Development of CO2 Concrete Predictive Models
The creation of predictive models requires a laboratory data set. Consequently, 61 different concrete variations are produced for the generation of both regression analysis and artificial neural network.
Materials for Concrete
The recycled aggregate used for the generation of concrete originates from a centralised recycling plant in south-eastern Australia. The recycled aggregate is graded to 10 and 20mm. Figure 1 shows a classification breakdown of the recycled aggregate.
The virgin aggregate used in this paper is basalt and is supplied by a south-eastern Australia centralised plant. Basalt is required to allow for the varying recycled aggregate replacement percentages. Figure 1 shows both the virgin aggregate and recycled aggregate.
The sand employed within this paper is obtainable from a south-eastern Australia supplier. The apparent particle density, particle density on dry basis, particle density on saturated surface-and dried basis are 2.67, 2.53 and 2.58 tonnes per m3 respectively. The water absorption is 2.20%.
The cement used in the creation of concrete for the generation of predictive models is a general blended cement. The cement contains 30% fly ash and conforms with AS.3972-2010  General purpose and blended cements.
CO2 is supplied from an Australia wide supplier and is industrial grade which has gas purity greater than 99.9%.
No chemical admixtures are used in the creation of concrete for this paper.
The carbonation chamber employed in the CO2 injection into recycled aggregate is rectangular shape, sized at 500 mm x 500 mm x 300 mm. The pressure of the CO2 is controlled with the assistance of a gas pressure regulator connected at the inlet valve. A relief valve is used to vent other gases while CO2 is injected into the chamber. Aggregate is carbonated at a natural moisture content. Silica gel is also added into the chamber to ensure there is no water accumulation due to the carbonation reaction. Figure 2 provides an image of the chamber design.
Concrete Testing, CO2 Concrete Variables and Mix Design for Generation of Prediction Models
Compressive Strength Testing
The creation of concrete specimens for laboratory compressive strength testing is required to allow for regression analysis as well as the creation of an artificial neural network. Three 100-mm-diameter and 200-mm-height concrete cylinders were utilised in the determination of compressive strength. The pouring of samples is completed in accordance with AS.1012.2 .
Compressive strength is obtained with the assistance of a hydraulic press and is completed in accordance with AS.1012.9 . Upon 28 days, the 100-mm-diameter and 200-mm-height concrete cylinders are either capped or ground on the flat surface to ensure uniform loading. The cylinder is then placed within the press and compressive load is applied at a rate of 20 ±2 MPa per minute until failure.
CO2 Concrete Variables
The CO2 Concrete experimental program includes several variables which allow for different compressive strength quality. The variables include water to cement ratio, recycled aggregate replacement ratio, carbonation pressure and carbonation duration. The variables can be observed in Table 2.
Based on the given variables 61 different concrete mixes or data points are created (creation dataset). Each data point or mix design is given a mix code. The mix code reads water to cement ratio/recycled aggregate-replacement-carbonation-duration-carbonation pressure. Table 2 provides all variables of the 61 data points.
The mix design allows for a direct comparison of water to cement ratio, recycled aggregate replacement and carbonation variables. Aggregate is mixed into concrete at a saturated-surface dry state to ensure that a fair comparison can be made. Table 2 shows the mix design for concrete samples used for regression analysis and artificial neural networking.
Laboratory Concrete Results for Generation of Prediction Models
All compressive strength results are an average of three results. Figure 3 splits into three mains sections, isolating water to cement ratio. With the separation of water-to-cement ratios, the recycled aggregate concrete replacement and carbonation variables can be compared to the control sample. The control sample bars are coloured in black, 30% recycled aggregate replacement coloured in blue, 50% recycled aggregate replacement coloured in red and 100% recycled aggregate replacement coloured in green. Figure 3 shows that the injection of CO2 can help greatly as the samples without carbonation are amongst the lowest in strength. For example, without carbonation the 100% recycled aggregate replacement sample (0.4/100-0-0) achieved a strength of 33.14 MPA while the carbonated concrete, 0.4/100-120-75 averaged 39.64 MPa and 0.4/100-120-25 achieved 39.33 which is a near 20% improvement. Furthermore, if the 100% samples with 0.45 and 0.5 water to cement ratios are observed the carbonated samples experience an even greater increase in compressive strength over the untreated samples. The 61 data points found in Figure 3 are used for regression analysis and the artificial neural network creation. The results are also published in the following paper .
Development of the Predictive Models based on CO2 Concrete’s Compressive Strength
By utilising the 61 statistical points (creation dataset) from the data array a regression analysis formula can be generated. The formula permits a multiple regression analysis based on seven key variables, which determine the compressive strength of CO2 Concrete. When applied with constants Equation 2 can be employed for determining the compressive strength of CO2 concrete.
Equation 2: Multiple Regression Formula for Determining the Compressive Strength of CO2 Concrete
F’c = -10089.538 – (Rwc) – (Rrca) + (Cp1) + (Cp2) + (Qc) + (Qw) – (Qsand)
The key variables in the formula are the compressive strength (F’c), water-to-cement ratio (Rwc), recycled coarse aggregate replacement ratio (Rrca), carbonation pressure (Cp1), carbonation duration (Cp2), amount of cement (QC), quantity of water (Qw) and volume of sand (Qsand).
Artificial Neural Networks
The artificial neural network is created using MATLAB and involved training multiple hidden layers to obtain precise results. An artificial neural network containing multiple hidden layers allows for a greater number of nodes and, therefore, can recognise complicated non-linear problems . The advent of multiple hidden layers allows for a reduction in error when forecasting the compressive strength of CO2 Concrete . Figure 4 shows two input screens for the created artificial neural network. The input windows permit (A) the prediction of compressive strength based on CO2 Concrete variables or conversely, (B) possible mix designs of CO2 Concrete based on a desired compressive strength.
Verification of the Developed Models
The accuracy of a prediction model can be compared for accuracy against existing laboratory compressive strengths, however, the generation of new concrete for the validation of such predictions is very important to endorse the accuracy of a given prediction model. Consequently, variables between the variables utilised in the production of prediction models can assist in evaluating the success of predictive model.
CO2 Concrete Variables for Verification of Predictive Model
Table 3 shows the verification variables in between the variables used for the creation of mathematical models.
Experimental Design for Variation of Predictive Model
The experimental design utilised partially randomised carbonation variables which largely corresponded to the high-pressure range 100 to 200 kPa and long durations of 50 to 120 min. The selected carbonation variables permit new data to be obtained in new intervals. The verification data lies between the 75 and 200 kPa arrays, which allows for forecasting of the compressive strength without the use of the same variables to create the model to verify the prediction model. Each result obtained is an average of three separate results.
The water-to-cement ratio as well as the recycled aggregate replacement ratio exhibits a smaller variation than the carbonation variables. The selection of three water-to-cement ratios and recycled aggregate replacement ratios mirror a real-life or practical design. Changes in either of these variables require a slightly different mix and therefore would not be practical for implementation.
The validation data including 22 (validation dataset) different concrete mixtures assist in the investigation of validation of predictive model against CO2 concrete itself. When interpreting the mix code, the first number refers to the water-to-cement ratio, the second represents recycled aggregate replacement percentage, the third reveals the carbonation duration and, finally, the fourth specifies the carbonation pressure. Table 3 presents the concrete utilised for endorsement of the predictive model.
Materials and Mix Design
The materials and mix design utilised in the production of CO2 Concrete for the verification of the predictive models complies with those of the previous mixtures used in the generation of the model in terms of ratio. The mix design for verification is in Table 3.