Compressibility indices of saturated clays by group method of data handling and genetic algorithms

Compression index (Cc) and recompression index (Cr) are used to estimate the consolidation settlement of fine-grained soils. As the determination of these indices from oedometer test is relatively time-consuming, in present research group method of data handling-type neural network optimized using genetic algorithms is used to estimate the compressibility indices (Cc and Cr) of saturated clays. Cc and Cr were modeled as a function of three variables including the initial void ratio (e0), liquid limit (LL) and specific gravity (Gs). Three hundred data sets collected from multiple sites in the province of Mazandaran, Iran, were used for the training and testing of the models. The predicted compressibility indices were compared with those of experimentally measured values to evaluate the performances of the proposed models. The results showed that appreciable improvement toward other correlations has been achieved. At the end, sensitivity analyses of the obtained models were carried out to evaluate the influence of input parameters on model outputs and showed that e0 and LL are the most influential parameters on Cc and Cr, respectively. Also, it has been demonstrated that the compressibility indices predicted by models are considerably influenced by changing measured Gs (uncertainty). In other words, the mean absolute percent error values increase greatly by Gs variation. Therefore, it needs more accuracy to measure this parameter in the laboratory.