Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks

Hassan Sharafi,Sahar Jalili

doi:10.4236/ojce.2014.43019

Abstract

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.

Highlights

Cyclic Resistance Ratio (CRR) is one of the fundamental parameters in the prediction of liquefaction phenomenon, frequently observed during many moderate to strong earthquakes in sand-silt mixture deposits
The main information from the tests needed for this study include; initial effective mean confining pressure, σ 'mean, initial relative density after consolidation, Dr (%), percentage of fines content, FC(%), void ratio, e, the number of cycle of liquefaction, Nl, and measured cyclic resistance ratio required for liquefaction triggering, CRR
A database including 667 laboratory cyclic tests on clean and silty sands were utilized to develop an artificial neural networks (ANNs) model to predict the amount of cyclic resistance ratio

Summary

Introduction

Cyclic Resistance Ratio (CRR) is one of the fundamental parameters in the prediction of liquefaction phenomenon, frequently observed during many moderate to strong earthquakes in sand-silt mixture deposits. No comprehensive study has yet been conducted to examine a wide-range laboratory test results on liquefaction resistance ratio (CRR) using ANN as a framework Based on these considerations, this study introduces a new ANN model, developed to predict liquefaction resistance of sand mixture with silt using published cyclic triaxial, hollow torsional and direct simple shear (DSS) tests data for various different sands. After investigating a number of models based on different combinations of input soil parameters, the final ANN model with best predication capability was proposed and parametric study was performed to verify the credibility of the model

Database Used for the Development of ANN Model

Neural Network Development

Results and Discussion

Parametric Study

Summary and Conclusions