journal article·2024-12Open access

Flexural and fracture characterization of UHPGC notched beam with various glass sand replacement ratios and steel fiber contents

Yubo Jiao, Qifan Wu, Heping Liu, Hua Yang, Lijun Xu, Menghan Fang

Abstract

The preparation process of quartz sand (QS) can cause serious environmental pollution, which is particularly important to find a new replacement material for QS in cement-based construction materials. In this study, an economical and environmentally friendly ultra-high performance glass sand concrete (UHPGC) was prepared by partially replacing QS in conventional ultra-high performance concrete (UHPC) with glass sand (GS) ground from waste glass. The effects of material composition characteristics including water-binder ratio (W/B), GS replacement ratio, and steel fiber content on the flexural and fracture characteristics of UHPGC notched beams were investigated based on response surface method (RSM) and acoustic emission (AE) monitoring. The results indicate that the GS replacement rate presents almost no effect on the tensile strength and flexural toughness of the specimens while reducing the water-binder ratio or increasing the steel fiber content can significantly improve the flexural tensile strength of UHPGC notched beams. UHPGC notched beams show the best performance when the W/B is 0.17, the steel fiber content is 3 %, and the GS replacement rate is 50 %. By analyzing and comparing the AE parameters and fracture modes, the discrepancies in flexural and fracture properties of UHPGC notched beams with variable material composition characteristics can be elucidated. Among them, the low water-binder ratio or high steel fiber content has a significant effect on the fracture characteristics and fracture process of the specimens, while the GS replacement rate exhibits negligible effect.
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Research context

OpenAlex topics: Innovative concrete reinforcement materials · Structural Behavior of Reinforced Concrete · Advanced ceramic materials synthesis

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

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