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  5. Influence of Fiber Orientation and Hybrid Ratios on Tensile Response and Hybrid Effect of Hybrid Steel Fiber-Reinforced Cementitious Composites

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Preprint
en
2024

Influence of Fiber Orientation and Hybrid Ratios on Tensile Response and Hybrid Effect of Hybrid Steel Fiber-Reinforced Cementitious Composites

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en
2024
DOI: 10.2139/ssrn.4816885

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Zhongya Zhang
Zhongya Zhang

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Ziyi Wang
Xiaokang Wang
Zhongya Zhang
+4 more

Abstract

This study employed magnetic field induction and steel fiber hybridization methods to prepare Hybrid Aligned Steel Fiber-Reinforced Cementitious Composites (HASFRCCs). The direct tensile performance of specimens with aligned and randomly dispersed steel fibers was compared under different hybrid coarse-to-fine fiber ratios (3:1, 2:1, 1:1, 1:2, and 1:3). The fiber pullout tests were conducted to determine the bond-stress-slip relationship between steel fibers and matrix for both coarse and fine steel fibers. Based on these results, an analytical model for the tensile behavior of HASFRCC was then developed based on the composite mechanics theory and modified according to the hybrid fiber effect. The results demonstrated a significant increase in the fiber orientation coefficient ηθ of the HASFRCC by 21.1% to 26.9% compared with the random specimens. Additionally, the tensile strength and energy absorption capacity substantially improved by 43.8% to 64.1% and 58.5% to 71.4%, respectively, compared to the specimens with random steel fiber. In addition, the modified model of HASFRCC quantitatively reveals the role of the two kinds of fibers in the tensile process, and the difference between the model-predicted and experimental results was less than 10%.

How to cite this publication

Ziyi Wang, Xiaokang Wang, Zhongya Zhang, Yang Zou, Jiang Du, Raffaele Cucuzza, Jun Yang (2024). Influence of Fiber Orientation and Hybrid Ratios on Tensile Response and Hybrid Effect of Hybrid Steel Fiber-Reinforced Cementitious Composites. , DOI: https://doi.org/10.2139/ssrn.4816885.

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Publication Details

Type

Preprint

Year

2024

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.2139/ssrn.4816885

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