## 5 Conclusions and Future Recommendations  
This study investigated the relationship between the compressive strength of concrete and the moisture content of RCA using machine learning techniques. Furthermore, various machine learning models were employed to comprehensively understand the impact of RCA moisture content on predicting concrete performance metrics. A literature review was conducted to explore the relationship between RCA moisture content and concrete compressive strength, based on which a database consisting of 752 items was constructed. Subsequently, a predictive model for RCA compressive strength was developed using seven machine learning models, and evaluation metrics were used to assess its performance. Through the comprehensive construction of the database and the application of seven machine learning models, including XGBoost and LightGBM, the study developed a predictive model for RCA compressive strength. Evaluation results demonstrated that the LightGBM model outperformed other models in terms of R\({}^{2}\) values, RMSE, MAE, and generalization performance, thereby proving to be the optimal choice for this study. Feature importance and interaction plot analyses were conducted to investigate how moisture content affects compressive strength. The analysis revealed that \"Age\", \"Water/Cement \" and \"Fine RCA Ratio\" were the most influential features, in line with prior research. Surprisingly, the moisture condition of the RCA ranked relatively low in importance, indicating that factors like water-to-cement ratio and aggregate content had a greater influence. Interaction plot analysis highlighted the significance of water-to-cement ratio, aggregate ratios, and moisture conditions on compressive strength. Notably, reducing the water-to-cement ratio consistently led to higher compressive strength. Maintaining optimal aggregate ratios, both fine and coarse, proved crucial for enhancing compressive strength. Additionally, controlling moisture within specific ranges, particularly avoiding extremes like fully saturated surface dry (SSD) states, was crucial for maximizing compressive strength. These findings support established research, emphasizing the importance of these factors in concrete mixture design for achieving desired mechanical properties. Overall, this study fills the gap in predicting concrete performance metrics considering RCA moisture content and provides valuable insights for optimizing concrete mixtures containing recycled aggregates. These findings underscore the importance of comprehensive parameter consideration and the use of machine learning techniques in enhancing predictive models for concrete technology. From a recommendation perspective, further research is needed to understand the impact of various recycled materials on concrete performance and to develop optimal mixtures. Among these, slag, generated during the steel manufacturing process, stands out as a valuable recycled material for concrete production. Properly processed, slag can enhance concrete quality and provide environmental benefits. Leveraging innovative technologies like machine learning to optimize the utilization of recycled materials holds significant promise in promoting sustainable construction and material production. These efforts are expected to contribute to achieving sustainable architecture and material production by enhancing environmental protection and resource efficiency. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14083512/s1](https://www.mdpi.com/article/10.3390/app14083512/s1)]([https://www.mdpi.com/article/10.3390/app14083512/s1](https://www.mdpi.com/article/10.3390/app14083512/s1)), The Supplementary Materials for this article include Python Code and Database Source. S.-W.C.: Investigation, Writing--original draft, Writing--review and editing. S.E.C. : Data curation, Software, Validation, Visualization, Writing--original draft. A.S.B.