In this study, mortars containing locally available natural pozzolan (NP) in Almadinah Almunawara, Kingdom of Saudi Arabia, were investigated as a partial substitute for sand or cement in mortars and silica fume (SF). The benefit of using local NP powder as a replacement for cement is that it reduces the carbon dioxide emission during the cement manufacturing process, whereas the benefit of using local NP as fine aggregates is that it reduces the density of the produced mortars and improves its properties because of its pozzolanic effect. Because of these reasons, there is a need to develop an effective predictive model to estimate the compressive strength of mortars with partial replacement of cement or sand with NP and with SF as a replacement for cement at 28 days. Data of 68 cubic specimens of 50 mm were established through experimental work with other researchers, and they were chosen to create a database for the proposed model. There were three input parameters: a) level of partial substitution of cement with NP powder, b) level of partial substitution of sand with NP, and c) level of partial substitution of cement with SF. The output parameter was compressive strength. Best correlations were obtained between the compressive strength and sand replacement with NP. To predict the compressive strengths of cement mortars containing NP and SF, multivariate regression models were proposed and compared to find the best one. It was concluded that the full quadratic model was the best model with highest correlation when compared with other proposed models.
This research demonstrates the results of an investigation into the California bearing ratio (CBR) of granular soils from Qassim region, Saudi Arabia, using multilinear regression (MLR), pure quadratic (PQ) models, and gene expression programming (GEP) methods utilized to develop mathematical models for estimating the CBR based on basic soil index properties. In this study, samples were collected from different borrowing pits in the Qassim area. Forty-three samples of soil were taken and transferred to a laboratory for examination. Seven multilinear regressions and seven PQ models were investigated, while four GEP models were made. The selection of each model variable depends on soil indices, grouping into grain size distribution, Atterberg limits, and compaction parameters. The results of this analysis showed that the PQ model had a higher accuracy [coefficient of determination (R2) = 0.89, root mean square error (RMSE) = 16.006, uncertainty (U95) = 16.17, and reliability = 57%] than the multilinear regression model, which has a lower accuracy model [R2 = 0.811, RMSE = 20.791, U95 = 15.569, and reliability = 51%]. The best GEP model yields [R2 = 0.776, RMSE = 22.552, U95 = 15.787, and reliability = 53%]. Furthermore, sensitivity analysis was conducted to distinguish the influences of different input variables on CBR; it was found that fines percentage (F200), maximum dry density (MDD), and optimum moisture content (OMC) are the most influential variables.
Piles are used widely for stabilization of landslides. To stabilize a slope settled on bedrock with piles the required factor of safety must be checked, and pile should be designed properly. Piles should be socketed into firm rock to prevent uprooting or overturning .In this research it is aimed to look into the socketed length of pile in bedrock. Therefore the parameters that affect the factor of safety of slope/pile system such as location, length, spacing and diameter of piles are analyzed. The effect of socketed length of pile in rock on pile behavior is investigated by plotting the shear force and bending moment diagrams along pile. The optimal pile position is found to be located slightly upper of the middle of the slope. The minimum socketed length after which the factor of safety will be remained constant is found to be 0.12L where L is pile length.FLAC3D computer code based on finite difference method is used to simulate the slope/pile system.
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