Modeling soil collapse by artificial neural networks

The feasibility of using neural networks to model the complex relationship between soil parameters, loading conditions, and the collapse potential is investigated in this paper. A back propagation neural network process was used in this study. The neural network was trained using experimental data. The experimental program involved the assessment of the collapse potential using the one-dimensional oedometer apparatus. To cover the broadest possible scope of data, a total of eight types of soils were selected covering a wide range of gradation. Various conditions of water content, unit weights and applied pressures were imposed on the soils. For each placement condition, three samples were prepared and tested with the measured collapse potential values averaged to obtain a representative data point. This resulted in 414 collapse tests with 138 average test values, which were divided into two groups. Group I, consisting of 82 data points, was used to train the neural networks for a specific paradigm. Training was carried out until the mean sum squared error (MSSE) was minimized. The model consisting of eight hidden nodes and six variables was the most successful. These variables were: soil coefficient of uniformity, initial water content, compaction unit weight, applied pressure at wetting, percent sand and percent clay. Once the neural networks have been deemed fully trained its accuracy in predicting collapse potential was tested using group II of the experimental data. The model was further validated using information available in the literature. The data used in both the testing and validation phases were not included in the training phase. The results proved that neural networks are very efficient in assessing the complex behavior of collapsible soils using minimal processing of data. This revised version was published online in July 2006 with corrections to the Cover Date.