Predictive Model of Rock Fragmentation Using the Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) to Estimate Fragmentation Size in Open Pit Mining

This publication has 11 references indexed in Scilit:

• Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting
  Engineering with Computers, 2018
• Fragment Size Distribution of Blasted Rock Mass
  IOP Conference Series: Earth and Environmental Science, 2017
• Developing GPR model for forecasting the rock fragmentation in surface mines
  Engineering with Computers, 2017
• Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review
  Geotechnical and Geological Engineering, 2017
• Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm
  Engineering with Computers, 2017
• Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system
  Environmental Earth Sciences, 2016
• Effect of Geological Condition on Degree of Fragmentation in a Simpang Pulai Marble Quarry
  Procedia Chemistry, 2016
• Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike
  Applied Soft Computing, 2015
• Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm
  Bulletin of Engineering Geology and the Environment, 2015
• Blastability evaluation for rock mass fragmentation in Iran central iron ore mines
  International Journal of Mining Science and Technology, 2015