Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

Zhang, Xiliang; Nguyen, Hoang; Bui, Xuan‐Nam; Tran, Quang-Hieu; Nguyen, Dinh-An; Bui, Dieu Tien; Moayedi, Hossein

doi:10.1007/s11053-019-09492-7

Cited by 123 publications

(35 citation statements)

References 48 publications

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“…Usually, the network's weights are adjusted (changes) and, accordingly, processed to obtain the desired response. Moreover, the ANN system with single hidden layer is adequate to compute the specified problem [60,61]. Whereas, ANN with two or more hidden layers pattern may create complex glitches [12].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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“…Behzadafshar et al [44] ICA-linear R 2 = 0.939; RMSE = 0.320; VAF = 92.18%; MBE = 0.22; MAPE = 0.038 Tian et al [45] GA-power R 2 = 0.977; RMSE = 0.285 Hasanipanah et al [46] FS-ICA R 2 = 0.942; RMSE = 0.22; VAF = 94.2% Nguyen et al [12] HKM-ANN R 2 = 0.983; RMSE = 0.554; VAF = 97.488% Nguyen et al [11] HKM-CA R 2 = 0.995; RMSE = 0.475; MAE = 0.373 Zhang et al [8] PSO-XGBoost R 2 = 0.968; RMSE = 0.583; MAE = 0.346, VAF = 96.083…”

Section: Reference Methods Resultsmentioning

confidence: 99%

“…In this regard, artificial intelligence (AI) applications are considered useful, not only as robust techniques in the mining field but also in many other areas (e.g., civil engineering, fuel, and energy, and environment) [2,4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. An overview of the literature related to PPV prediction showed that many AI models have been developed and proposed, as listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

Nguyen

Choi

Bui

et al. 2019

Sensors

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In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.

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“…In blasting, only 25% to 30% of the explosive energy is spent in rock mass fragmentation, and more than 70% is wasted and causes side effects such as back break, fly rock, and blast-induced ground vibration [1][2][3]. Among these blast-induced side effects, blast-induced ground vibration is considered as the most common, important and dangerous side effects for nearby structures, human life and the environment [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…In practice, AI technology is also applied to predict blast-induced ground vibration and yield high prediction precision [32][33][34]. For example, Zhang et al [2] combined the particle swarm optimization (PSO) algorithm and extreme gradient boosting machine (XGBoost) for peak particle velocity prediction. In his paper, the PSO was utilized to search the optimal hyperparameters of XGBoost and a promising result was obtained.…”

Section: Introductionmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

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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

Cited by 123 publications

References 48 publications

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

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