Utilization of Waste Materials and Prediction Models in Concrete: A Comprehensive Study on Mechanical Properties and Structural Analysis

The study explores the use of various waste materials, including recycled steel fibers (RSF), coconut shells, E-waste particles, and machine-crushed animal bones (CAB), as partial or full replacements in concrete mixes. It also examines the use of artificial neural networks (ANNs) and finite element methods (FEM) for predicting compressive strength and stress analysis in concrete structures. Key findings indicate that incorporating RSF improves compressive strength but reduces workability, while coconut shells offer an eco-friendly aggregate alternative, achieving a compressive strength of 16.5 N/mm². E-waste replacement at 15% yielded optimal strength in M30 grade concrete. CAB aggregates provided lightweight concrete solutions but reduced overall strength. ANN models proved effective for predicting 28-day compressive strength, with significant accuracy. FEM analysis of concrete pavement highlighted critical bending stresses due to thermal and wheel loads. The study concludes that combining waste materials in concrete and advanced prediction models can enhance sustainability while maintaining desirable mechanical properties.