Optimization of Concrete with Human Hair Using Experimental Study and Artificial Neural Network via Response Surface Methodology and Anova

Abstract

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 R2 values of 0.9434, 0.9365, and 0.9311 for CS, FS, and STS, respectively. ANOVA confirmed the significance of model inputs with p-values below 0.05. Furthermore, SEM, EDX, and XRD analyses validated the integration of HH into the concrete matrix and substantiated the observed mechanical properties. This study confirms the feasibility of HH as a sustainable fiber in concrete, enhancing critical performance metrics when applied at optimal dosages. The amalgamation of ANN, RSM, and ANOVA offers a thorough methodology for optimizing innovative concrete composites and clarifying the mechanisms underlying performance enhancement.