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)
1. Karstic water exploration using the Schlumberger VES and dipole–dipole resistivity profiling surveys in the Tepal area, west of Shahrood, Iran
Volume 5, Issue 1 , Winter 2014, Pages 1-12
Abstract
The need for clean groundwater resources to have sustainable development in a country is undoubted. Due to the importance and high quality of karstic waters in supplying water in Iran ... Read More
2. Enhancing magnetic signals in unexploded ordnances (UXO) detection based on edge-preserved stable downward continuation method
Volume 5, Issue 1 , Winter 2014, Pages 13-24
Abstract
This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (UXO). The magnetic anomalies ... Read More
3. A laboratory study of rock properties affecting the penetration rate of pneumatic top hammer drills
Volume 5, Issue 1 , Winter 2014, Pages 25-34
Abstract
In this paper, after collecting the rock samples from eight mines and one high way slope, the tests for determination of dry density, Uniaxial Compressive Strength, tensile Strength ... Read More
4. Magnitude of vibration triggering component determines safety of structures
Volume 5, Issue 1 , Winter 2014, Pages 35-46
Abstract
Transmission of blast waves is a complex phenomenon and the characteristics vary with blast design parameters and geo-technical properties of medium. Frequency of vibration and triggering ... Read More
5. A parametric model for predicting cut point of hydraulic classifiers
Volume 5, Issue 1 , Winter 2014, Pages 47-54
Abstract
A new parametric model was developed for predicting cut point of hydraulic classifiers. The model directly uses operating parameters including pulp flowrate, feed particle size characteristics, ... Read More
6. Prediction of recovery of gold thiosulfate on activated carbon using artificial neural networks
Volume 5, Issue 1 , Winter 2014, Pages 55-66