The safety of buried pipes under repeated load has been a challenging task in
geotechnical engineering. In this paper artificial neural network and regression model for
predicting the vertical deformation of high-density polyethylene (HDPE), small diameter flexible
pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the
heavy vehicle loads, are proposed.
The experimental data from tests show that the vertical diametric strain (VDS) of pipe embedded
in reinforced sand depends on relative density of sand, number of reinforced layers and height of
embedment depth of pipe significantly. Therefore in this investigation, the value of VDS is related
to above pointed parameters.
A database of 72 experiments from laboratory tests were utilized to train, validate and test the
developed neural network and regression model. The results show that the predicted of the vertical
diametric strain (VDS) using the trained neural network and regression model are in good
agreement with the experimental results but the predictions obtained from the neural network are
better than regression model as the maximum percentage of error for training data is less than
1.56% and 27.4%, for neural network and regression model, respectively. Also the additional set
of 24 data was used for validation of the model as 90% of predicted results have less than 7% and
21.5% error for neural network and regression model, respectively. A parametric study has been
conducted using the trained neural network to study the important parameters on the vertical
diametric strain.

Type of Study: Technical Note |