Abstract 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 17,000 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 Artificial Neural Network (ANN) models obtaining small but high-quality experimental training datasets instead of large training datasets affected by a higher error is an advisable approach.
The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data
KSCE Journal of Civil Engineering ; 23 , 1 ; 130-137
2018-12-03
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data
Online Contents | 2019
|Taylor & Francis Verlag | 2020
|Reliability analysis of bearing capacity for shallow foundations based on Eurocode 7
HENRY – Bundesanstalt für Wasserbau (BAW) | 2011
|Seismic bearing capacity of shallow foundations resting on rock masses subjected to seismic loads
Online Contents | 2016
|