta Geophysica, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Hossein MoayediAbstract:Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that significantly affect the environment, especially Ground Vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k -nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced Ground Vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict Ground Vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient ( R ^2), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced Ground Vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting Ground Vibration with an RMSE of 0.508, R ^2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R ^2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.
Xuan-nam Bui - One of the best experts on this subject based on the ideXlab platform.
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computational intelligence model for estimating intensity of blast induced Ground Vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms
Natural resources research, 2020Co-Authors: Ziwei Ding, Xuan-nam Bui, Hoang Nguyen, Jian Zhou, Hossein MoayediAbstract:In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced Ground Vibration in a mine based on extreme gradient boosting (XGBoost) and imperialist competitive algorithm (ICA). For comparison, we used another hybrid model combining particle swarm optimization and XGBoost [i.e., particle swarm optimization (PSO)–XGBoost] as well as other models, namely classical XGBoost, artificial neural network (ANN), gradient boosting machine (GBM), and support vector regression (SVR). We compared these techniques using 136 blasting events data gathered at an open-pit coal mine in Vietnam. The models’ performance evaluation criteria were the determination coefficient (R2), root-mean-square error, mean absolute error, ranking, and color intensity. Based on the results, our ICA–XGBoost model is the most robust in predicting blast-produced Ground Vibration. The PSO–XGBoost model provided a slightly poorer performance. The classical XGBoost model showed a lower performance than the hybrid models (i.e., ICA–XGBoost and PSO–XGBoost). The SVR and ANN models gave average performances, whereas the GBM model yielded the worst performance. The results also reveal that the maximum explosive charge capacity, the elevation between blast sites and monitoring points, and the monitoring distance are the most critical variables that should be used in predicting the intensity of blast-induced Ground Vibration in a mine.
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a novel artificial intelligence approach to predict blast induced Ground Vibration in open pit mines based on the firefly algorithm and artificial neural network
Natural resources research, 2020Co-Authors: Yonghui Shang, Xuan-nam Bui, Hoang Nguyen, Quanghieu Tran, Hossein MoayediAbstract:The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict Ground Vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of Ground Vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced Ground Vibration.
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novel soft computing model for predicting blast induced Ground Vibration in open pit mines based on particle swarm optimization and xgboost
Natural resources research, 2020Co-Authors: Xiliang Zhang, Xuan-nam Bui, Hoang Nguyen, Dieu Tien Bui, Quanghieu Tran, Dinhan Nguyen, Hossein MoayediAbstract:Blasting is a useful technique for rocks fragmentation in open-pit mines, underGround mines, as well as for civil engineering work. However, the negative impacts of blasting, especially Ground Vibration, on the surrounding environment are significant. Ground Vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.
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A comparison of advanced computational models and experimental techniques in predicting blast-induced Ground Vibration in open-pit coal mine
Acta Geophysica, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Hossein MoayediAbstract:Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that significantly affect the environment, especially Ground Vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k -nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced Ground Vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict Ground Vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient ( R ^2), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced Ground Vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting Ground Vibration with an RMSE of 0.508, R ^2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R ^2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.
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a new soft computing model for estimating and controlling blast produced Ground Vibration based on hierarchical k means clustering and cubist algorithms
Applied Soft Computing, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Quanghieu Tran, Ngocluan MaiAbstract:Abstract Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially Ground Vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.
Quanghieu Tran - One of the best experts on this subject based on the ideXlab platform.
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a novel artificial intelligence approach to predict blast induced Ground Vibration in open pit mines based on the firefly algorithm and artificial neural network
Natural resources research, 2020Co-Authors: Yonghui Shang, Xuan-nam Bui, Hoang Nguyen, Quanghieu Tran, Hossein MoayediAbstract:The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict Ground Vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of Ground Vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced Ground Vibration.
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novel soft computing model for predicting blast induced Ground Vibration in open pit mines based on particle swarm optimization and xgboost
Natural resources research, 2020Co-Authors: Xiliang Zhang, Xuan-nam Bui, Hoang Nguyen, Dieu Tien Bui, Quanghieu Tran, Dinhan Nguyen, Hossein MoayediAbstract:Blasting is a useful technique for rocks fragmentation in open-pit mines, underGround mines, as well as for civil engineering work. However, the negative impacts of blasting, especially Ground Vibration, on the surrounding environment are significant. Ground Vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.
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a new soft computing model for estimating and controlling blast produced Ground Vibration based on hierarchical k means clustering and cubist algorithms
Applied Soft Computing, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Quanghieu Tran, Ngocluan MaiAbstract:Abstract Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially Ground Vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.
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evaluating and predicting blast induced Ground Vibration in open cast mine using ann a case study in vietnam
SN Applied Sciences, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Quanghieu Tran, Le Thi Thu HoaAbstract:Blasting is one of the cheapest and effective methods for breaking rock mass in open-pit mines. However, its side effects are not small such as Ground Vibration (PPV), air overpressure, fly rock, back break, dust, and toxic. Of these side effects, blast-induced PPV is the most dangerous for the human and surrounding environment. Therefore, evaluating and accurately forecasting blast-induced PPV is one of the most challenging issues facing open-pit mines today. In this paper, a series of artificial neural network models were applied to predict blast-induced PPV in an open-pit coal mine of Vietnam; 68 blasting events were used in this study for development of the ANN models. Of the whole dataset, 80% (approximately 56 observations) were used for the training process, and the rest of 20% (12 observations) were used for the testing process. Five ANN models were developed in this study with the difference in the number of hidden layers. The ANN 2-5-1; ANN 2-8-6-1; ANN 2-5-3-1; ANN 2-8-6-4-1; and ANN 2-10-8-5-1 models were considered in this study. An empirical technique was also conducted to estimate blast-induced PPV and compared to the constructed ANN models. For evaluating the performance of the models, root-mean-squared error (RMSE) and determination coefficient (R2) were used. The results indicated that the ANN 2-10-8-5-1 model (10 neurons in the first hidden layer, 8 neurons in the second hidden layer, and 5 neurons for the third hidden layer) yielded a superior performance over the other models with an RMSE of 0.738 and R2 of 0.964. In contrast, the empirical performed poorest performance with an RMSE of 2.670 and R2 of 0.768. This study is a new approach to predict blast-induced PPV in open-cast mines aim to minimize the adverse effects of blasting operations on the surrounding environment.
Dieu Tien Bui - One of the best experts on this subject based on the ideXlab platform.
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novel soft computing model for predicting blast induced Ground Vibration in open pit mines based on particle swarm optimization and xgboost
Natural resources research, 2020Co-Authors: Xiliang Zhang, Xuan-nam Bui, Hoang Nguyen, Dieu Tien Bui, Quanghieu Tran, Dinhan Nguyen, Hossein MoayediAbstract:Blasting is a useful technique for rocks fragmentation in open-pit mines, underGround mines, as well as for civil engineering work. However, the negative impacts of blasting, especially Ground Vibration, on the surrounding environment are significant. Ground Vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.
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Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network
Natural Resources Research, 2019Co-Authors: Hoang Nguyen, Xuan-nam Bui, Carsten Drebenstedt, Dieu Tien BuiAbstract:Ground Vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the stability of benches and slopes in open-pit mines, and impact underGround water, railway, highway, and puzzling for neighboring communities. Therefore, controlling, accurate prediction, and mitigating blast-induced PPV are necessary. This study contributed a new computational model in predicting blast-induced PPV for the science community and practical engineering with high accuracy level. In this study, a novel hybrid artificial intelligence model based on the hierarchical k -means clustering algorithm (HKM) and artificial neural network (ANN), namely a HKM–ANN model, was considered and proposed for predicting blast-caused PPV in open-pit mines. Accordingly, input data were first clustered by the HKM algorithm, and then, the ANN models were developed based on the obtained clusters. For this aim, 185 blasting events were collected and analyzed. A hybrid model based on fuzzy c -means clustering (FCM) and support vector regression (SVR), i.e., FCM–SVR model, which was proposed by previous authors was also applied for comparison of results with our proposed HKM–ANN model. In addition, an empirical method, several ANN and SVR models (without clustering), FCM–ANN, and HKM–SVR were developed for comparison purposes. For measuring the performance of the improved models, coefficient determination ( R ^2), root-mean-square error, and variance account for were used as the performance indicators. The results show that the HKM algorithm played a significant role in improving the accuracy of the ANN models. The proposed HKM–ANN model was the most superior model in estimating PPV caused by blasting operations in this study.
