Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms

Considerable efforts have been made to increase the compressive strength of concrete by incorporating industrial by-products such as recycled aggregates and manufactured sand as partial substitutes for natural materials. However, predicting the compressive strength of concrete remains a challenge due to the influence of various factors, such as the type and proportion of aggregates, the water-cement ratio, and the age of the concrete. This research focuses on the development of machine learning (ML) models to predict concrete's compressive strength (CS) at 7 and 28 days. Fifteen input parameters—cement, natural and recycled fine and coarse aggregates, fly ash, manufactured Sand (M-Sand), water, admixture, w/c ratio, and age—were identified as critical factors influencing CS. A data set of 1030 samples from the literature was used, supplemented by additional experiments with recycled aggregates and manufactured sand. The models were trained on 70 % of the data, and the remaining 30% was used for testing. The results show that ML algorithms are highly effective in predicting CS, with the random forest algorithm achieving the highest accuracy (R² = 0,95; error = 3,74). In addition, a