author’s name
Optimal Mix Approach for Recycled Aggregate Concrete
Vivian W. Y. Tam1 *, Khoa N. Le1, Duangthidar Kotrayothar2
1*WesternSydneyUniversity, School of Computing, Engineering and Mathematics and Institute for Infrastructure Engineering, Locked Bag 1797, Penrith, NSW 2751, Australia
2 Nakhon Ratchasima Rajabhat University, Suranarai Rd, Muang, Nakhon Ratchasima, 30000Thailand.
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Copyright © 2014 by author(s) and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
Abstract
Based on the experimental work on recycled aggregate concrete (RAC) from different mix designs, including recycled coarse aggregate (RCA) replacement ratios, water-to-cement ratios and aggregate-to-cement ratios, this paper develops an optimal mix approach for RAC. The optimal mix approach for RAC with seven accurate prediction equations is developed in this paper using linear regression analysis and proved with high accuracy. The developed equations can be used to predict characteristic compressive, tensile and flexural strength of RAC by providing parameters of mix designs or from one of the characteristic strengths of RAC. The optimal mix approach developed in this paper can significantly help the industry to efficiently and effectively use RCA and RAC in all applications; that is, not limited to non-structural applications.
Keywords
recycled aggregate concrete, recycled coarse aggregate, optimal mix approach, mechanical behaviour
1. Introduction
Concrete is an essential, mass-produced material in the construction industry. However, much effort has been made to recycle and conserve precious natural resources. Repeated recycling can be suitable for concrete, as is the case for steel and aluminum [1, 2]. An effective method would be the use of recycled coarse aggregate (RCA) in the production of recycled aggregate concrete (RAC). RCA is a particle of stones attached with old cement mortar generated by crushing demolished concrete waste. RAC is created by mixing RCA along with other natural ingredients, including cement, water, fine aggregate and other materials. As concrete is composed only of cementitious materials, and powders generated during the production of RCA can be reprocessed as cement resources, repeated recycling is possible. This also enables concrete to be recycled in a fully closed system, thus improving the environment by reducing landfill and concrete waste.
RAC has not been commonly used as structural applications; one of the main reasons is limited technologies developed in improving the quality of RCA and thus RAC. This paper aims to study the use of RCA from demolished concrete waste as RAC and develops an optimal mix approach of RAC for structural applications.
2. Research Methodologies
Experimental work on RAC from different mix designs, including replacement ratios of RCA from 0% to 100%, water-to-cement ratios at 0.35, 0.40, 0.45, 0.50, 0.55 and 0.60, and aggregate-to-cement ratios at 3.0, 3.5, 4.0, 4.5, 5.0, 5.5 and 6.0, were examined. In total, forty-four mixes were conducted in this paper.
The RAC mixing was first charged with about half of coarse aggregate, then with fine aggregate, then with cement, and finally with the remaining coarse aggregate. Water was then immediately added after starting the operation for two minutes according to the Australian Standard [3]. All mixes are controlled with a slump value between 70mm and 100mm without additions of any additives.
Three major properties of RAC were under investigation: compressive strength, tensile strength and flexural strength. The 28-day compressive, tensile and flexural strengths from the different mix designs for the RAC were conducted based on the Australian Standard [4-6]. The average of three samples of the 28-day compressive, tensile and flexural strength tests was calculated. The characteristic compressive (ƒc΄), tensile (ƒct΄) and flexural (ƒcf΄) strengths of RAC were used for the development of prediction equations for the optimal mix approach. Equation 1 shows the calculation method for the characteristic compressive strength from the average compressive strength [7]. The same calculation method is used for the characteristic tensile and flexural strengths.
/ Equation 1whereƒc΄ is thecharacteristic compressive strength; ƒcr isthe average compressive strength; and σ isthe standard deviation with a 95% confidence level.
Data collected from the experimental work were analysed using the Statistical Package for Social Sciences (SPSS) Version 18.0 for Windows. Before we conduct correlation and regression analysis of the experimental work data, sensitivity analysis was undertaken. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its input [8], which can be measured by Equation 2[9]. All four available regression analysis methods, including Stepwise, Remove, Backward and Forward, were used. All regression analysis in this paper are assumed as linear.
G(p) = [x ϵ C| - F (p,x) ϵ M (x)] / Equation 23. Optimal mix approach for recycled aggregate concrete
Table 4 summarises the results of compressive, tensile and flexural strengths of the RAC samples from the experimental work in this paper. It is clear that the relationship between different parameters of the RAC, including the RCA replacement ratio, amount of water used, amount of cement used, amount of sand used, water-to-cement ratio and aggregate-to-cement ratio, and compressive, tensile and flexural strengths are linear. The implication is that the higher RCA replacement ratio will deteriorate the concrete strength.
Table4: Characteristic of compressive, tensile and flexural strength
Water-to-cement ratios / Aggregate-to-cement ratios / RCA
replacement
ratio (%) / Average compressive strength, (MPa) / Average tensile strength
(MPa) / Average flexural strength
(MPa) / Characteristic
compressive
strength, ƒc΄
(MPa) / Characteristic
tensile
strength, ƒct΄
(MPa) / Characteristic
flexural
strength, ƒcf ΄
(MPa)
0.30 / 4.5 / 0 / 71.0 / 5.9 / 6.6 / 69.8 / 5.2 / 6.3
30 / 63.9 / 5.2 / 6.1 / 63.6 / 4.8 / 5.8
100 / 49.2 / 4.8 / 4.1 / 47.5 / 4.5 / 4.0
0.35 / 0 / 68.0 / 5.6 / 6.2 / 66.3 / 4.9 / 5.9
30 / 56.2 / 4.9 / 5.5 / 54.0 / 4.6 / 5.2
100 / 46.0 / 4.5 / 4.0 / 44.7 / 3.4 / 3.9
0.40 / 0 / 62.0 / 5.3 / 5.8 / 61.5 / 4.7 / 5.3
30 / 53.7 / 4.9 / 5.0 / 52.1 / 4.5 / 4.5
100 / 41.9 / 4.2 / 3.8 / 39.5 / 4.0 / 3.3
0.40 / 0 / 56.8 / 5.1 / 5.6 / 53.1 / 4.9 / 4.7
10 / 55.4 / 5.0 / 5.0 / 54.5 / 4.8 / 4.8
20 / 55.1 / 4.9 / 4.9 / 48.7 / 4.1 / 4.3
30 / 52.4 / 4.7 / 4.6 / 49.1 / 4.4 / 4.0
40 / 52.0 / 4.6 / 4.4 / 50.9 / 4.2 / 3.7
50 / 50.0 / 4.5 / 4.1 / 42.2 / 4.2 / 3.8
60 / 48.0 / 4.6 / 4.4 / 46.4 / 4.3 / 4.2
70 / 46.8 / 4.4 / 4.2 / 43.7 / 3.8 / 3.8
80 / 47.2 / 4.3 / 4.2 / 43.9 / 3.7 / 3.7
90 / 44.2 / 4.1 / 4.1 / 41.1 / 4.0 / 3.5
100 / 39.1 / 3.8 / 3.8 / 34.9 / 3.4 / 3.7
0.50 / 0 / 46.0 / 4.6 / 4.9 / 45.3 / 4.2 / 4.7
30 / 43.4 / 4.4 / 4.3 / 41.2 / 3.9 / 3.9
100 / 32.0 / 3.4 / 3.5 / 30.4 / 3.1 / 2.9
0.55 / 0 / 40.0 / 4.1 / 4.4 / 37.2 / 4.0 / 4.0
30 / 37.2 / 3.9 / 4.0 / 34.7 / 3.3 / 3.7
100 / 29.0 / 3.1 / 3.1 / 27.4 / 2.9 / 2.9
0.60 / 0 / 32.2 / 3.8 / 4.2 / 30.4 / 3.5 / 3.7
30 / 32.3 / 3.6 / 3.6 / 30.6 / 2.7 / 3.1
100 / 25.3 / 3.0 / 3.0 / 21.8 / 2.8 / 2.8
0.45 / 3.0 / 0 / 80.4 / 6.2 / 6.7 / 78.7 / 6.1 / 6.6
30 / 74.0 / 5.6 / 5.8 / 72.7 / 4.8 / 5.1
100 / 55.5 / 4.5 / 4.8 / 54.5 / 4.3 / 3.9
3.5 / 0 / 73.1 / 5.8 / 6.2 / 72.2 / 5.0 / 6.0
30 / 65.9 / 5.2 / 5.3 / 63.6 / 4.7 / 5.0
100 / 51.8 / 4.3 / 4.2 / 50.2 / 4.1 / 4.0
4.0 / 0 / 66.1 / 5.4 / 5.9 / 64.4 / 5.0 / 5.5
30 / 57.1 / 5.0 / 4.8 / 56.2 / 4.6 / 4.5
100 / 45.6 / 4.1 / 3.9 / 44.1 / 3.7 / 3.7
5.0 / 0 / 52.2 / 4.9 / 4.9 / 49.5 / 4.6 / 4.6
30 / 47.6 / 4.6 / 4.3 / 47.2 / 4.5 / 3.7
100 / 34.4 / 3.6 / 3.4 / 33.4 / 3.4 / 3.3
5.5 / 0 / 50.4 / 4.6 / 4.4 / 49.3 / 4.0 / 3.4
30 / 42.9 / 4.3 / 3.9 / 40.5 / 3.2 / 3.6
100 / 32.3 / 3.4 / 3.1 / 31.9 / 3.2 / 2.7
6.0 / 0 / 42.9 / 4.3 / 4.0 / 41.5 / 3.9 / 3.1
30 / 34.9 / 3.7 / 3.6 / 34.1 / 3.3 / 3.2
100 / 27.1 / 2.9 / 2.8 / 24.3 / 2.3 / 2.1
Linear regression analysis is used for the development of the prediction equations for the RAC optimal mix approach. The correlation among the characteristic strengths (fc, fct and fcf) and different RCA replacements (Rrca), water-to-cement (Rwc) and aggregate-to-cement (Rac) ratios, and quantities of 10mm and 20mm natural (Qnca10 and Qnca20respectively) and 10mm and 20mm recycled coarse aggregate (Qrca10 and Qrca20 respectively), water (Qw), cement (Qc), and sand (Qs) is found. The correlation between ƒc΄and Qrca10, Qnca10, Qrca20, Qnca20, Qw, Qc, Qs, Rrca, Rwc, and Rac are 0.538, 0.512, 0.538, 0.512, 0.660, 0.646, 0.344, 0.528, 0.804 and 0.580 respectively. The significant values between the characteristic strengths and RCA parameters are all less than 0.05, which means that all correlations are significant at the 95% confidence level. The sensibility analysis results for the data for RAC is 0.918, which shows that the data is suitable for analysis and the development of the optimal mix approach.
There are four methods: Stepwise, Remove, Backward and Forward, used in the linear regression analysis. Prediction equations are developed if the correlation significant (sig. or α) of the models is less than 0.05. Table 6 summarizes the acceptance models for the characteristic compressive strength with their coefficients, R square values and correlation significant , which leads to the development of Equation 3 and Equation 4.
ƒc΄ = 105.118 – (111.411 x Rwc) – (1.161 x Qrca10) / Equation 3ƒc΄ = 324,870 + (69.32 x Qw) – (26.148 x Qs) – (0.175 x Rrca) – (132,236 x Rwc)
+ (131,455 x Rac) / Equation 4
where ƒc΄is thecharacteristic compressive strength; Rwc is the water-to-cement ratio; Qrca10 is the quantity of 10mm RCA by proportion (in %); Qwisthe quantity of water by proportion (in %); Qsisthe quantity of sand by proportion (in %); Rrcaisthe RCA replacement ratio; and Rac is the aggregate-to-cement ratio.
Table 6: Proposed models’ coefficients for ƒc΄as a dependent variable
Model / Unstandardized coefficients / Standardized coefficients / T / Sig. / R / R Square / Std. error of the estimate / Analysis methodB / Std. Error / Beta
1 / .000 / .962 / .926 / 3.72075 / Stepwise
(Constant) / 105.118 / 2.703 / 38.888 / .000
Rwc / -111.411 / 5.742 / -.798 / -19.404 / .000
Qrca10 / -1.161 / .090 / -.529 / -12.857 / .000
2 / .000 / .980 / .961 / 2.79422 / Backward
(Constant) / 324.870 / 62.780 / 5.175 / .000
Qw / 69.320 / 21.048 / 5.563 / 3.294 / .002
Qs / -26.148 / 4.891 / -1.762 / -5.346 / .000
Rrca / -.175 / .010 / -.528 / -17.093 / .000
Rwc / -1321.236 / 349.498 / -9.460 / -3.780 / .000
Rac / 131.455 / 34.063 / 6.655 / 3.859 / .000
The same procedure is also used for the development of the acceptance modelfor the characteristic tensile and flexural strengths with the RCA parameters. Similar procedures for the characteristic tensile and flexural strengths with their coefficients, R square values and correlation significant, which leads to the development of Equation 5 and Equation 6 respectively.
ƒct΄= 7.192 – (5.892 x Rwc) – (0.01 x Rrca) / Equation 5ƒcf΄= 8.246 – (7.648 x Rwc) – (0.015 x Rrca) / Equation 6
where ƒct΄is thecharacteristic tensile strength; and ƒct΄ is thecharacteristic flexural strength.
Equation 3, Equation 4, Equation 5and Equation 6 are provided to predict target characteristic compressive, tensile and flexural strengths from mix designs. The independent variables for Equation 3 are water-to-cement ratio and quantity of 10mm RCA by proportion. The independent variables for Equation 5 and Equation 6 are water-to-cement ratio and RCA replacement ratios. Equation 3 is re-analyzed using the same independent variables as in Equation 5 and Equation 6 in the linear regression analysis with the four methods, Equation 7 is then deviated.
ƒc΄= 105.509 – (112,283 x Rwc) – (0.175 x Rrca) / Equation 7From this study, it is also found that there are strong correlations among the characteristic compressive, tensile and flexural strengths. Linear regression analysis is used to develop prediction equations with the similar procedures as above. The acceptance models for setting different characteristic strengths as the dependent variables with their coefficients, R square values and correlation significant are found, which leads to the development of Equation 8, Equation 9 and Equation 10.
/ Equation 8/ Equation 9
/ Equation 10
4. Conclusion
The paper developed an optimal mix approach for RAC with seven accurate prediction equations using linear regression analysis. Using the developed equations, characteristic compressive, tensile and flexural strengths by providing parameters of mix designs or from one of the characteristic strength with their designed mix designs can be predicted. The developed equations (Equation 3, Equation 5 to Equation 10) can also be used to estimate the requirements for concrete mix designs in order to achieve the requirement strength. This paper can significantly help the industry in using RCA to achieve the highest possible strength for RAC, which can potentially be used in structural applications.
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