Experimental Investigation on Damage Behavior of Polypropylene Fiber Reinforced Concrete under Compression
Source abstract
Abstract
This paper presents an experimental investigation on the stress–strain behavior and the damage mechanism of polypropylene fiber reinforced concrete (PFRC) under monotonic and cyclic compression. Fifty-four specimens for different fiber volume fractions and aspect ratios were tested. Acoustic emission (AE) technique was used to monitor the damage progression. The damage mechanism of concrete was analyzed based on the AE parametric analysis. The results show that the incorporation of polypropylene fiber (PF) has a positive effect on the monotonic and cyclic behaviors of concrete, especially for the post-cracking branch. The toughness and ultimate strain are enhanced and the performance degradation in terms of elastic stiffness and strength is alleviated by the addition of PF. However, PF has little influences on the plastic strain, and the damage process of concrete is mainly driven by the envelope strain. The effect of fiber volume fraction on the cyclic behavior of concrete shows more pronounced than that of aspect ratio. In addition, it is found from AE results that the damage, closely related to AE events, has a quick evolution just after the peak stress, with the AE hits having a concentrated release. The total amount of AE hits increases with increasing fiber volume fraction due to fiber pullout and sliding, while the concrete with fiber aspect ratio of 280 reaches the largest amount. Meanwhile, as substantiated by AE, the failure of PFRC shows an obvious shear mode, with shear cracks dominating the damage progression. Finally, a damage elasto-plastic model is developed to predict the monotonic and cyclic responses of PFRC and the prediction yields a fairly close estimation with experimental results.
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Research context
OpenAlex topics: Structural Behavior of Reinforced Concrete · Innovative concrete reinforcement materials · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (62)
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