Experimental–FEM Validation of ANN Models for One-Way HFRC Slabs

Cement concrete is the most widely utilized construction material globally, recognized for its versatility, workability, and ability to create complex structural and non-structural components. Despite its extensive application, plain concrete exhibits inherent weaknesses, including low tensile strength, limited crack resistance, and brittleness caused by internal microcracking. These limitations highlight the need for enhancements in conventional cement concrete to satisfy the diverse structural and durability requirements of civil engineering. The incorporation of fibers in specific proportions has shown significant improvements in the mechanical properties of concrete. This study presents the validation of Artificial Neural Network (ANN) models, trained on 145 High-Performance Fiber-Reinforced Concrete (HFRC) samples, alongside the corresponding experimental results for one-way HFRC slabs and Finite Element Method (FEM) analyses on the behavior of 21 one-way slabs with varying volumetric proportions of steel and polypropylene fibers. One-way slabs underwent testing using a four-point bending method. Structural performance was evaluated based on strain, deflection, crack initiation, and ultimate failure load. Steel fibers were added in proportions ranging from 0.7% to 1.0% by volume, while polypropylene fibers were incorporated in increments of 0.1% to 0.9% at 0.2% intervals. Both Finite Element Analysis (FEA) and ANN predictions exhibited a strong correlation with experimental outcomes for the ultimate flexural load and mid-span deflection of the 21 reinforced concrete beams. FEA demonstrated remarkable quantitative precision, with minimal deviation from experimental results and a robust simulation of complex mechanical behaviors such as damage accumulation and ductility.