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Investigation into the Robustness of Artificial Neural Networks for a Case Study in Civil E
Investigation into the Robustness of Artificial Neural
Networks for a Case Study in Civil Engineering
1M. A. Shahin, 2H. R. Maier and 3M. B. Jaksa
1Research Fellow, School of Civil, Mining Environmental Engineering, University of Wollongong, NSW,
Australia, E-Mail: shahin@uow.edu.au
2Associate Professor, School of Civil Environmental Engineering, University of Adelaide, SA, Australia
3Senior Lecturer, School of Civil Environmental Engineering, University of Adelaide, SA, Australia
Keywords: Robustness; Artificial neural networks; Settlement; Shallow foundations.
EXTENDED ABSTRACT set). Therefore, the purpose of ANNs is to non-
linearly interpolate (generalise) in high-
Artificial neural networks (ANNs) have been used dimensional space between the data used for cali-
as a prediction tool in many areas of engineering. bration. ANNs have been shown to outperform
In order to test the robustness and generalisation more traditional statistical methods as they are
ability of ANN models, the approach that is gener- universal function approximators (Hornik et al.
ally adopted is to test the performance of trained 1989). However, one of the difficulties in using
ANNs on an independent validation set. If such ANN models is that the potential number of free
performance is adequate, the model is deemed to model parameters (i.e. connection weights) is gen-
be robust and able to generalise. However, this is erally large compared wi
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