Environment
B.G. Mousa; Marzouk Mohamed Aly Abdelhamid; Mohamed Elgharib Gomah; M. M. Zaki; H.A. Farag; W.M. Draz; Remonda Dimian; Yousef A. Al-Masnay
Abstract
Rock damage due to severe environmental conditions may affect durability over time. Therefore, this study aims to investigate the degradation of igneous rocks triggered by freezing-thawing weathering and to predict their loss of integrity. Four types of igneous rocks were collected from the Saint Katherine ...
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Rock damage due to severe environmental conditions may affect durability over time. Therefore, this study aims to investigate the degradation of igneous rocks triggered by freezing-thawing weathering and to predict their loss of integrity. Four types of igneous rocks were collected from the Saint Katherine mountain area, Sinai, Egypt, and subjected to 100 cycles of the freeze-thaw method. The land surface temperature variation was analyzed using remote sensing data as an indicator for selecting the freeze-thaw temperature for our experiments. The physical and mechanical characteristics of the studied rocks, such as dry bulk density, effective porosity, ultrasound pulse velocity, abrasion loss, unconfined compressive strength, and point load strength, were measured after 0, 20, 40, 60, 80, and 100 cycles of the freeze-thaw process and evaluated using deterioration ratios and statistical models. Exponential and linear decay function models were established to statistically assess the integrity loss rate of the mechanical strength properties of the selected rocks. Two critical factors, decay constant and half-life, were derived from the studied models and used to predict rock durability. This evaluation suggests that the behavior of rock decay is very similar across different types of igneous rocks, exhibiting a strong correlation and high precision. Therefore, these models are reliable and valid for predicting the durability of the studied igneous rocks. Results also showed that the tested igneous rocks can be used as construction stones in regions exposed to repeated cyclic freezing-thawing weathering and harsh environmental conditions over long periods without disintegration.
Exploration
Ashraf Ismael; Abdelrahem Khalefa Embaby; Faissal Ali; Hussin Farag; Sayed Gomaa; Mohamed Elwageeh; Bahaa Mousa
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 ...
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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 are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO2%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.
