This research harnessed the potential of artificial neural networks (ANNs) to anticipate the characteristics of bricks derived from recycled soil. The study encompassed the production of bricks employing varying proportions of recycled soil, spanning from 0 to 50% with incremental steps of 10%. Subsequently, these bricks underwent exposure to both controlled and uncontrolled temperature conditions. Post-production, a curing process was initiated, followed by subjecting the bricks to comprehensive testing to evaluate their water absorption and compressive strength, a week after curing. Two distinct ANN models were accurately constructed and employed to predict the attributes of bricks post-burning under controlled and uncontrolled temperature settings. To gauge the accuracy and efficacy, the trained ANN model were assessed by analysing statistically, examining training graphs, and applying k-fold cross-validation techniques. The results showcased the capability of the ANN models in generating precise forecasts for water absorption and compressive strength values. Impressively, the ANN model exhibited high regression values of 0.99621 for bricks subjected to controlled temperatures and 0.99874 for those exposed to uncontrolled temperatures, underscoring the robustness and accuracy of the predictions.
