Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning

), correlation coefficient (R), willmott index (WI), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and symmetric mean absolute percentage error (SMAPE) between predicted and calculated values of the output. At the end, machine learning has been found to be a transformative approach that enhances the efficiency, cost-effectiveness, and sustainability of evaluating compressive strength in industrial wastes-based concrete reinforced with steel fiber. Among the models reviewed, Kstar and DT emerge as the most practical for achieving precise and sustainable results. Their adoption can significantly reduce environmental impacts and promote the sustainable use of industrial by-products in construction. The sensitivity of the input variables on the compressive strength of industrial wastes-based concrete reinforced with steel fiber produced 36% from C, 71% from W, 70% from FAg, 60% from CAg, 34% from PL, 5% from SF, 33% from FA, 67% from Vf, 5% from FbL, and 61% from 61%. Fiber Volume Fraction (Vf) (67%) high sensitivity suggests that steel fiber content greatly impacts crack resistance and tensile strength. Steel Fiber Orientation (61%) indicates the importance of fiber alignment in distributing stresses and enhancing structural integrity.