ABSTRACT

The Constant rate of strain consolidation (CRSC) test, in which the continuous loading is applied the sample has been developed to overcome some of the problems associated with the incremental loading consolidation (ILC) test. Therefore, it is able to reduce the test time and provide a well defined the curve of effective stress versus strain due to continuous Stress-strain points. Also, the CRSC test has been accepted widely as a standard method in foreign countries because of its many advantages. However, in Korea the CRSC test has not been used in engineering practice and experimentally verified. Because there is not a precise criterion of strain rate despite consolidation characteristics are influenced on strain rate. Consequently, it is difficult to apply in engineering practice. In this study, artificial neural networks are applied to the estimation of the proper strain rate of the CRSC test. This study shows the possibility of utilizing the artificial neural networks model for estimation of the strain rate in the CRSC test.

INTRODUCTION

Recently, a number of construction projects for obtaining industrial areas have been increased in many coastal areas or soft ground though these areas have a weak condition. The structure constructed on soft ground are seriously damaged due to soft ground with a high water content, low stress, and large strain undergoing large settlement. Accordingly, the properties of consolidation must be precisely perceived to solve these problems. The ILC test has been used widely until now as most common method for predicting the consolidation properties in softy clay. However, the period of testing is required the time more than a week to finish the ILT test. Also, It is difficult to determine the preconsolidation pressure due to the curve of stress and strain is not appeared is to definitely.

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