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)
Rock Mechanics
Application of machine learning techniques in slope stability analysis: A comprehensive overview

Arun Kumar Sahoo; Debi Prasad Tripathy; Singam Jayanthu

Articles in Press, Accepted Manuscript, Available Online from 23 January 2024

https://doi.org/10.22044/jme.2024.13881.2583

Abstract
  The mining industry needs to accept new-age autonomous technologies and intelligent systems to stay up with the modernization of technology, to benefit the shake of investors and stakeholders, and most significantly, for the nation, and to protect health and safety. An essential part of geo-technical ...  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

Financial Risk Management Prediction of Mining and Industrial Projects using Combination of Artificial Intelligence and Simulation Methods

Sirvan Moradi; Seyed Davoud Mohammadi; Abbas Aghajani Bazzazi; Ali Aali Anvari; Ava Osmanpour

Volume 13, Issue 4 , October 2022, , Pages 1211-1223

https://doi.org/10.22044/jme.2022.12425.2255

Abstract
  Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since ...  Read More

Exploration
A Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data

N. Mahvash Mohammadi; A. Hezarkhani

Volume 11, Issue 1 , January 2020, , Pages 49-61

https://doi.org/10.22044/jme.2019.7956.1664

Abstract
  Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...  Read More

Rock Mechanics
Prediction of ultimate strength of shale using artificial neural network

S. Moshrefi; K. Shahriar; A. Ramezanzadeh; K. Goshtasbi

Volume 9, Issue 1 , January 2018, , Pages 91-105

https://doi.org/10.22044/jme.2017.5790.1390

Abstract
  A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research ...  Read More

Support vector regression for prediction of gas reservoirs permeability

R. Gholami; A. Moradzadeh

Volume 2, Issue 1 , January 2011

https://doi.org/10.22044/jme.2012.18

Abstract
  Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are ...  Read More