Related references
Note: Only part of the references are listed.A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam
Hoang Nguyen et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques
Xuan-Nam Bui et al.
NATURAL RESOURCES RESEARCH (2020)
Prediction of Flyrock in Mine Blasting: A New Computational Intelligence Approach
Hima Nikafshan Rad et al.
NATURAL RESOURCES RESEARCH (2020)
Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network
Hoang Nguyen et al.
NATURAL RESOURCES RESEARCH (2020)
Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost
Xiliang Zhang et al.
NATURAL RESOURCES RESEARCH (2020)
Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO
Haiqing Yang et al.
NATURAL RESOURCES RESEARCH (2020)
A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network
Yonghui Shang et al.
NATURAL RESOURCES RESEARCH (2020)
Three hybrid intelligent models in estimating flyrock distance resulting from blasting
Mohammadreza Koopialipoor et al.
ENGINEERING WITH COMPUTERS (2019)
Analysis of ground vibration risk on mine infrastructures: integrating fuzzy slack-based measure model and failure effects analysis
E. Bakhtavar et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2019)
Constitutive modelling of cemented paste backfill: A data-mining approach
Chongchong Qi et al.
CONSTRUCTION AND BUILDING MATERIALS (2019)
Towards Intelligent Mining for Backfill: A genetic programming-based method for strength forecasting of cemented paste backfill
Chongchong Qi et al.
MINERALS ENGINEERING (2019)
Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest
Hoang Nguyen et al.
NATURAL RESOURCES RESEARCH (2019)
Ensemble Learning Regression for Estimating Unconfined Compressive Strength of Cemented Paste Backfill
Xiang Lu et al.
IEEE ACCESS (2019)
Feasibility of ICA in approximating ground vibration resulting from mine blasting
Danial Jahed Armaghani et al.
NEURAL COMPUTING & APPLICATIONS (2018)
Developing a least squares support vector machine for estimating the blast-induced flyrock
Hima Nikafshan Rad et al.
ENGINEERING WITH COMPUTERS (2018)
Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system
M. Hasanipanah et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2018)
Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm
Katayoun Behzadafshar et al.
ENGINEERING COMPUTATIONS (2018)
Data-driven modelling of the flocculation process on mineral processing tailings treatment
Chongchong Qi et al.
JOURNAL OF CLEANER PRODUCTION (2018)
A Risk-Based Technique to Analyze Flyrock Results Through Rock Engineering System
Mahdi Hasanipanah et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2018)
Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model
Mahdi Hasanipanah et al.
ENGINEERING WITH COMPUTERS (2017)
Forecasting blast-induced ground vibration developing a CART model
Mahdi Hasanipanah et al.
ENGINEERING WITH COMPUTERS (2017)
Development of a precise model for prediction of blast-induced flyrock using regression tree technique
Mahdi Hasanipanah et al.
ENVIRONMENTAL EARTH SCIENCES (2017)
Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
Mahdi Hasanipanah et al.
NEURAL COMPUTING & APPLICATIONS (2017)
A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model
Azam Shahnazar et al.
ENVIRONMENTAL EARTH SCIENCES (2017)
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
Danial Jahed Armaghani et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2017)
Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods
D. Jahed Armaghani et al.
ENGINEERING WITH COMPUTERS (2016)
Several non-linear models in estimating air-overpressure resulting from mine blasting
Mahdi Hasanipanah et al.
ENGINEERING WITH COMPUTERS (2016)
Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety
M. Yari et al.
NEURAL COMPUTING & APPLICATIONS (2016)
Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling
Mahdi Hasanipanah et al.
ENGINEERING WITH COMPUTERS (2016)
Extreme learning machine assessment for estimating sediment transport in open channels
Isa Ebtehaj et al.
ENGINEERING WITH COMPUTERS (2016)
Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique
Manoj Khandelwal et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2016)
Application of two intelligent systems in predicting environmental impacts of quarry blasting
Danial Jahed Armaghani et al.
ARABIAN JOURNAL OF GEOSCIENCES (2015)
Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles
Ehsan Momeni et al.
Earth Sciences Research Journal (2015)
Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach
Mohsen Hajihassani et al.
ENVIRONMENTAL EARTH SCIENCES (2015)
Outlier-robust extreme learning machine for regression problems
Kai Zhang et al.
NEUROCOMPUTING (2015)
An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on Main Range granite
Danial Jahed Armaghani et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2015)
Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
Mohsen Hajihassani et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2015)
Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS
Ratnesh Trivedi et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2015)
Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation
Ebrahim Ghasemi et al.
ARABIAN JOURNAL OF GEOSCIENCES (2014)
Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization
D. Jahed Armaghani et al.
ARABIAN JOURNAL OF GEOSCIENCES (2014)
Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network
Danial Jahed Armaghani et al.
MEASUREMENT (2014)
Developing a mathematical assessment model for blasting patterns management: Sungun copper mine
M. Yari et al.
JOURNAL OF CENTRAL SOUTH UNIVERSITY (2014)
Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network
Masoud Monjezi et al.
NEURAL COMPUTING & APPLICATIONS (2013)
Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach
M. Monjezi et al.
ARABIAN JOURNAL OF GEOSCIENCES (2012)
Development of a fuzzy model to predict flyrock in surface mining
M. Rezaei et al.
SAFETY SCIENCE (2011)
ALTERNATING DIRECTION ALGORITHMS FOR l(1)-PROBLEMS IN COMPRESSIVE SENSING
Junfeng Yang et al.
SIAM JOURNAL ON SCIENTIFIC COMPUTING (2011)
Stochastic modeling of accident risks associated with an underground coal mine in Turkey
Mehmet Sari et al.
SAFETY SCIENCE (2009)
Evaluation of blast-induced ground vibration predictors
Manoj Khandelwal et al.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2007)
Flyrock phenomena and area security in blasting-related accidents
V Kecojevic et al.
SAFETY SCIENCE (2005)
Blasting injuries in surface mining with emphasis on flyrock and blast area security
TS Bajpayee et al.
JOURNAL OF SAFETY RESEARCH (2004)
Share Paper
