PREDICTION OF SWELLING PARAMETERS OF TWO CLAYEY SOILS FROM ALGERIA USING ARTIFICIAL NEURAL NETWORKS

Abstract

The phenomenon of swelling is one of the more complicated geotechnical problems that the engineer have to deal with. However, its quantification is essential for the design of structures and various methods can be applied to the identification of this phenomenon. Some, such as mineralogical identification and direct measurements of swelling, are more or less long and require very specific equipment. However, there are other methods that offer the advantage of being relatively fast and lesser expensive: they are based on soil mechanics parameters. Using these parameters, several authors have introduced soil swelling prediction models, mostly in the form of classifications and empirical formulas. This work concerns in the first part the identification and classification of the swelling potential of two clays located in north-western Algeria. Followed by a statistical analysis carried out to test the reliability of the observations for the estimation of the pressure and the swelling amplitude using a multiple linear regression. A second part is devoted to the development of a prediction method by artificial neural networks allowing the estimation of swelling parameters (pressure and amplitude) by minimizing the difference between the experimental measurements and the numerical results. Modeling by artificial neural networks is of great interest in the field of prediction. The application of two networks makes it possible to obtain good forecasts of the swelling parameters.