Optimization of concrete with human hair using experimental study and artificial neural network via response surface methodology and ANOVA.

The increasing demand for sustainable construction materials has prompted the investigation of non-biodegradable waste, such as human hair (HH), for concrete reinforcement. This study seeks to evaluate the impact of HH fiber on the fresh, physical, and mechanical characteristics of concrete. HH was incorporated in varying proportions (1-5% by weight of cement), along with modifications in cement content, to ascertain optimal performance conditions. An extensive experimental program was executed, succeeded by the utilization of Artificial Neural Networks (ANN) to formulate predictive models for compressive strength (CS), flexural strength (FS), and splitting tensile strength (STS). Furthermore, Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) were utilized to identify statistically significant factors and optimize the mix design. The findings indicated that the mechanical performance of concrete enhanced with HH inclusion up to 3%, after which a deterioration ensued, presumably due to inadequate dispersion and workability challenges. The ANN models precisely predicted mechanical outcomes, while the RSM-derived models demonstrated strong correlations, with R