Experimental-AI approach (hybrid) for evaluating the strength characteristics of eco-efficient concrete incorporating coal bottom ash.

Thermal power stations generate large amount of coal bottom ash (CBA), which is mostly disposed of in landfills, creating environmental hazards. At the same time, the continual use of natural aggregate in concrete is depleting resources. Present study aims to address both these issues by utilizing coal bottom ash as partial substitute to river sand and quarry sand at values between 0% and 100%. Experimental results indicate that workability decreases with increasing CBA contain. The mixes made using river sand (CBA1) had their compressive and split tensile strengths close to 5% of those of the control mixes for higher curing ages. In contrast, the mixes made using quarry sand (CBA2) had a compressive strength that was 6% to 25% less than that of the control mixes at the 28-day curing period depending on the percentage of CBA used, whereas there was a drop to around ± 0% to 7% after 180 days of curing. Among different models developed, effective prediction approach within the considered experimental dataset Gradient Boosting outperformed the other models, showing the highest accuracy (R² = 0.99) and also with the least number of prediction errors. The outcome of present research suggests that CBA may serve as a durable material and that advanced machine learning techniques might accurately forecast its strength.