Artificial neural networks analysis of treatment process of gypseous soi

Abstract EN

Abstract Artificial Neural Networks (ANNs) are used to relate the properties of gypseous soils and evaluate the values of compression of soils under different conditions.

Therefore, onelayer perception training using back propagation algorithm is used to assess the validity of application of ANNs for modelling the settlement ratio for wetting process, (S/B)w, and the settlement ratio for soaking process, (S/B)s.

It was found that ANNs have the ability to predict the compression of gypseous soil due to soaking, washing process with high degree of accuracy.

Also, performance of ANNs showed that one hidden layer with one hidden nodes is practically enough for the neural network analysis.

The sensitivity analysis indicates that the viscosity and specific gravity have the most significant effect on the predicated settlement ratio and the density of injection material and void ratio have moderate impact on the settlement ratio.

The results also show that the initial gypsum content, stress and time have the smallest impact on settlement ratio.

It was concluded that the artificial neural networks (ANNs) have the ability to predict the settlement ratio for wetting process (S/B)w, and settlement ratio for soaking process (S/B)s of gypseous soil with high degree of accuracy.

The equations obtained using (ANNs) for (S/B)w, and (S/B)s showed excellent correlation with experimental results where the coefficients of correlation are (0.9541) and (0.991), respectively.-