An efficient neural network model to determine maximum swelling pressure of clayey soils

Abstract

Expansive soils exhibit excessive volume increases upon contact with water, which can pose a serious threat to stability of structures and foundations. Therefore, it is essential to determine the swelling properties, e.g. maximum swelling pressure, of these problematic soils. We employed a feed-forward neural network algorithm trained with Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient, and genetic algorithm to build a network model capable of determining the maximum swelling pressure of clayey soils over a wide range of conditions. The models were developed based on a sufficiently large experimental dataset that takes into account key factors that influence the soil swelling. The results show that the feed-forward neural network algorithm trained with Bayesian regularization has the highest overall accuracy, as its predictions agree well with the experimental data. Besides, a simplified network model was developed to be used in cases of limited data availability. The developed model provides accurate predictions over a wide range of conditions and can serve as a valuable tool for researchers and engineers dealing with expansive soils.