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The application of artificial neural networks to geotechnical engineering gives new opportunities to learn from experience and to increase efficiency. This is shown by three examples. In the first example the automatic derivation of improved correlations between parameters of different models is shown. In the second example information from poor field data is extracted, resulting in a reliable prediction when combined with basic expert knowledge. The third example shows how artificial neural networks can be used to improve design and to enable new applications.
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