Effect Of Mineral Admixtures On Frost Resistance Of Self-compacting Concrete

Self-compacting concrete,which has been widely used in various projects now,is a new type of concrete,and it has excellent performance compared with ordinary concrete.Cement is one of its basic components.And high energy consumption and high emissions during the production process of cement have been controlling its production costs and affecting its green development process.It has been found that replacing some of the cement with some industrial wastes,such as fly ash and slag,can not only reduce cement dosage and lower the production cost of self-compacting concrete,but improve some properties of self-compacting concrete because of unique properties of these admixtures.At present,studies on self-compacting high-antifreezing concrete mostly concentrated on high-strength concrete(strength level is above C50 or C60)or single effect of a kind of mineral admixture on its performance.However,few papers focused on low strength self-compacting concrete under C40 and the effect of several mineral admixtures on properties of self-compacting concrete.But the demand for low strength self-compacting concrete is still in a leading position in the concrete field.Therefore,the C25 strength self-compacting concrete is used as the research object to study the effect of three kinds of mineral admixtures which are fly ash,slag and silica fume and air-entraining agents on its performance.In this paper,a four-factor quadratic regression method is used to design the test and the compressive strength and the mass loss rate are used as indicators.At the beginning,the mechanical properties of self-compacting concrete are briefly analyzed.On this basis,we discuss its frost-resistance to provide the reference for future applications in the project.This paper mainly draws the following conclusions:(1)The quadratic polynomial regression model used in this experimental condition can be used for the regression equation of 28 day's compressive strength of self-compacting concrete and its significance is very good.The influence of the four factors selected in this paper on the compressive strength of self-compacting concrete in order: fly ash content> slag content> silica fume content> air-entraining agent content.With the increase of fly ash content,the compressive strength of self-compacting concrete decreases first and then increases;With the increase of slag content,the compressive strength of self-compacting concrete increases first and then decreases;With the increase of silica fume content,thecompressive strength of self-compacting concrete shows a trend of increases;With the increase of the amount of air-entraining agent,the compressive strength of self-compacting concrete shows a trend of decreases.The solution for the maximum compressive strength is obtained as follows: X1=X2=X3=1,X4=-1.628,which is equivalent to 32.285% of fly ash content,32.285% of slag content,9.686% of silica fume content and 0% of Air entraining agent content.(2)The quadratic polynomial regression model used in this experimental condition can be used to establish the regression equation of self-compacting concrete with C25 strength and the significance is very good.The influence of four factors selected on the frost resistance of self-compacting concrete is as follows: fly ash content> slag content> air-entraining agent content> silica fume content.With the increase of fly ash content,the mass loss rate of self-compacting concrete decreases first and then increases;With the increase of slag content,the mass loss rate of self-compacting concrete decreases first and then increases;With the increase of silica fume content,the mass loss rate of self-compacting concrete shows a trend of decrease;With the increase of air-entraining agent content,the mass loss rate of self-compacting concrete decreases first and then rises.The solution with the highest frost resistance is: X1=1,X2=X3=-1,X4=0,which is equivalent to 32.285% of fly ash content,7.715% of slag content,2.314% of silica fume and 0.03% of air entraining agent content.