Mohammad M. Al-Ani; Mohammad Y. Fattah; Mahmoud T. A. Al-Lamy
Abstract
Artificial Neural Networks (ANNs) are used to relate the properties of gypseous soilsand evaluate the values of compression of soils under different conditions. Therefore, onelayerperception ...
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Artificial Neural Networks (ANNs) are used to relate the properties of gypseous soilsand evaluate the values of compression of soils under different conditions. Therefore, onelayerperception training using back propagation algorithm is used to assess the validity ofapplication of ANNs for modelling the settlement ratio for wetting process, (S/B)w, and thesettlement ratio for soaking process, (S/B)s.It was found that ANNs have the ability to predict the compression of gypseous soildue to soaking, washing process with high degree of accuracy. Also, performance of ANNsshowed that one hidden layer with one hidden nodes is practically enough for the neuralnetwork analysis.The sensitivity analysis indicates that the viscosity and specific gravity have themost significant effect on the predicated settlement ratio and the density of injection materialand void ratio have moderate impact on the settlement ratio. The results also show that theinitial gypsum content, stress and time have the smallest impact on settlement ratio.It was concluded that the artificial neural networks (ANNs) have the ability topredict the settlement ratio for wetting process (S/B)w, and settlement ratio for soakingprocess (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 wherethe coefficients of correlation are (0.9541) and (0.991), respectively.
