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This study is focused on determining the potential of using deep neuralnetworks (DNNs) to predict the ultimate bearing capacity of shallow foundationin situations when the experimental data which may be used to train networks isscarce. Two experiments involving testing over 17000 networks were conducted.The first experiment was aimed at comparing the accuracy of shallow neuralnetworks and DNNs predictions. It shows that when the experimental dataset usedfor preparing models is small then DNNs have a significant advantage overshallow networks. The second experiment was conducted to compare theperformance of DNNs consisting of different number of neurons and layers.Obtained results indicate that the optimal number of layers varies between 5 to7. Networks with less and - surprisingly - more layers obtain lower accuracy.Moreover, the number of neurons in DNN has a lower impact on the predictionaccuracy than the number of DNN's layers. DNNs perform very well, even whentrained with only 6 samples. Basing on the results it seems that whenpredicting the ultimate bearing capacity with ANN models obtaining small buthigh-quality experimental training datasets instead of large training datasetsaffected by a higher error is an advisable approach.

The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations Trained on Scarce Data