Abstract This study explores the possibilities of a new binding material, i.e., marble cement (MC) made from recycled marble. It will assess how well it performs when mixed with ash from rice husks and fly ash. This research analyzes flexural strength of marble cement mortar (FR-MCM), a mortar that incorporates MC, fly ash, and rice husk ash. A set of machine learning models capable of predicting CS and FS (flexural and compressive strengths) were developed. Gene expression programming (GEP) and multi-expression programming (MEP) are crucial in creating these types of models. Statistics, Taylor’s diagrams, R 2 values, and comparisons of experimental and theoretical results were used to evaluate the models. Stress testing also showed how different input features affected the model’s outputs. The accuracy of all GEP models was shown to fall within the acceptable range (R 2 = 0.952 for CS and R 2 = 0.920 for FS), and all MEP prediction models were determined to be exceptionally accurate (R 2 = 0.970 for CS and R 2 = 0.935 for FS). The statistical testing for error validation also verified that MEP models were more accurate than GEP models. According to sensitivity analysis, curing age and rice husk ash exerted the most significant influence on the prediction of CS and FS, followed by fly ash and MC.
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