journal article·2022-06Open access

Study on dispersion uniformity and performance improvement of steel fibre reinforced lightweight aggregate concrete by vibrational mixing

Guangqi Xiong, Chong Wang, Shuai Zhou, Yalin Zheng

Abstract

Steel fibres (SFs) easily cluster at high dosages during mixing, which directly limits the performance of concrete. Recently, vibrational mixing has provided a novel perspective to overcome the defects of ordinary mixing. However, the influence of vibration effects on steel fibre reinforced lightweight aggregate concrete (SFRLAC) has not been investigated. In this paper, SFRLAC was prepared based on ordinary mixing and vibrational mixing, respectively. Different volume dosages of SFs (0%, 0.5%, 1.0%, 1.5% and 2.0%) were selected as variables. The flowability test, the dry density test and the mechanical properties test were used to characterise the macroscopic performance of the concrete. After the macroscopic test of SFRLAC, the distribution and orientation of SFs were quantified by integrated digital image processing (DIP) technology. The interfaces of the SFs and the cement matrix were characterised by environmental scanning electron microscopy (ESEM). The obtained results revealed that vibrational mixing could improve flowability. Meanwhile, it can increase the compressive strength and flexural toughness of SFRLAC continuously after SFs volume exceeds 1.0%. Moreover, the technology was conducive to dispersing SFs at high dosages and made the orientation more consistent than the ordinary mixing. Notably, a dense paste film appeared on the fibre surface, suggesting a significant improvement in the load capacity. Thus, vibrational mixing can be used to prepare the reinforced concrete with high SFs contents.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Innovative concrete reinforcement materials · Innovations in Concrete and Construction Materials · Advanced ceramic materials synthesis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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