• Journal of the Korean Geotechnical Society
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• v.29 no.12
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• pp.45-56
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• 2013

Abrams equation could be effectively applied to predict strength of cement-admixed clay and clay-water content to cement content ratio is a fundamental parameter for governing strength. This paper analyses unconfined compression strength varying with $w_c/C$ and curing time using laboratory test results. An attempt is made to identify strength of composite soil of cement and clay according to variation of Abrams coefficients and curing time. The value B, which was considered to be constant value in past researches, needs to be considered as parameter variable with curing time. From Abrams equation a correlation was formed for unconfined compression strength with mixing conditions by $w_c/C$ and curing time as dependent variable. Regression results in this paper could be used to predict strength of cement-admixed clay at various mixing conditions.

• Journal of the Korean GEO-environmental Society
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• v.15 no.7
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• pp.31-38
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• 2014

For analysis of mechanics properties of soil cement, unconfined compressive strength has been proposed by existing case studies. In this study, mechanical changes with water content of silt, curing time and cement content were analyzed through unconfined compressive strength test. In addition, the changes for B factor by Abrams were compared with existing case studies after the prediction equations could be proposed about the unconfined compressive strength of admixed cement soil. Especially, the B constant factor was changed with soil characteristics and curing time. For analysis results of appropriateness status and unconfined compressive strength, consideration of variable form was titrated. The prediction equations at low plasticity silt admixed using the uniaxial compressive strength with applying Abrams's equation and considering cement content, curing time is proposed.

• Computers and Concrete
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• v.23 no.4
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.