A data-based artificial intelligence for predicting compressive strength of rice husk ash sandcrete blocks

In the face of climate change, rapid population growth, and increasing resource limitations, promoting sustainable environmental practices has become a global priority. The use of rice husk ash (RHA) sandcrete blocks as an eco-friendly building material has attracted considerable interest in recent years. However, accurately predicting their engineering properties remains a challenging task. This study proposes an artificial intelligence-based approach to predict the compressive strength of RHA sandcrete blocks. A comprehensive dataset comprising 182 samples, each characterized by four input variables, was used to train and evaluate various models. The models assessed include multiple linear regression, support vector machine, and artificial neural network. The results demonstrated that all models provided reasonable accuracy in predicting compressive strength, with the artificial neural network model outperforming the others by achieving a coefficient of determination (R2) of 0.94. This high level of predictive accuracy highlights the potential of AI in enhancing the reliability and efficiency of material property predictions. By integrating AI technology into the assessment process, this study advances sustainable construction practices and supports the optimization of mix design and production strategies.