Modeling the Mechanical Bending Behavior of Steel Fiber-Reinforced Self-Compacting Mortars: Analysis of Fiber Distribution Effect

This paper presents an experimental and numerical study on the effect of steel fiber distribution on the flexural behavior of self-compacting mortars (FRSCMs). Six FRSCM mixtures were modeled in ABAQUS as prismatic specimens (40 × 40 × 160 mm3) subjected to static three-point bending. The methodology involved two steps: (i) preparation of six mortar variants composed of three layers with different hooked steel fiber dosages (20, 30, and 40 kg/m3 for M20, M30, and M40) assembled in various configurations to study fiber distribution effects; (ii) numerical modeling of prismatic specimens in ABAQUS, using structured meshing with C3D8R hexahedral elements. Each layer was meshed separately with aligned nodes to ensure proper assembly. Our results highlight the strong influence of fiber distribution: despite identical fiber content (90 kg/m3 of hooked steel fibers), flexural strength varied across beam configurations. Layered casting led to an increase in flexural strength of up to 71.83% compared to the reference. The numerical predictions closely matched the experimental results, with relative errors ranging from 1% to 8.13% for most variants, demonstrating the reliability of the model. The larger discrepancies observed for specimens M324 and M342 are attributed to the limitation of the study to the elastic domain, as damage and plasticity effects were not included in the simulations. The distribution and orientation of fibers (particularly steel fibers) in a cementitious matrix, namely concrete or cement mortar, has been the subject of several studies aimed at determining the best mechanical performance of fiber-reinforced concrete. The proposed modeling approach of bending mechanical behavior allows us to predict the effects of fiber distribution in fluid mortars and reinforced self-compacting mortars, thereby reducing the need for extensive experimental testing. It also represents a significant improvement over existing approaches reported in the literature.