Author/Authors :
Ghanizadeh ، Ali Reza Department of Civil Engineering - Sirjan University of Technology , Heidarabadizadeh ، Nasrin Department of Civil Engineering - Sirjan University of Technology , Bayat ، Meysam Department of Civil Engineering - Islamic Azad University, Najafabad Branch , Khalifeh ، Vahid Department of Civil Engineering - Sirjan University of Technology
Abstract :
In this study, the evolutionary polynomial regression (EPR) method has been employed to develop simple models with reasonable accuracy to predict the compressive strength and Young s modulus of the lime/cement stabilized clayey subgrade soil. For this purpose, the different specimens with the various cement and lime contents, at three moisture contents (dry side, wet side, and optimum moisture content) were fabricated and were cured for 7, 14, 21, 28, and, 60 days to conduct the unconfined compressive strength (UCS) test. According to the test results, a dataset consisting of 75 records for each additive was prepared. Results of this study show that the R^2 value of the developed model for predicting UCS of cement-stabilized clay soil is equal to 0.96 and 0.95 for training and testing sets, respectively. These two values for lime-stabilized soil are 0.91 and 0.87, respectively. Moreover, the R^2 for predicting Young s modulus of cement-stabilized clay soil is equal to 0.90 and 0.89 for the training and testing set, respectively. These two values for predicting Young s modulus of lime-stabilized soil are 0.88 and 0.94, respectively. The sensitivity analysis showed that for the Portland cement stabilized clayey subgrade, the percentage of the Portland cement and moisture content are the most significant parameters for predicting the UCS and Young s modulus, respectively. In contrast, for the lime-stabilized clayey subgrade soil, the most important parameters are the moisture content and the UCS, respectively.
Keywords :
Stabilized clay , Portland cement and lime , Unconfined compressive strength , Young s modulus , Evolutionary polynomial regression