The statistical analysis of the sand grain size distribution of Al- Ubay-lah barchan dunes, Northwestern Ar-Rub-Alkhali desert, Saudi Arabia

GeoJournal ◽  
1986 ◽  
Vol 13 (2) ◽  
pp. 103-109 ◽  
Author(s):  
YahyaMohammedSheikh Abolkhair
2018 ◽  
Vol 8 (1) ◽  
pp. 2447-2451 ◽  
Author(s):  
M. A. M. Alghamdi

Surficial sediments with low radon content are desirable materials in construction applications. In this study, the relationship between grain size and radon content was investigated in sediments collected from seven sites in Wadi Arar, Saudi Arabia, with the intent of determining whether grain size analysis could be used for rapidly assessing the suitability of potential construction materials. Thirty-five samples were collected (five per site) and the grain size distribution was determined using sieves. Radon contents were measured on composite samples with a RAD7 radon detector. Among the sediment types (gravel, coarse sand, medium sand, fine sand, and silt and clay), the best linear correlations between grain size and radon contents were found for the coarse sand (negative slope, r=0.82) and fine sand (positive slope, r=0.78). Polynomial relationships were also tested. A fourth-degree polynomial equation effectively described the correlation between grain size and radon content (R2 = 0.933). As shown by this model, the highest correlations with radon contents were detected at grain sizes smaller than 2.0 mm. Thus, grain size may be useful for preliminary site assessment work.


2017 ◽  
Vol S (1) ◽  
pp. 37-45
Author(s):  
Wan Hanna Melini Wan Mohtar ◽  
◽  
Siti Aminah Bassa Nawang ◽  
Mohd Noor Shafique Rahman

2018 ◽  
Vol 284 ◽  
pp. 431-435 ◽  
Author(s):  
Alexey V. Stolbovsky ◽  
Elena Farafontova

Analysis of histograms of grain size distribution of materials nanostructured by severe plastic deformation has been carried out using statistical analysis methods. It has been established that in materials with quite homogeneous nanostructure, the fitting of histograms of grain size distribution by using a logarithmic standard distribution is not accurate enough. It is proposed to compensate for the observed imprecision by including into the model the additional component – normal distribution. It is shown that this approach is applicable to nanostructured materials with both the deformation-origin nanostructure and the grain structure formed during annealing.


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