A Multi‐Fiber Data‐Driven Framework for Predicting Impact Resistance of Fiber‐Reinforced Concrete Using Hybrid ANN Optimization

ABSTRACT In recent years, impact resistance has become a critical performance metric for fiber‐reinforced concrete (FRC), particularly in safety‐critical structures such as bridges, bunkers, and nuclear facilities. While drop‐weight impact tests provide a reliable evaluation, they are costly, destructive, and impractical for large‐scale design optimization. The aim of this study is to predict the impact resistance (N1 and N2) of fiber‐reinforced concrete using hybrid artificial neural network (ANN) models trained with genetic algorithm (GA) and Levenberg–Marquardt (LM) optimization techniques. The dataset comprises 117 experimental concrete samples, including 63 reinforced with polypropylene fibers, 45 with steel fibers, and 9 plain mixes for comparative assessment. Input features include fiber characteristics (type, length, tensile strength, aspect ratio), and concrete mix parameters. The results indicate that the developed ANN models successfully predicted the impact resistance of FRC with moderate predictive performance. Using the LM algorithm, the coefficients of determination (R) were as follows: N1—training (0.84094), testing (0.83542), validation (0.89993); N2—training (0.97655), testing (0.90333), validation (0.75404). Sensitivity analysis revealed that in the N1 scenario, Cement had the greatest influence, followed by fiber dosage and sand content, whereas in the N2 scenario, Fiber Dosage was the most influential parameter. These findings can assist engineers and designers in optimizing fiber‐reinforced concrete mix designs by focusing on the most influential parameters. Moreover, the developed model can significantly reduce the time and cost associated with extensive experimental testing and enable non‐destructive prediction of impact resistance across diverse mix designs and fiber types.