Modeling smeared fiber-matrix pullout strength of steel fiber-reinforced concrete with various types of steel fiber and its tensile and compressive strength
This paper presents a novel smeared fiber-matrix pullout strength model for predicting the mechanical properties of steel fiber-reinforced concrete (SFRC). The model, developed within the fiber bridging stress framework, uniquely accounts for various fiber types (straight, hooked-end, and twisted) a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525000415 |
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| Summary: | This paper presents a novel smeared fiber-matrix pullout strength model for predicting the mechanical properties of steel fiber-reinforced concrete (SFRC). The model, developed within the fiber bridging stress framework, uniquely accounts for various fiber types (straight, hooked-end, and twisted) and their interaction with the cementitious matrix. The approach integrates critical factors including fiber orientation, embedment length, and matrix strength to provide a comprehensive understanding of the fiber bridging mechanism. Based on a database of 965 single-fiber pullout tests, formulas were we derived for predicting both tensile and compressive strengths of SFRC with single or hybrid steel fiber configurations. The model’s effectiveness is validated using separate databases containing 321 tensile and 1595 compressive strength test results across a wide range of concrete strengths (16.7–262 MPa). The model demonstrates adequate predictive capability, particularly for straight and hooked-end fibers, while showing some limitations for twisted fibers and fiber volume fractions above 3 %. This physically-significant approach not only provides a reliable tool for designing and optimizing SFRC materials but also offers new insights into the mechanisms of fiber reinforcement in concrete. |
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| ISSN: | 2214-5095 |