A supervised artificial neural network representational model based prediction of contact pressure and bulk density

Hamid Taghavifar

Abstract


Artificial neural networks are fitting replacements to traditional statistical modeling techniques in scientific disciplines. The attempt was to find a supervised feed-forward artificial neural network (ANN) trained by back propagation algorithms to predict contact pressure and bulk density from readily available data obtained from experiments conducted in soil bin facility and a single wheel-tester. A total of 648 samples were used for training, validating and testing the neural networks. Evaluating a three layered architecture (i.e. one hidden layer) with a four layered one (i.e. two hidden layers), the optimal topology to yield better performance on the criteria of lower root mean squared error (RMSE), T value and coefficient of determination (R2) was a four-layered one. Then among 2 hidden layers, each of layers was increased from 0 to 20 to realize the best number of neurons by the best performance. It was divulged that a 6-12-10-2 provided the best performance of RMSE and T were 0.027, 0.977, and R2 for bulk density and contact pressure were 0.9950 and 0.9982, respectively, and that a proper trained ANN could model and predict output variable wherein mathematical and statistical methods may fail to have the equal efficiency owing to unidentified behavior of soil due to its unknown nature. 


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References


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