## 2 Related Works  
In the exploration and application of machine learning methods in concrete science and technology, researchers have developed innovative strategies for optimizing and improving RCA concrete. One study utilized machine learning models to predict and optimize RCA concrete compressive strength, using a large experimental dataset. Gradient BoostingRegression Trees (GBRT) and Deep Learning had superior predictive performance. GBRT, coupled with Particle Swarm Optimization (PSO), proposed economically and environmentally optimized mixture designs [10]. Another study used Gene Expression Programming (GEP) and Artificial Neural Network (ANN)-supervised machine learning to predict the compressive strength of RCA concrete. GEP showed better performance than ANN [11]. One study used hybrid machine learning algorithms to predict the compressive strength of RCA concrete. The interval type-2 fuzzy inference system (IT2 FIS) outperformed type-1, with concrete age, natural fine aggregate ratio, and superplasticizer-to-binder ratio impacting positively on strength [12]. Also, a study employed ensemble machine learning methods to predict sulfate resistance in RCA concrete. Extreme gradient boosting performed the best, indicating that RCA concrete sulfate resistance is most affected by dry-state environmental conditions. A graphical user interface was also developed for practical applications [13]. Furthermore, a Back Propagation Neural Network (BPNN) model demonstrated superior performance in predicting rubber-modified recycled aggregate concrete's properties, achieving correlation coefficients of 0.9721 for compressive strength and 0.9441 for peak strain [14]. Another study utilized machine learning techniques to predict 28-day concrete compressive strength in self-compacting concrete (SCC) using recycled aggregates. The best-performing models, based on R\({}^{2}\) values and Mean Squared Error (MSE), are Category Boosting, K-Nearest Neighbors, and Extra Trees [15]. Last but not least, an interesting study used machine learning to explore the relationship between recycled aggregate properties, mix proportion, and compressive strength of recycled aggregate concrete. Two optimized hybrid models (PSO-SVR and GWO-SVR) outperformed standard algorithms (ANN and SVR), but GWO-SVR risked overfitting. Cement content, water content, natural fine aggregates, and water absorption were identified as significant influencing factors [16]. In recent research efforts to predict the compressive strength of concrete made with GGBFS, machine learning has been a fundamental tool. One study used a range of machine learning techniques and a substantial dataset made up of 625 experimental outcomes [17]. The accuracy of these models was thoroughly assessed using performance indicators and sensitivity analyses. The end result of this thorough analysis was the formulation of equations intended to assist in estimating easily the strength. Other research successfully predicted the compressive strength of environmentally friendly alkaline-activated slag concrete (AASC) using ANN, with the most accurate model having an R\({}^{2}\) of 0.9817 [18]. This promotes sustainability and stimulates the usage of AASC in the construction industry. In a separate investigation, the predictive capabilities of a regularized multivariate polynomial regression (MPR) model was harnessed to estimate the compressive strength of GGBFS-metakaolin-based geopolymer pastes [19]. Providing robust estimations, the model exhibited commendable performance with R\({}^{2}\) values of 0.946 and 0.927 for the 7-day and 28-day strengths, respectively, thereby confirming its reliability in this specific context. Lastly, the Random Forest (RF) model was highly accurate in predicting the compressive strength of concrete with GGBFS, with a correlation coefficient of 0.9729, a Root Mean Squared Error (RMSE) of 4.9585, and a Mean Absolute Error (MAE) of 3.9423 on the testing dataset [20].