Statistical and neural network assessment of the compression index of clay-bearing soils

The compression index is used to estimate the consolidation settlement of clay-bearing soils. As the determination of compression index from oedometer tests is relatively time-consuming, empirical equations based on index properties can be useful. In this study the performance of widely used single and multi-variable empirical equations was evaluated using a database consisting of 135 test data. New empirical equations were developed utilizing least square regression analysis. In addition, an artificial neural network (ANN) with eight input variables was also developed to estimate the compression index. It was concluded that ANN provides the best results.RésuméL’indice de compression est utilisé pour estimer le tassement de consolidation des sols argileux. Comme la détermination de cet indice à partir des essais oedométriques prend quelque temps, des équations empiriques basées sur des indices géotechniques peuvent être utiles. Dans cette étude, l’intérêt d’équations empiriques à une ou plusieurs variables a été évalué à partir d’une base de données comportant 135 résultats d’essais. De nouvelles équations empiriques ont été développées à partir d’une analyse de régression par la méthode des moindres carrés. De plus, un réseau de neurones artificiel (ANN) avec huit variables d’entrée a été développé pour estimer l’indice de compression. La conclusion est que l’ANN donne les meilleurs résultats.

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