Influence of polypropylene fibers on the mechanical properties of radiation shielding concrete with barite aggregates

Abstract

The brittleness of barite aggregates leads to inferior strength and toughness of radiation shielding concrete with barite aggregates (RSBC), which limits its application in high-pressure and high-heat nuclear engineering. This study aims to investigate the influence of polypropylene fibers on the mechanical properties of RSBC, and the effects of fiber type (PR40 and TMA20) and fiber content (3–9 kg/m3) on the mechanical properties of RSBC with water-to-binder (w/b) ratios of 0.44 and 0.39 are discussed. The microstructure of polypropylene fiber-reinforced RSBC was analyzed through scanning electron microscopy, revealing the underlying reinforcement mechanism of polypropylene fibers. The applicability of prediction formulas from typical codes was evaluated based on experimental data, and a reliable prediction model for the mechanical properties of RSBC was established. The findings showed that incorporating polypropylene fibers can significantly improve the compressive strength, splitting tensile strength, and flexural strength of RSBC. The addition of 9 kg/m3 PR40 and TMA20 fibers increased the 28-day compressive strength of RSBC by 17.9%–19.8% and 9.5%–16.2%, respectively; the 28-day splitting tensile strength increased by 12.1%–27.7% and 9.3%–26.1%, respectively; and the 28-day flexural strength increased by 19.3%–25.0% and 7.0%–13.5%, respectively. Adding PR40 fibers with a lower slenderness ratio yielded significantly better mechanical properties. Polypropylene fibers form a three-dimensional network structure in the concrete, acting as a bridge between the aggregate and cement mortar, enhancing the stability of interfacial transition zones, thereby effectively improving the macroscopic mechanical performance of RSBC. The mechanical property prediction models of EC 2 and GB 50010 for RSBC exhibit deviations, while the ACI 318 splitting tensile strength model and the flexural strength model established in this research demonstrate good predictive performance.