**3. Results and Analysis**


The results of the gradient boosting model for RAC's compressive strength are shown in Figure 6a,b. Figure 6a depicts the relationships between the experimental and anticipated results. The gradient boosting approach yielded findings with a satisfactory level of accuracy and a lower distinction between the experimental and projected values. The R2 of 0.87 signifies that the gradient boosting model is reasonably precise at forecasting the compressive strength of RAC. The distribution of forecast and error values for the gradient boosting compressive strength model is presented in Figure 6b. The discrepancy between experimental and estimated values was found to be between 0.00 and 27.96 MPa (44.52% deviation), with an average of 4.78 MPa (11.67%). Additionally, the divergence from the experimental outcomes was less than 1 MPa for 27 mixes, between 1 and 3 MPa for 32 mixes, between 3 and 6 MPa for 32 mixes, between 6 and 10 MPa for 21 mixes, and greater than 10 MPa for 16 mixes. These deviations indicate that the gradient boosting model's predicted results deviated less from the experimental results. As a result, the gradient boosting technique is quite accurate at predicting RAC's compressive strength.

**Figure 6.** Gradient boosting model for compressive strength: (**a**) relationship between experimental and predicted results; (**b**) spreading of predicted and error values.

#### 3.1.2. Flexural Strength

Figure 7a,b provides a comparison of the experimental and predicted outcomes of the gradient boosting model for the flexural strength of RAC. The correlation between experimental and estimated findings is exemplified in Figure 7a, where an R2 of 0.79 indicates that the gradient boosting model for the flexural strength is less specific than for the compressive strength estimation of RAC. This reduced R2 is due to the lower number of data points used for forecasting the flexural strength compared to the compressive strength. The distribution of estimated and error values for the gradient boosting flexural strength model is represented in Figure 7b. The difference between experimental and estimated values was discovered to be between 0.00 and 4.27 MPa (89.27% deviation), with an average of 5.86 MPa (11.44%). Furthermore, the difference from the experimental outcomes was less than 1 MPa for 22 mixes and greater than 1 MPa for 6 mixes. These deviation values suggest a moderate disparity between the gradient boosting model's projected and experimental outcomes. As a result, the gradient boosting approach predicts RAC's flexural strength less accurately compared to its precision in foretelling the compressive strength of RAC.

**Figure 7.** Gradient boosting model for flexural strength: (**a**) relationship between experimental and predicted results; (**b**) spreading of predicted and error values.

#### *3.2. Random Forest Model*

#### 3.2.1. Compressive Strength

The outcomes of the random forest model for the compressive strength of RAC are presented in Figure 8. In Figure 8a, an R<sup>2</sup> value of 0.91 indicates that the random forest model outperforms the gradient boosting model in this study in terms of precision. The dispersion of projected and error values for the random forest compressive strength model is shown in Figure 8b. The variation (error) between experimental and estimated values was found to range between 0.07 and 25.57 MPa (39.28% variation), with an average of 4.19 MPa (10.50% variation). Furthermore, the difference from the experimental outcomes was less than 1 MPa for 18 mixes, between 1 and 3 MPa for 41 mixes, between 3 and 6 MPa for 39 mixes, between 6 and 10 MPa for 22 mixes, and larger than 10 MPa for only 8 mixes. These values show that the difference between experimental and expected outcomes is less compared to the gradient boosting model. As a result, the random forest approach is superior for assessing the compressive strength of RAC with the greatest precision.

**Figure 8.** Random forest model for compressive strength: (**a**) relationship between experimental and predicted results; (**b**) spreading of predicted and error values.

#### 3.2.2. Flexural Strength

The experimental and anticipated outcomes of the random forest model for the flexural strength of RAC are shown in Figure 9. Figure 9a represents the relationships between experimental and projected outcomes, with an R2 of 0.86 indicating that the random forest model for the flexural strength is less specific than the compressive strength prediction of RAC. This reduced R2 is because there are fewer data points used to forecast the flexural strength than the compressive strength. Figure 9b indicates the distribution of estimated and error values for the random forest flexural strength model. The discrepancy between experimental and estimated values ranged from 0.02 to 2.24 MPa (34.46 variances), with an average of 0.56 MPa (10.43% variance). Moreover, for 23 mixes, the variation from the experimental outcomes was less than 1 MPa, whereas it was greater than 1 MPa for only 5 mixes. These values indicate a lower difference between the random forest model's predicted and experimental results. As a result, the random forest technique is more accurate in forecasting RAC's flexural strength than the gradient boosting model.

**Figure 9.** Random forest model for flexural strength: (**a**) relationship between experimental and predicted results; (**b**) spreading of predicted and error values.
