Abstract
In recent years, building materials made from agricultural waste have become popular due to their lower cost and environmental impact. The Bio-Brick is mixed with Cement-Fly Ash and Hydrated Lime and a fine aggregate of groundnut shell in percentages (20%, 30%, 40%, 50%, and 60%). The optimum mix proportions of Bio-Brick and hydrated lime mortar were found from the compressive strength and were further continued to study the dry density, water absorption, and efflorescence. Machine Learning techniques are used to optimize and predict the properties of Bio-Bricks and mortars. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) are employed to forecast properties such as compressive strength, dry density, and water absorption with exceptional accuracy. The results from RSM models exhibit high degrees of accuracy, with R-squared values exceeding 0.88 for compressive strength, dry density, and water absorption. ANN models further enhance this predictive power, with R-squared values exceeding 0.99 in predicting these critical properties.