A SVR-GWO technique to minimize flyrock distance resulting from blasting

2020 ◽  
Vol 79 (8) ◽  
pp. 4369-4385 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Maziyar Bahri ◽  
Mahdi Hasanipanah ◽  
M. M. Tahir
Keyword(s):  
Flyrock Distance

scholarly journals Intelligence Prediction of Some Selected Environmental Issues of Blasting: A Review

2020 ◽  
Vol 14 (1) ◽  
pp. 298-308
Author(s):  
Bhatawdekar Ramesh Murlidhar ◽  
Danial Jahed Armaghani ◽  
Edy Tonnizam Mohamad
Keyword(s):  
Environmental Effects ◽  
Hard Rock ◽  
Ground Vibration ◽  
Environmental Issues ◽  
Imperialist Competitive Algorithm ◽  
Rock Fragmentation ◽  
Future Research ◽  
Computational Techniques ◽  
Flyrock Distance ◽  
Artificial Neural Network Ann

Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance.

Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance

2019 ◽  
Vol 29 (2) ◽  
pp. 625-639 ◽  
Author(s):  
Jian Zhou ◽  
Mohammadreza Koopialipoor ◽  
Bhatawdekar Ramesh Murlidhar ◽  
Seyed Alireza Fatemi ◽  
M. M. Tahir ◽  
...  
Keyword(s):  
Flyrock Distance ◽  
Intelligent Methods

Regression and Fitting Analysis of Flyrock Prediction Formulae for Loosening Blasting

2013 ◽  
Vol 405-408 ◽  
pp. 2346-2350
Author(s):  
Jian Jun Shi ◽  
Hua Ming An ◽  
Chun Ping Wu
Keyword(s):  
Influence Factors ◽  
Calculated Data ◽  
Present Situation ◽  
Financial Resources ◽  
Prediction Formula ◽  
Predictive Values ◽  
Flyrock Distance ◽  
Mechanics Analysis ◽  
Complex Influence ◽  
Flyrock Prediction

With so many complex influence factors of blasting flyrock, there is no critical formula for prediction the flying distance of blasting flyrock which was adapted by most of the scholars. Widely existing influential prediction formulas are mainly aimed at ordinary blasting technology, using the statistical law or mechanics analysis to get the prediction. The calculated data for flyrock distance are different distinctly. Predictive values are different largely between various formulas. Moreover, when loose blasting, the values predicted by those formulas will be larger than the actual data. This situation wastes lots of human and financial resources for blasting alert. In view of the present situation, the field experiment of loosening blasting was carried and the impacts of blasting parameters to the flyrock distance in loosening blasting were considered respectively. Some blasting parameters were regressed to get a prediction formula. The correlation analysis indicates that the formula for loosening blasting is good for flying distance.

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

2016 ◽  
Vol 49 (9) ◽  
pp. 3631-3641 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Amir Mahdiyar ◽  
Mahdi Hasanipanah ◽  
Roohollah Shirani Faradonbeh ◽  
Manoj Khandelwal ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Flyrock Distance ◽  
Quarry Blasting
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