Effect of Steel Fiber Content on the Mesoscopic Damage Mechanism of Cemented Gangue Backfill
Year: 2026
Type: article
Abstract: To overcome the limitations of conventional numerical simulations of cemented gangue backfill (CGB), this study developed a refined PFC2D model that incorporates the actual particle size distributions of coal gangue and river sand. Randomly distributed steel fibers were generated using FISH programming. Based on uniaxial compression tests and scanning electron microscopy (SEM) observations, the influence of steel fibers on the mesoscopic damage mechanism of CGB is systematically investigated. The results indicate that: (1) the refined model significantly improves the reliability of numerical simulations, accurately reproducing stress concentration within coarse aggregates and the steel fiber "bridging effect"; (2) a steel fiber volume fraction of 0.8% optimizes force chain distribution and suppresses crack propagation, promoting a transition in failure mode from brittle shear failure to ductile compressive-extrusion failure mode, with the peak strength and residual strength increased by 23.7% and 40.2%, respectively, compared with the fiber-free specimen; (3) PFC simulations reveal that steel fibers markedly retard damage accumulation by modifying the force chain network and crack propagation paths; and (4) SEM analysis demonstrates that steel fibers enhance the toughening effect through the interfacial transition zone, whereas excessive fiber content (1.2%) leads to fiber agglomeration and a 62.5% increase in porosity, resulting in performance deterioration. This study provides a robust theoretical framework for gradation reconstruction and refined fiber modeling in the design of roadside backfill materials.
Source: Materials
DOI: 10.3390/ma19153217
Institution: China University of Mining and Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G2789003102, G8100877824
OpenAlex ID: W7171535510
