**4. Discussions**

The goal of this study was to add to the existing domain of research on the use of modern methods for evaluating the strength of RAC. This sort of exploration will benefit the building sector by allowing for the advancement of fast and cost-effective material property projection methods. Furthermore, by implementing these techniques to encourage environmentally friendly construction, the acceptance and usage of RAC in the building sector could be expedited. Figure 15 depicts the advantages of adopting RAC in the construction industry. Significant infrastructural renovation is required as a result of urbanization and industrialization, resulting in high volumes of construction and demolition waste. Therefore, desirable areas are turned into garbage ditches, land prices continue to rise, and trash dumping costs rise, with landfill space becoming increasingly rare. As a result, waste management has become of leading significance in emerging countries and is a global concern that demands long-term solutions. In addition, extracting and processing natural aggregates for concrete uses a lot of energy and produces a lot of CO2 [52]. Thus, using RAC in concrete production could result in lower energy consumption, resource conservation, building sustainability, cost savings, and a significant decrease in construction and demolition waste.

This research shows how machine learning methods may be used to forecast the compressive and flexural strength of RAC. The study employed two ensemble machine learning techniques—gradient boosting and random forest—to determine which technique is the most accurate predictor. The random forest model, with an R<sup>2</sup> of 0.91 for compressive strength and 0.86 for flexural strength prediction, suggested a higher precision compared to the gradient boosting model, which produced R2 of 0.87 and 0.79 for compressive and flexural strength prediction, respectively. Furthermore, the accuracy of all machine learning methods was tested through the use of k-fold and statistical methods. The model is more precise if there are fewer error values in it. However, selecting and suggesting the best machine learning model for forecasting outcomes in a range of fields is difficult, because a model's validity is highly dependent on the input factors and size of the dataset employed [53]. Ensemble machine learning techniques frequently take advantage of the weak learner by building 20 submodels that might be trained on data and altered to maximize the R<sup>2</sup> value. The random forest model has also been found to be more exact in forecasting the strength of concrete by other researchers [54–56] in terms of R2 and error values. Farooq et al. [54] compared the functioning of random forest with that of the artificial neural network, gene expression programming, and decision tree methods, and found that the random forest model, with an R<sup>2</sup> of 0.96, had a higher precision than the others. The reason for the higher accuracy of random forest is that it employs the bagging approach to combine all regression trees [57,58]. By minimizing the variation associated with prediction, bagging can increase prediction accuracy.

**Figure 15.** Benefits related to the adoption and application of recycled aggregate concrete.

Figure 16 depicts the R<sup>2</sup> value dispersion for the gradient boosting and random forest submodels. For gradient boosting compressive strength submodels, the lowest, average, and maximum R2 values were 0.818, 0.844, and 0.869, respectively. Additionally, the least, average, and highest R<sup>2</sup> values for the gradient boosting flexural strength submodels were noted to be 0.731, 0.762, and 0.793, respectively. Similarly, for random forest compressive strength submodels, the lowest, average, and highest R<sup>2</sup> values were 0.877, 0.907, and 0.915, respectively. Meanwhile, the least, average, and greatest R<sup>2</sup> values for the random forest flexural strength submodels were identified to be 0.803, 0.834, and 0.863, respectively. These findings revealed that the random forest submodels had greater R2 values than the gradient boosting submodels, indicating that the random forest model was more precise in estimating RAC's strength. A sensitivity analysis was also conducted to determine the effects of all inputs on the projected strength of RAC. The size of the dataset and the input parameters may have an impact on the model's performance. The sensitivity analysis determined the contributions of each of the 12 input parameters to the expected output. The three most important input factors were discovered to be the RCA replacement ratio, parent concrete strength, and weff/c.

**Figure 16.** Correlation coefficients (R2) of submodels.
