Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that using ANN gave a very high correlation factor associated with the results obtained from Terzagih’s equation, besides little computation time needed compared with computation time needed when applying Terzagih’s equation.
In this study, stabilization of expansive soils using waste materials namely; Cement Kiln Dust (CKD), and waste plastic bottles (WPB) was experimentally investigated. Using CKD and WPB are exponentially increasing day by day, due to their capability to solve both environmental and geotechnical problems successfully. Expansive soils were collected from locations with a wide range of plasticity index (PI) (15 - 27) and liquid limit (LL) (35% - 64%). Stabilizer percentages were varied from 0% to 20%, and curing durations for CKD cases were 7 and 28 days. Results showed the best percentages of CKD and WPB are 12% of each one respectively. LL, plastic limit (PL), and swelling percent (SP) loss were observed, which are 46%, 55%, and 96% respec
... Show MoreThis study comprised three traverses extending parallel through the Northern, Central and Southern Mahmudiya districts, and perpendicular to the course of the Euphrates River. They were identified to collect (15) soil samples and some water samples as distributed within the land cover classes of the study area. Those classes were determined by visual interpretation and supervised classification for Landsat (TM) images obtained in August/2007. The digital classification was based on Maximum Likelihood method using six spectral bands excluding the thermal band. Chemical and physical laboratory analysis for the soil characteristics was performed to determine the types of land degradation in the study area.
The results showed that the hig
This qualitative study was conducted on eight types of commercial baking yeast which available in local markets to estimate their fermentation activity as affecting the Bread industry and the impact of the salt added to DoughLeavening, The results showed a great variation in the fermentation capacity of yeast samples (their role in swelling the dough), most notably the sample value Y3 and least sample Y7 and reached 80% and 20% respectively, and the value of Leavening by using the two types of yeast with addition of three levels of salt (0 , 1 and 2%) have 20.0 , 19.7 and 15.7 of the sample Y3, compared with 10.5 , 10.3 and 8.8 of the sample Y7 for each of the levels of salt respectively, reflect
... Show MoreBearing capacity of a concrete pile in fine grained cohesive soils is affected by the degree of saturation of the surrounding soil through the contribution of the matric suction. In addition, the embedded depth and the roughness of the concrete pile surface (expressed as British Pendulum Number BPN) also have their contribution to the shear strength of the concrete pile, consequently its bearing capacity. Herein, relationships among degree of saturation, pile depth, and surface roughness, were proposed as a mathematical model expressed as an equation where the shear strength of a pile can be predicted in terms of degree of saturation, depth, and BPN. Rel
... Show MoreANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data
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