Prediction of the vertical swelling percentage of expansive clays using a two-stage artificial neural networks methodology

Artificial Neural Networks (ANN), which are used in many different areas, have been applied to predict the vertical swelling percentage of expansive clays. In contrast to previous models that estimated ANN Models in a single phase, this paper proposes an alternative analysis in based on the following two-stage operation: (a) conducting an ANN analysis on the swelling-pressure test results (i.e., the ASTM 4546 Method C test results) to obtain the swelling- pressure model for any given clay characteristics, and (b) performing an additional ANN analysis on the swelling-percentage test results (i.e., the ASTM 4546 Method B test results), including the former ones, with the given independent variables of the clay characteristics. This second stage includes a defined expression containing the given surcharge pressure and the predicted value of the swelling pressure as obtained from the model of the previous stage. Two final ANN Models, each with a different arrangement of the given independent variables, were derived from this two-stage procedure. Their statistical fit was clearly found to be superior in comparison to previous models estimated with the same data set. Furthermore, one of these two models exhibited the expected geophysical behavior. As this new ANN Model yields higher predicted swelling-percentage values, it can definitely be regarded as a preferable one in the sense of enlarging the safety margin in heave calculations.