**5. Conclusions**

This study aimed to employ two ensemble machine learning algorithms to anticipate the compressive and flexural strength of recycled aggregate concrete (RAC). Gradient boosting and random forest were chosen to achieve the study's goals. The dataset containing the strength of RAC of 638 mixes was collected, of which all contained compressive strength results and 139 contained flexural strength results. Both gradient boosting and random forest models were employed to predict the compressive and flexural strength of RAC, and their accuracy was compared. The conclusions of this study are as follows:


natural aggregate size, and water absorption of the natural aggregate accounting for 11.6%, 8.7%, 8.1%, 6.5%, 5.0%, 3.7%, 2.8%, 2.5%, and 2.3%, respectively;

5. This sort of study will benefit the building sector by allowing for the advancement of rapid and cost-effective techniques for estimating the strength of materials. Furthermore, by encouraging computational techniques, the adoption and application of RAC in the building sector will be accelerated.

This study proposes that future studies should use experimental research, mixture proportions, field trials, and other numerical assessment methods to increase the amount of data points and findings (e.g., Monte Carlo simulation). Furthermore, to enhance the models' responsiveness, environmental characteristics (e.g., elevated/low temperature and humidity) and a full description of the raw materials may be included as input variables.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/ma15082823/s1, Table S1: Data used for modeling. References [59–121] are cited in the Supplementary Materials.

**Author Contributions:** X.Y., data curation, visualization, writing—original draft; Y.T., resources, investigation, supervision, writing—review and editing; W.A., conceptualization, software, methodology, validation, supervision, writing—original draft; A.A., resources, methodology, validation, formal analysis, writing—review and editing; K.I.U., funding acquisition, visualization, project administration, writing—review and editing; A.M.M., formal analysis, investigation, writing—review and editing; R.K., resources, methodology, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of the World-Class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17 November 2020).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used in this research has been properly cited and reported in the main text.

**Acknowledgments:** This work was supported by the Natural Science Foundation of Guangdong Province (2018A030313499) and the National Natural Science Foundation of China (51578343).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

