Development of GA-based models for simulating the ground vibration in mine blasting

Tian, Erlin; Zhang, Jianwei; Tehrani, Mehran Soltani; Aravindhan, Surendar; Ibatova, Aygul Z.

doi:10.1007/s00366-018-0635-1

Cited by 15 publications

(4 citation statements)

References 34 publications

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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%

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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Section: Reference Methods Resultsmentioning

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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“…It is difficult for the prediction equation to represent the relationships among the parameters [23]. Therefore, intelligent algorithms [24][25][26], such as particle swarm algorithms [27,28] and BP neural networks [29][30][31][32], are also used to predict the peak velocity and frequency of blasting vibration.…”

Section: Introductionmentioning

confidence: 99%

Full Waveform Prediction of Blasting Vibration Using Deep Learning

Wang

Zheng

et al. 2022

Sustainability

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Blasting vibration could cause dynamic instability of rock masses within a critical steady state. To control the blasting vibration, it is necessary to understand the complete dynamic response process of the rock masses under the blasting vibration. The Long Short-Term Memory (LSTM) technique uses blast monitoring data to predict the full waveform of the blast vibration. Based on the LSTM, a new full waveform prediction model is proposed in this study. To verify the feasibility of the proposed model, the sample data were constructed using the well-known linear blast wave superposition prediction formula. The full waveform prediction model is trained and the predicted waveform and the actual waveform are then evaluated and compared. The loss function is calculated and discussed, which verifies the feasibility of the prediction method. In addition to the numerical research, the actual blasting vibration data are also used for verification. The parameters, such as sequence size, training algorithm, and some hidden layer nodes, are discussed and optimized. The results show that the proposed full waveform prediction model based on LSTM can predict the full blasting waveform. This study provides a new idea for the prediction and control of blasting vibration.

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“…Hasanipanah et al (2015), Dindarloo (2015), and Masmoudi et al (2020) practiced support vector machines and machine learning methodology to investigate environmental issues. In addition, genetic algorithms have been widely used to predict blast-induced ground vibrations (e.g., Faradonbeh et al 2016;Singh et al 2016;Azimi et al 2019;Tian et al 2019). New prediction methods have provided some new perspectives on ground vibration forecasting.…”

Section: Introductionmentioning

confidence: 99%

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

Hudaverdi

Akyildiz

2021

Arab J Geosci

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This research focuses on blast-induced structural damage and human response to vibrations. Frequency is one of the important parameters that affect damage probability and human perception. The first and primary target of the study is to predict ground vibration and frequency simultaneously. A feed-forward back propagation neural network was created for modeling. Three operational parameters and vibration measurement distance were used as input parameters. The developed model was compared to the regression equations. Different error measures were considered to perform a vigorous model validation. Ten different blasting level charts were used to estimate structural damage probability. The regulations from North America, Europe, and Southern Hemisphere were examined and compared. Variation in the vibration regulations was also discussed. Some suggestions were put forward for the development of an international regulation. Two practical charts were created to predict human response to vibrations. A specific graph, which was proposed for pile driving induced vibrations, was considered for evaluation of blast vibrations. The developed soft computing framework can forecast probability of structural damage and human perception of vibrations effectively.

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Development of GA-based models for simulating the ground vibration in mine blasting

Cited by 15 publications

References 34 publications

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

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

Full Waveform Prediction of Blasting Vibration Using Deep Learning

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

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