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Go to Editorial ManagerPrevious concrete is a special, highly porous concrete used in applications that allow water to pass through it, such as parking areas and park walkways. In this research, three pervious concrete mixes were designed and produced by fixing all amounts of their raw materials (cement, water and gravel) but changing the gravel particle sizes as (4.75-10 mm), (10-14 mm) and (14-20 mm). by adopting 2-dimensional and 3-dimensional techniques, porosity (pore volume), compressive strength and permeability of the selected mixes were investigated. The results showed that the volumetric pore volume (3D porosity) of pervious concrete can be predicted by adopting the Image analysis technique (2D method). In addition, using smaller-sized aggregate (4.75-10 mm) helped in enhancing the pore structure, compressive strength (30% more) and permeability (80% less) of pervious concrete than using the bigger-sized aggregate (14-20 mm). Finally, it was found that the best-fitting line for both 2D and 3D porosities and permeability was an exponential model.
The effect of pore fluid chemistry on the engineering properties of soil in Garmatt-Ali zone of Basrah was investigated. The tested soil is described as silty clay of low plasticity. The pore fluid was altered to include distilled water, raw sewage, and solutions of various salts such calcium carbonate, magnesium sulphate, and calcium chloride. Also, the solutions of salts were used with different concentration (0.25, 0.5, 0.75, 1.0 normality). The prepared samples of soil were tested after different exposure periods. The test program included determination of shear strength characteristics, consolidation characteristics, and Atterberg limits. The changes in shear strength, coefficient of permeability, void ratio – effective stress relationship, and Atterberg limits were recorded with the change in exposure period or the concentration of pore fluid solution. Generally, it was found that there are reductions in the shear strength of soil when the pore fluid is changed from distilled water to solutions of salts or raw sewage. Also it was found that there is a change in the calculated values of permeability, upon changing the type of pore fluid. The coefficient of consolidation for polluted soil was found to be less than that for the reference samples with distilled water.
The studying of fluid flow throughout fracture in the reservoir is one of the most vital subjects attracted much attention from engineers and geologists. In the present paper, the Dual Porosity-Dual Permeability (DPDP) model has been applied to represent the fluid flow within the fractured reservoirs. This work aimed to demonstrate the utility of the fractures in the petroleum reservoir and how could be used the positive effect of these fractures on the productivity as well. The productivity of single-phase fluid flow within the single horizontal fracture, multi horizontal fractures, and inclined fracture with different orientations (20 o , 30 o , 45 o , and 70 o ) have been implemented by using ANSYS- CFX program and compared with the productivity of conventional (without fractures) reservoirs. In addition to, visualize the velocity streamlines within fracture and matrix zones for the DPDP model. To verify this work the comparison has been made with published paper, which studies the fluid flow through fractures, and a good agreement has been obtained with each other. The study indicates that the presence of macro scale fractures in petroleum reservoirs contributes to increasing the total productivity of these reservoirs. Clearly, the productivity index of multi-horizontal fractures domain is more than twice of nonfractured domain. It is also clear that, when comparing the fractured and nonfractured reservoir, the improvement percentage of the productivity index reaches to (71.8) for a single horizontal fracture with 9 ft length. While this percentage would be about (116.88) if the fracture is inclined with 20 o .
The land surface erosion is controlled by multifarious of different parameters, such as slope, soil physical properties (texture, structure, permeability, etc.), rainfall, runoff, and crop cover. However, it is impossible to develop precise simplest mathematical model that can predict the values of land surface soil erosion due to the behavior of controlled parameters. This paper presents the Neural Networks Model for assessing land surface soil erosion as amass per unit area per unit of time. The model derives from the analysis data obtained from available literature and was formulated as linear regression model and back propagation algorithm neural model. Both models were built by correlating firstly five watersheds variables with land surface erosion and secondly ten watershed variables with land surface erosion. The coefficients for independent variables were highly significant for both models. The case of correlating 10- watershed variables with land surface erosion gives R=0.978 & 0.976 for both models which is higher than that for 5- watershed variables. The mean absolute relative error (MARE%) is another procedure that used in order to evaluate the accuracy of the model and The average error % is 0.025 for (5) variables and 0.0064 for (10) variables. Both the supporting practices (P) and the slope length and slope steepness (LS) coefficients have a marked effect on the amount of land surface erosion in the case of 5- watershed variables. The amount of land surface erosion show a high level of sensitivity to the content of fine sand% in soil (FS) watershed variables on The amount of land surface soil erosion.