Prediction of blast-induced ground motion in a copper mine

Lapčević, Radojica; Kostić, Srđan; Pantović, Radoje; Vasović, Nebojša

doi:10.1016/j.ijrmms.2014.03.002

Cited by 28 publications

(3 citation statements)

References 13 publications

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“…One year later, also Monjezi et al [15] used Artificial Neural Network (ANN) to predict PPV considering 4 input parameters consisting of maximum charge per delay, distance from blasting face to the monitoring point, stemming, and hole depth. Lapčević et al [16] predicted PPV by ANNs with different number of hidden nodes, and just 5 input parameters were considered, i.e., total charge (kg), maximum charge per delay (kg), distance from blasting shot (m), charge per hole (kg), and delay time (ms). Saadat et al [17,18] discussed differential evolution algorithm for predicting blast-induced ground vibrations, and nine input parameters were taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Zhang

Jin

2018

Mathematical Problems in Engineering

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Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

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Section: Introductionmentioning

confidence: 99%

“…In recent years, a huge number of researchers have performed a train of works on combination of ANNs and principal component analysis [4,6,16,[19][20][21][22][23][24][25][26][27][28][29][30][31]. Nevertheless, the application of Mean Impact Value and Factor Analysis is exactly rare in previous research.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Zhang

Jin

2018

Mathematical Problems in Engineering

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show abstract

“…These days, researchers are using variety of contemporary function approximation methods, i.e. artificial neural network (ANN), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA), etc, to predict the blast induced ground vibration [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%