Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm

The thermal power industry, as a major consumer of hard coal, significantly contributes to harmful emissions, affecting both air quality and soil health during the operation and transportation of ash and slag waste. This study presents the modeling of aerated concrete using local raw materials and ash-and-slag waste in seismic areas through machine learning techniques. A comprehensive literature review and comparative analysis of normative documentation underscore the relevance and feasibility of employing non-autoclaved aerated concrete blocks in such regions. Machine learning methods are particularly effective for disjointed datasets, with neural networks demonstrating superior performance in modeling complex relationships for predicting concrete strength and density. The results reveal that neural networks, especially those with Bayesian Regularisation, consistently outperformed decision trees, achieving higher regression values (R strength = 0.9587 and R density = 0.91997) and lower error metrics (MSE, RMSE, RIE, MAE). This indicates their advanced capability to capture intricate non-linear patterns. The study concludes that artificial neural networks are a robust tool for predicting concrete properties, crucial for producing non-autoclaved curing wall blocks suitable for earthquake-resistant construction. Future research should focus on optimizing the balance between density and strength of blocks by enhancing the properties of aerated concrete and utilizing reliable models.