Using Artificial Neural Networks For Evaluation of Collapse Potential of Some Iraqi Gypseous Soils

In this research, Artificial Neural Networks (ANNs) is used in an attempt to predict collapse potential of gypseous soils. Two models are built; one for collapse potential obtained by single oedemeter test and the other is for collapse potential obtained by double oedemeter test. A database of laboratory measurements for collapse potential is used. Six parameters, which are 1.Gypsum content, 2.Initial void ratio, 3.Total unit weight, 4.Initial water content, 5.Dry unit weight, 6.Soaking pressure. are considered to have the most significant impact on the magnitude of collapse potential and used as an input to the models. The output model is the corresponding collapse potential. Multi-layer perceptron trainings using back propagation algorithm are used in this work. A number of issues in relation to ANN construction such as the effect of ANN geometry and internal parameters on the performance of ANN models are investigated. Information on the relative importance of the factors affecting the collapse potential are presented and practical equations for prediction of collapse potential of single oedemeter test and double oedemeter test in gypseous soils are developed. It is found that ANNs have the ability to predict the collapse potential of single oedemeter test and double oedemeter test in gypseous soil samples with a good degree of accuracy. The ANN models developed to study the impact of the internal network parameters on model performance indicate that ANN performance is sensitive to the number of hidden layer nodes, momentum terms, learning rate, and transfer functions. The sensitivity analysis indicated that the initial void ratio and gypsum content have the most significant affect on the prediction of collapse potential.