A predictive modeling approach for determining optimal PPF content in concrete under specific mixture parameters using ANN and RSM

The incorporation of polypropylene fibers (PPF) enhances concrete’s flexural performance but often reduces workability, creating a critical trade-off in mix design. This study employs data-driven modeling using artificial neural networks (ANN) and response surface methodology (RSM) to determine the optimal PPF content by balancing flexural strength enhancement and workability retention. The ANN models achieved coefficient of determination (R2) values of 0.934 for flexural strength variation (FSV) and 0.852 for slump variation (SV), while RSM yielded R² of 0.885 and 0.896, respectively. A multi-objective optimization approach identifies PPF contents that balance strength improvement with minimal workability loss, providing practical guidance for the design of fiber-reinforced concretes in construction applications.