Abstract:
Soil-wheel interactions as a
phenomenon in which both components are
behaving nonlinearly has been considered a
sophisticated and complex relation to be
modeled. A well-trained artificial neural
networks as a useful tool is widely used in
variety of science and engineering fields.
We inspired to use this facility for
application of some soil-wheel interaction
products since nonlinear and complex
relationships between wheel and soil
necessitate more precise and reliable
calculations. A 2-14-2 feed forward neural
network with back propagation algorithm
was found to have acceptable performance
with mean squared error of 0.020. This
model was used to predict two output
variables of rut depth and contact area with
regression correlations of 0.99961 and
0.99996 for rut depth and contact area,
respectively. Furthermore, the results were
compared with conventional models
proposed for predicting the contact area and
rut depth. The promising results of ANN
model give higher privilege over
conventional models. The findings also
introduce the potential of ANN for
modeling. However, the authors recommend
further studies to be conducted in this realm
of computing due to its great potential and
capability.