## 5 Dataset Used
However, study [48] indicated a decrease in compressive strength with increasing W/B ratios across all tested mixes. Notably, in a 30% RCA mix, compressive strength dropped significantly over 28 days as the W/B ratio increased from 0.3 to 0.4 and 0.5. Moreover, research [49] has shown negative impacts of increasing W/B ratio on the compressive strength of geopolymeric recycled aggregate concrete (GRAC). It was found that static compressive strength fell by 17%, 16%, and 28% when the W/B ratio increased from 0.3 to 0.4 in GRAC mixes with 0%, 50%, and 100% RCA, respectively. Therefore, appropriate selection of the W/B ratio is crucial to maintain the CS of various concrete mixes. Studies also revealed distinct differences in the properties of RAC with 100% RA and NAC, with the former displaying significant property loss. However, RAC composed of 75% NA and 25% RA showed negligible changes [50]. Research also suggested that a low W/B ratio can negatively impact the strength of RCA, indicating that the concrete strength is dependent on both the RCA and the W/B ratio [51]. As such, RCA is considered an essential element in our dataset due to its impact on concrete strength. The application of a superplasticizer enhances the quality of RCA, making it more suitable for structural usage. An increase in the dosage of the superplasticizer leads to a corresponding rise in the CS of the concrete. In particular, when standard superplasticizer was incorporated into concrete made with Fine Recycled Aggregates Concrete (FRCA), there was a significant improvement in CS: an increase of 47% was noted after seven days, 35% after 28 days, and 43% after 56 days, as per the findings of study [52]. The longevity and durability of concrete are intrinsically linked to its aging process. As the quantities of GGBFS and RCA increase, there is a marked decline in both the short-term and long-term CS of the concrete [53]. Yet, intriguingly, the rate at which strength develops with age amplifies when both GGBFS and RCA are heightened. This escalation in the strength growth rate can be attributed to the latent hydraulic activity of GGBFS and the amplified hydration of unhydrated cement particles present in RCA [53]. For the creation of an accurate model, it is vital to take into account the variables that have a significant influence on the desired output. Identifying the parameters with the greatest and least impact on the output requires determining the correlation among these parameters. Various types of correlations have been proposed over time, such as Intra-class and Rank correlations. However, the Pearson method has become a popular choice among researchers (as stated by [54; 55]). The Pearson Correlation Coefficient (PCC) is essentially defined as the quotient obtained when the covariance of two parameters, \(\text{cov}(X,Y)\), is divided by the product of their standard deviations, as given in the equation below:  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{\sum(x_{i}-\bar{ x})(y_{i}-\bar{y})}{\sqrt{\sum(x_{i}-\bar{x})^{2}}\sqrt{\sum(y_{i}-\bar{y})^{2}}} \tag{19}\]  
In this context, \(\bar{x}\) and \(\bar{y}\) denote the averages of the X and Y datasets, respectively. The PCC, symbolized as \(\rho_{X,Y}\), falls within the range of \(-1\) to \(1\). A correlation coefficient of \(\rho_{X,Y}=1\) indicates a strong correlation between the variables, while a value of \(\rho_{X,Y}=0\) suggests that \(X\) and \(Y\) are linearly independent. However, it is essential to understand that a correlation coefficient of zero does not necessarily imply a complete lack of correlation. The variables might still be related, but in a non-linear manner, as pointed out by [PERSON] and [PERSON] in [56]. Furthermore, as [PERSON] highlighted in [57], a negative correlation coefficient indicates an inverse relationship between the two variables, meaning that as one variable increases, the other decreases, and vice versa. Figure 3 provides a matrix of the PCCs for the input and output variables as a heatmap.