Within the last years, four methods have been developed to predict the soil profile and its parameters in Sudan. However, a method making such predictions with the required degree of accuracy and consistency has not yet been developed. In this paper, artificial neural networks, ANNs are used in an attempt to compare between these methods by applying them on large zone contains many sites to select a unified method. A large database of actual measured is used to develop and verify the ANN model. The predicted soil profile found by utilizing ANNs is compared between them. The results indicate that ANNs are a useful technique for predicting the soil profile and its parameters when using anyone of the compared methods.
This paper evaluates the effect of soil stiffness on the design parameters of a stiffened raft on an expansive soil of high potential for swelling. The evaluation was based on a finite element program originally developed by Wray [8]. The input soil parameters were measured in the laboratory for a highly expansive soil from Central Sudan. Soil Young’s Modulus was measured at varying moisture conditions using the unconfined compression test. The effects of soil modulus on moments, shear forces and differential deflection were investigated for a case study of a light residential building founded on the tested soil. Evaluation was made using different values of measured and assumed heave and end penetration. Results showed that the modulus of elasticity decreased as water content of the soil increased. The determined results of the finite element program suggested that, generally, the magnitude of longitudinal, traverse moments, shear forces, and differential deflection increased with increase in the soil modulus of elasticity and that the heave values affected the results.
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