1 research outputs found
The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations Trained on Scarce Data
This study is focused on determining the potential of using deep neural
networks (DNNs) to predict the ultimate bearing capacity of shallow foundation
in situations when the experimental data which may be used to train networks is
scarce. Two experiments involving testing over 17000 networks were conducted.
The first experiment was aimed at comparing the accuracy of shallow neural
networks and DNNs predictions. It shows that when the experimental dataset used
for preparing models is small then DNNs have a significant advantage over
shallow networks. The second experiment was conducted to compare the
performance of DNNs consisting of different number of neurons and layers.
Obtained results indicate that the optimal number of layers varies between 5 to
7. Networks with less and - surprisingly - more layers obtain lower accuracy.
Moreover, the number of neurons in DNN has a lower impact on the prediction
accuracy than the number of DNN's layers. DNNs perform very well, even when
trained with only 6 samples. Basing on the results it seems that when
predicting the ultimate bearing capacity with ANN models obtaining small but
high-quality experimental training datasets instead of large training datasets
affected by a higher error is an advisable approach.Comment: KSCE Journal of Civil Engineering 201