The Artificial Neural Network Design Procedure
The design and deployment of the Artificial Neural Network (ANN) for
this research work was divided into six (6) major stages, namely: data
acquisition stage; feature selection and data normalization stage, ANN
architecture optimization stage, the ANN algorithm optimization stage,
ANN initialization and training stage, testing, validation and
deployment stage. Feed Forward neural network with Levenberg-Marquardt
back propagation ANN model of MATLAB training was used for the
computation of data and to determine the best model. The neural network
tool box of MATLAB was used for necessary computation required for the
development of the models. The Coefficient of Correlation (R) and the
Root Mean Square Error (RMSE), were employed to determine the degree of
correlation between the target of the soft computing models and their
eventual outputs. The six input variables were cement (%), Rice Husk
Ash (RHA) (%), Liquid Limit (LL) (%), Plasticity Index (PI) (%),
Maximum Dry Density (MDD) (Kg/m3) , and OMC (%),
while CBR Unsoaked (%) and CBR Soaked (%) were the output variables.
From the experimental results, 322 set of soil data were obtained, the
data were subdivided into 70% for training, 15% for testing and 15%
for validation. Table 1 shows details of the components of the ANN
model.
Table 1: Details of components of the ANN model.