Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio of Soils
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2015, Vol 5, Issue 1
Abstract
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using 60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between the predicted values and observed values of outputs for training and testing process, from the graph it is found that all the points are close to equality line, indicating predicted values are close to observed values.
Authors and Affiliations
Phani Kumar Vaddi , Ch. Manjula , P. Poornima
Keywords
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