University of Khartoum

Soil Profile Prediction Using Artificial Neural Networks in Sudan

Soil Profile Prediction Using Artificial Neural Networks in Sudan

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Title: Soil Profile Prediction Using Artificial Neural Networks in Sudan
Author: Mohammed Nour, Yassir
Abstract: Artificial Neural Networks (ANNs) are a form of Artificial Intelligence, which are mathematical models, inspired from the brains of certain information-processing characteristics, producing meaningful solutions, which fall beyond the reach of conventional digital computers. In recent years, the use of ANNs has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. In this study,ANNs are used for soil classification prediction in a specified locations at different depths, based on the available site investigation data from a specific area in Sudan. Regarding the large number of the data and considerable variations in soil layers in Sudan, hundred of boreholes were selected for this study . Seven Networks are developed to predict the soil layering in specified locations in Khartoum city.In this study ,area of about 165 square kilometers of Khartoum concentrating on Blue Nile region is considered and the results are then compared with data of actual boreholes to check the ANN model’s validity . The results indicate that Artificial Neural Networks are a useful technique for predicting relationships between the input parameters of the three dimensional coordinates and the resulting soil classification and soil parameters output. So, Artificial Neural Networks can be considered as an effective tool for predicting the soil classification in Khartoum.
URI: http://khartoumspace.uofk.edu/handle/123456789/10157
Date: 2015-05-03


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