Mayura

Application of artificial neural networks in predicting sub-base cbr values

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  • Mayura Yeole

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Establishing a realistic working profile of soil properties has been, and is still, one of the most challenging problems facing Geo-technical engineers, especially for CBR results. In the present study a neural-network approach is used to tackle this problem. Source data of a series of California Bearing Ratio Tests (CBR) performed at the Laboratory and Geo-technical Experimental Site. This will be useful for training and testing an artificial neural network. The developed neural network will be showing the prediction of CBR values of the site studied. Data are then generated for constructing the profiles of the CBR values using the trained neural network. This study might be useful for the future as this process will reduce the work on procedure and graphical calculations.


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