Author(s)

Hassan Sharafi, Sahar Jalili

Department of Civil Engineering, College of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
School of Faculty Engineering, Razi University, Kermanshah, Iran.

In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.

Liquefaction, Cyclic Tests, Neural Networks, CRR, Silty Sands

Sharafi, H. and Jalili, S. (2014) Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks. Open Journal of Civil Engineering, 4, 217-228. doi: 10.4236/ojce.2014.43019.