The utilization of Recycled Coarse Aggregate (RCA) in concrete has gained signifi-cant traction due to its environmental and economic advantages. However, ensuring the quality of RCA poses challenges as it is influenced by various unpredictable factors includ-ing the high water absorption of RCA, ineffective recycling processes, and the presence of contaminants. The existing body of research on the influence of RCA on the Compressive Strength (CS) of concrete has yielded inconsistent findings, and limited knowledge exists regarding the specific combination of parameters that enable effective control over CS. To address this gap, the present study aims to identify the essential parameters that contribute to controlling CS in concrete through the development of a predictive model. By investigat-ing these crucial parameters, this research intends to extent current knowledge on optimiz-ing the use of RCA in concrete.
To investigate the impact of the crucial parameters on the CS of concrete when uti-lizing RCA, a series of experiments were conducted. The RCA was obtained through the selective demolition recycling technique. The content of RCA was divided through manual separation into unbound stones, Low-Quality Recycled Aggregate (LQRA), and contami-nants. LQRA is composed of residual mortar and stones with mortar attached to their sur-face. The experiments included physical properties tests and optimization of the concrete mix designs. Additionally, relevant literature was consulted to identify the parameters that would serve as variables in constructing the predictive model. Through analysis via re-sponse surface methodology, a predictive model was developed to assess the impact of these critical parameters on the CS of concrete.
The experimental findings confirmed the statistical significance of the predictive model in assessing the impact of critical parameters on the CS of concrete. The level of LQRA was found to have a negative impact on the quality of RCA. The water-to-cement ratio was identified as a significant factor affecting the CS of concrete, with lower ratios yielding higher CS. When using RCA with high LQRA content (up to 65% of the total weight of RCA) as a substitute for natural coarse aggregate, higher replacement ratios re-sulted in lower CS.
In order to further validate the predictive model, Artificial Neural Network (ANN) modelling was incorporated as a non-linear method of assessing the relationship between the variables and the output, which is the CS. The high R2 values obtained from the ANN model demonstrated the robust alignment between the model and the data, strengthening its reliability. The integration of a Pareto chart and model-fitting regression gives a better physical understanding of the results of the predictive model by identifying influential terms and reducing complexity. The resulting model improves interpretability and predic-tive accuracy. The analyses emphasize the significance of integrating ANN and the Pareto chart approach in enhancing model validation and simplification.
These findings offer valuable insights into the parameters that are crucial to the CS of concrete which consists of RCA. By implementing the procedures that assess the quality of RCA, sustainable construction practices can be promoted, and the wider application of RCA can be facilitated on an industrial scale.

Recycled aggregate concrete is emphasized more and more nowadays due to its importance towards the construction industry and general welfare of our planet. Being such a significant feature with regard to universal sustainable development, every aspect of its properties should be evaluated, examined and optimized so that there is a well-known, well-described and ready to use product. This project aimed at correlating some of the established parameters of conventional concrete such as water absorption of the coarse aggregate fraction and the resulting compressing strength, only applied in the field of recycled aggregate concrete. Along with these two properties, a prediction model was sought for which would be able to forecast the mechanical strength of concrete based on its coarse aggregate composition. A series of experiments were performed on samples fabricated for water absorption tests and compressive strength assessment. In total, 112 water absorption samples and over 250 concrete specimens were prepared and examined for the respective attribute. These samples used natural aggregate as the base of the coarse aggregate portion of the mix design, alongside a series of so-called contaminants which replaced the gravel in certain concentrations. These contaminants included bricks, ceramic tiles, glass, wood, gypsum, plastics, mineral fibers and recycled sand concrete and were entirely based upon the C&DW composition globally. Further investigation was done on the inclusion of air within concrete so that contaminated concrete results could be equated to this constant. All water absorption tests were performed according to the current standards and concrete specimens were crushed after 7 and 28 days of curing in order to obtain their strength in compression, while the predictive model was initiated via the Minitab statistical software.
The results indicated some interesting and promising trends. From both sets of experiments, it was evident that the water absorption of the coarse aggregate fraction was not the main contributor towards the strength development of recycled concrete. It had a minor influence, especially compared to the type of contaminant present and how much volume it took. Furthermore, samples with identical water absorption fabricated concretes with different strengths. Overall, plastics and wood had the most negative effect in terms of compressive strength, while bricks, tiles and glass seemed to affect this aspect in neutral or even slightly positive manner. EPS foam in very limited amounts yielded a notable 30 to 35% strength drop, while on the other hand, brick replacing 20% of the NA improved the strength by approximately 7% after 7 days and 2% after 28 days. In the end, based on all experimental input, a predictive model was developed, optimized and validated in several steps so that it was able to predict the water absorption of coarse aggregate, compressive strength after 7 and 28 days and equivalent air content based on the composition of the coarse aggregates. The back-end of the model is provided within the report as a MATLAB code.