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Published:
Mar 31, 2016DOI:
10.4314/njt.v35i2.5Keywords:
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Main Article Content
ESTIMATION OF SHEAR STRENGTH PARAMETERS OF LATERITIC SOILS USING ARTIFICIAL NEURAL NETWORK
Abstract
This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of lateritic soils in central and southern areas of Delta State using artificial neural network modeling technique. The application of these models will help reduce cost and time in acquiring geotechnical data needed for both design and construction in the study area. A total of eighty-three (83) soil samples were collected from various locations in Delta State of Nigeria. The geotechnical soil properties were determined in accordance with British Standards. The range of the angle of internal friction and cohesion obtained from the tests are 2 to 43 degrees and 3 to 82 kN/m2 respectively. The optimum artificial neural network architecture network was found to be 3-9-1, that is three inputs, nine hidden layer nodes, and one output node for cohesion. While, the angle of friction had an optimal network geometry of 3-11-1, that is three inputs, eleven hidden layer nodes, and one output node. The results of the coefficient of determination and root mean square showed that the artificial neural network method outperforms some selected empirical formulae in the prediction of shear strength parameters.
