Unveiling the underlying mechanisms of tensile behaviour enhancement in fibre reinforced foam concrete

Fibre reinforcement is beneficial to control crack behaviour and the energy absorption ability of foam concrete. Several studies have investigated the flexural tensile behaviour of fibre-reinforced foam concrete to determine its ultimate peak strength gain. However, there remains a knowledge gap regarding the underlying mechanism of fibre influence on its tensile behaviour, as well as the impact of fibres on the pre and post-crack behaviour of foam concrete. This paper analysed and compared the flexural tensile behaviour and splitting tensile strength of PVA fibre-reinforced foam concrete. In addition, machine learning technology was used to develop regression models that described the importance of design parameters (foam concrete density, fibre length, diameter, and content), fibre distribution, and pore structure on foam concrete's tensile behaviour. The results show that the mechanism of fibre influence on pre-crack and post-crack flexural behaviour in foam concrete differs from that in normalweight concrete. In particular, contrary to the behaviour observed in normalweight concrete, PVA fibres noticeably enhanced the pre-crack flexural performance and splitting tensile strength of foam concrete. This paper found that this improvement is related to the effect of fibres on the pore structure in foam concrete, which in turn had a substantial impact on both pre-crack flexural behaviour and splitting tensile strength. Different from pre-crack behaviour (mainly influenced by pore structure), fibre content and size dominated post-crack behaviour of foam concrete. Optimising fibre size and content resulted in substantial improvements in split-ting tensile strength (up to 75.3% for high-density foam concrete and 49.9% for low-density foam concrete) and flexural strength (up to 44.4% for high-density foam concrete and 117.9% for low-density foam concrete).

Automated summary

This paper investigates the flexural tensile behavior and splitting tensile strength of PVA fiber-reinforced foam concrete, revealing the underlying mechanism of fiber influence on both pre-crack and post-crack behavior. Machine learning technology is utilized to develop regression models that describe the importance of design parameters, fiber distribution, and pore structure. The results show that PVA fibers significantly enhance the pre-crack flexural performance and splitting tensile strength of foam concrete, and this improvement is related to the effect of fibers on the pore structure.