Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods
Published 2015 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods
Authors
Keywords
Blasting, Flyrock, Empirical graph, Artificial neural network, Adaptive neuro-fuzzy inference system
Journal
ENGINEERING WITH COMPUTERS
Volume 32, Issue 1, Pages 109-121
Publisher
Springer Nature
Online
2015-03-19
DOI
10.1007/s00366-015-0402-5
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
- (2014) M. Hajihassani et al. APPLIED ACOUSTICS
- Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization
- (2013) D. Jahed Armaghani et al. Arabian Journal of Geosciences
- Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation
- (2012) Ebrahim Ghasemi et al. Arabian Journal of Geosciences
- Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines
- (2012) Ebrahim Ghasemi et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Evaluation of effect of blast design parameters on flyrock using artificial neural networks
- (2012) M. Monjezi et al. NEURAL COMPUTING & APPLICATIONS
- Estimation of elastic constant of rocks using an ANFIS approach
- (2011) Rajesh Singh et al. APPLIED SOFT COMPUTING
- Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks
- (2011) T. N. Singh et al. ENGINEERING WITH COMPUTERS
- Prediction of flyrock trajectories for forensic applications using ballistic flight equations
- (2011) Saša Stojadinović et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Evaluation of flyrock phenomenon due to blasting operation by support vector machine
- (2011) Hasel Amini et al. NEURAL COMPUTING & APPLICATIONS
- Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach
- (2010) M. Monjezi et al. Arabian Journal of Geosciences
- New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization
- (2010) Adem Kalinli et al. ENGINEERING GEOLOGY
- Prediction of rock fragmentation due to blasting using artificial neural network
- (2010) A. Bahrami et al. ENGINEERING WITH COMPUTERS
- Development of a fuzzy model to predict flyrock in surface mining
- (2010) M. Rezaei et al. SAFETY SCIENCE
- Prediction and controlling of flyrock in blasting operation using artificial neural network
- (2009) M. Monjezi et al. Arabian Journal of Geosciences
- Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
- (2009) S. Yagiz et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Application of soft computing to predict blast-induced ground vibration
- (2009) Manoj Khandelwal et al. ENGINEERING WITH COMPUTERS
- Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks
- (2009) M. Monjezi et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Discussion on the paper by H. Gullu and E. Ercelebi “A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey (in press)”
- (2007) H. Sonmez et al. ENGINEERING GEOLOGY
- Prediction of uniaxial compressive strength of sandstones using petrography-based models
- (2007) K. Zorlu et al. ENGINEERING GEOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started