## 5 Dataset Used  
This research study draws its data from eleven separate previous investigations [5; 7; 37; 38; 39; 40; 41; 42; 43; 44; 45]. The primary focus is on building machine learning models, and the predictive variables taken into consideration for the construction of these models encompass several aspects. These aspects include the proportion of Recycled Aggregate (RA) employed as a substitute for Natural Aggregate (NA) in the mixture (expressed as a percentage of RA--RA%), the proportion of GGBFS from the total binder used in the mix and expressed as a percentage of GGBFS (GGBFS%), the amount of superplasticizer in kilograms (Sp (kg)), the ratio of water to binder (W/B), and the age of the concrete (in days). The response variable, in this case, is the Compressive Strength (CS) of what we refer to as 'eco-friendly concrete'. A concerted  
Figure 1: The functioning process of the BO-XGBoost model. effort was made to consolidate the results. The data were selectively gathered from studies that examined the effect of both GGBFS and RCA on the concrete CS. Additionally, while collating this data, the selection was limited to specimens that were strictly cube-shaped and measured either 100 mm or 150 mm in length. To standardize the CS across varying cube sizes, the proposed expressions from [46] were used. These expressions enable the conversion of the CS from a 100 mm cube mold to its equivalent in a 150 mm cube mold. Within the gathered data, it was noted that five of the records did not include either RA or GGBFS in their mixture proportions, which suggests these were standard concrete mixtures. Figure 2 showcases histograms of input and output variables that were used in the creation of the predictive models for CS. Table 1, on the other hand, presents descriptive data for all variables involved in the construction of the model. This table also outlines the typical mixing proportions used in the production of eco-friendly concrete, which incorporates both RA and GGBFS. As revealed in Table 1, the CS of concrete varies significantly, ranging from 9 MPa to 68 MPa, and corresponds to 7-day and 90-day specimens, respectively. Given that a regression tree forms the fundamental learning component of XGBoost, it eliminates the need for normalizing data samples [47]. This implies that the prediction outcome will remain unaffected even when features originate from diverse units. The factors presented in Table 1 are employed to create eco-friendly concrete. To ensure thorough understanding, a succinct explanation of each parameter is offered below. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Category** & **Statistics** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** & **Median** & **Maximum** & **Minimum** & **Kurtosis** \\ \hline Training data & W/B & 0.13 & 0.46 & 0.50 & 0.75 & 0.20 & 0.06 \\ \hline  & RA\% & 38.19 & 65.23 & 75.00 & 100.00 & 0.00 & \(-\)1.35 \\  & GGBFS\% & 31.72 & 38.36 & 40.00 & 100.00 & 0.00 & \(-\)0.76 \\  & Sp (kg) & 2.01 & 1.70 & 1.15 & 7.80 & 0.00 & 2.23 \\  & Age (days) & 29.37 & 32.13 & 28.00 & 90.00 & 7.00 & 0.00 \\  & CS (MPa) & 13.49 & 35.38 & 34.37 & 68.00 & 9.00 & \(-\)0.71 \\ \hline Testing data & W/B & 0.12 & 0.45 & 0.50 & 0.75 & 0.20 & 0.34 \\ \hline  & RA\% & 39.68 & 68.56 & 100.00 & 100.00 & 0.00 & \(-\)1.03 \\  & GGBFS\% & 31.39 & 33.89 & 40.00 & 100.00 & 0.00 & \(-\)0.80 \\  & Sp (kg) & 1.81 & 1.34 & 0.76 & 7.80 & 0.00 & 5.65 \\  & Age (days) & 31.77 & 42.36 & 28.00 & 90.00 & 7.00 & \(-\)1.21 \\  & CS (MPa) & 13.13 & 36.97 & 35.00 & 68.00 & 17.60 & \(-\)0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input and output key parameters for the dataset. Figure 2: A visual representation of the output and input parameters. The W/B ratio played a critical role in determining concrete strength. Study [25] suggested that higher W/B ratios and lower binder concentrations can lessen the adverse effects of RCA on the CS of concrete.