Plasticity prediction of expansive soil treated with sand by artificial neural network

Résumé

Abstract Clay soils consist of fine particles and are generally characterized by low strength. The interactions between clays and water result in high-plasticity clay mixtures that can easily deform or crack. It should be noted that the addition of sand to expansive soils can help to improve their particle size and reduce their plasticity, and consequently augment their strength. The present study aims mainly to develop a model for the prediction of the plasticity index (PI) of soil treated with sand, at various contents using the artificial neural network (ANN) method. It was revealed that the ANN technique ensures good prediction accuracy for a large number of parameters related to geotechnical problems. For the purpose of predicting the plasticity index values of sand-treated soils, the results of experimental tests that were conducted on 38 soil samples were collected and thoroughly analyzed. It was decided to consider three inputs, namely the plastic limit, liquid limit, and sand percentage, while there was only one output, i.e. the plasticity index. The results of the analyses conducted in this study indicated that the artificial neural network (ANN) model might be quite efficient in predicting the plasticity index of soils treated with sand. Afterwards, an experimental investigation was carried out on the clayey soil that was brought from the Wilaya (Province) of Medea in Algeria. This soil was treated with dune sand at different percentages, and the results obtained were compared with those predicted by the artificial neural network (ANN) technique.

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