Prediction of compressibility parameters of the soils using artificial neural network.
The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.
Department of Geophysical Engineering, Engineering Faculty, Sakarya University, Serdivan, Sakarya Turkey.
T Fikret
TF
Kurnaz
Department of Civil Engineering, Engineering Faculty, Dumlupinar University, Kutahya, Turkey.
Ugur
U
Dagdeviren
Department of Electrical and Electronics Engineering, Engineering Faculty, Sakarya University, Serdivan, Sakarya Turkey.
Murat
M
Yildiz
Department of Electrical and Electronics Engineering, Engineering Faculty, Sakarya University, Serdivan, Sakarya Turkey.
Ozhan
O
Ozkan
Artificial neural network; Compressibility; Compression index; Consolidation; Recompression index