Volume 15 (2024)
Volume 14 (2023)
Volume 13 (2022)
Volume 12 (2021)
Volume 11 (2020)
Volume 10 (2019)
Volume 9 (2018)
Volume 8 (2017)
Volume 7 (2016)
Volume 6 (2015)
Volume 5 (2014)
Volume 4 (2013)
Volume 3 (2012)
Volume 2 (2011)
Volume 1 (2010)
Exploration
Prediction of Iron Ore Grade using Artificial Neural Network, Computational Method, and Geo-statistical Technique at El-Gezera Area, Western Desert, Egypt

Ashraf Ismael; Abdelrahem Khalefa Embaby; Faissal Ali; Hussin Farag; Sayed Gomaa; Mohamed Elwageeh; Bahaa Mousa

Volume 15, Issue 3 , May 2024, , Pages 889-905

https://doi.org/10.22044/jme.2024.13879.2581

Abstract
  The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation ...  Read More

Exploration
A Comparative Analysis of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) Data-driven Models for Prospecting Porphyry Cu Mineralization; Case Study of Shahr-e-Babak Area, Kerman Province, SE Iran

Bashir Shokouh Saljoughi; Ardeshir Hezarkhani

Volume 15, Issue 2 , April 2024, , Pages 761-790

https://doi.org/10.22044/jme.2023.13852.2573

Abstract
  The porphyry Cu-mineralization potential area studied in this article is located in the southern section of the Central Iranian volcano–sedimentary complex, contains large number of mineral deposits, and occurrences that are currently facing a shortage of resources. Therefore, prospecting potential ...  Read More

Rock Mechanics
Performance Prediction of a Hard Rock TBM using Statistical and Artificial Intelligence Methods

Alireza Afradi; Arash Ebrahimabadi; Mansour Hedayatzadeh

Volume 15, Issue 1 , January 2024, , Pages 323-343

https://doi.org/10.22044/jme.2023.13370.2460

Abstract
  Tunnel Boring Machines (TBMs) are extensively used to excavate underground spaces in civil and tunneling projects. An accurate evaluation of their penetration rate is the key factor for the TBM performance prediction. In this study, artificial intelligence methods are used to predict the TBM penetration ...  Read More

Performance Comparison of Particle Swarm Optimization and Genetic Algorithm for Back-analysis of Soil Layer Geotechnical Parameters

Leila Nikakhtar; Shokroallah Zare; Hossein Mirzaei

Volume 14, Issue 1 , January 2023, , Pages 217-232

https://doi.org/10.22044/jme.2023.12452.2260

Abstract
  Surface settlement induced by tunneling is one of the most crucial problems in urban environments. Hence, accurate prediction of soil geotechnical properties is an important prerequisite in the minimization of it. In this research work, the amount of surface settlement is predicted using three-dimensional ...  Read More

Improvement of Small-Scale Dolomite Blasting Productivity: Comparison of Existing Empirical Models with Image Analysis Software and Artificial Neural Network Models

Blessing Olamide Taiwo

Volume 13, Issue 3 , July 2022, , Pages 627-641

https://doi.org/10.22044/jme.2022.11771.2169

Abstract
  Assessment of blast results is a significant approach for the improvement of mining operations. The different procedures for investigating rock fragmentation have their limitations, causing different variation prediction errors. Thus every technique is site-explicit, and applicable for a few explicit ...  Read More

Developing New Models for Flyrock Distance Assessment in Open-Pit Mines

J. Shakeri; H. Amini Khoshalan; H. Dehghani; M. Bascompta; K. Onyelowe

Volume 13, Issue 2 , April 2022, , Pages 375-389

https://doi.org/10.22044/jme.2022.11805.2170

Abstract
  In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis ...  Read More

Mine Economic and Management
Prediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks

R. Bastami; A. Aghajani Bazzazi; H. Hamidian Shoormasti; K. Ahangari

Volume 11, Issue 1 , January 2020, , Pages 281-300

https://doi.org/10.22044/jme.2019.9027.1790

Abstract
  The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone ...  Read More

A comparison between knowledge-driven fuzzy and data-driven artificial neural network approaches for prospecting porphyry Cu mineralization; a case study of Shahr-e-Babak area, Kerman Province, SE Iran

B. Shokouh Saljoughi; A. Hezarkhani; E. Farahbakhsh

Volume 9, Issue 4 , October 2018, , Pages 917-940

https://doi.org/10.22044/jme.2018.6752.1495

Abstract
  The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become ...  Read More

Estimation of Cadmium and Uranium in a stream sediment from Eshtehard region in Iran using an Artificial Neural Network

F. Razavi Rad; F. Mohammad Torab; A. Abdollahzadeh

Volume 7, Issue 1 , January 2016, , Pages 97-107

https://doi.org/10.22044/jme.2016.500

Abstract
  Considering the importance of Cd and U as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the Eshtehard region in Iran by means of a developed artificial neural network (ANN) model. The forward selection (FS) method is used to select ...  Read More

Application of artificial neural network and genetic algorithm to modelling the groundwater inflow to an advancing open pit mine

S. Bahrami; F. Doulati Ardejani

Volume 6, Issue 1 , January 2015, , Pages 21-30

https://doi.org/10.22044/jme.2014.330

Abstract
  In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius ...  Read More

Prediction of recovery of gold thiosulfate on activated carbon using artificial neural networks

Saeed Alishahi; Ahmad Darban; Mahmood Abdollahi

Volume 5, Issue 1 , January 2014, , Pages 55-66

https://doi.org/10.22044/jme.2014.271

Abstract
  Since a high toxicity of cyanide which use as a reagent in the gold processing plant, thiosulfate has been recognized as a environmental friendly reagent for leaching of gold from ore. After gold leaching process it's important for recovery of gold from solution using adsorption or extraction methods, ...  Read More