Characterization of bending performance of reinforced cementitious composites beams with hybrid fibers after exposure to high temperatures

Li, Li; Guan, Junfeng; Xie, Yan Jun; Cao, Mingcui

doi:10.1002/suco.202100078

Cited by 16 publications

(12 citation statements)

References 43 publications

(92 reference statements)

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“…The addition of SF improves the mechanical properties of the RCAC; in particular, it better controls its fracture process [16]. It has been well confirmed that the incorporation of SF improves the working performance of RCAC, including better crack resistance, ductility, and toughness [17][18][19]. Also, the combination of SF and RCA Buildings 2023, 13, 2341 2 of 15 has great environmental and economic positive effects; SF can prevent the development of cracks in concrete through the bridging of the fibers and improve the strength and toughness of concrete [18,20,21].…”

Section: Introductionmentioning

confidence: 91%

“…It can be concluded that the curves are close to straight lines and nearly parallel to one another, which indicates that the three types of SF (MF, BF, and SWF) have very similar effects on the f fcu of SFNCAC and SFRCAC. This is because the compressive properties of SF-reinforced concrete are mainly controlled by the f fcu of the concrete matrix [19]. The same kind of SF has the same reinforcing effect on concrete whether the concrete matrix is NCAC or RCAC.…”

Section: Compressive Strengthmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Steel Fiber Volume Fraction and Morphology on the Strength of Recycled Aggregate Concrete

You,

Ma,

Zhang

et al. 2023

Buildings

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Steel-fiber-reinforced recycled coarse aggregate concrete (SFRCAC) has great potential for use in structural members due to environmental and economic reasons. A comparison of SF’s reinforcing effect on the strength of RCAC with natural recycled coarse aggregate concrete (NCAC) was conducted through experiments and analysis. Three types of steel fiber—milling (MF), shear-wave (SWF), and both-end hooked (BF)—were used. The SF volume fraction (Vf) was taken as 0, 0.5%, 1.0%, 1.5%, and 2%. The results show that SF has a similar reinforcing effect on NCAC and RCAC. The reinforcing effect of SF on the strength of RCAC is relevant to the strength of the RCAC matrix. The suitable content range of SF is from 0.5% to 1.5% in terms of the reinforcement effects on the compressive strength (ffcu) and splitting tensile strength (ffts) of RCAC. SF with a higher aspect ratio (AR) has a better reinforcing effect on the splitting tensile strength (ffts) and flexural strength (fftm) of RCAC. The equations of ffts/fts and fftm/ftm with the characteristic parameters of steel fiber (λf) were put forward to accurately determine the dosage of SF.

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Section: Introductionmentioning

confidence: 91%

Section: Compressive Strengthmentioning

confidence: 99%

Impact of Steel Fiber Volume Fraction and Morphology on the Strength of Recycled Aggregate Concrete

You,

Ma,

Zhang

et al. 2023

Buildings

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show abstract

“…15 Many previous studies have focused on the improvement of concrete properties. [16][17][18] Among them, some studies have investigated the τ BS of CSB. 19,20 According to the test results of the pullout specimen and the beam specimen, Patil et al 21 found that the bond strength decreases rapidly for pullout tests, but at a slower rate for beams with progressive increase in corrosion.…”

Section: Introductionmentioning

confidence: 99%

“…The corroded reinforced concrete performances have been studied widely 15 . Many previous studies have focused on the improvement of concrete properties 16–18 . Among them, some studies have investigated the τ BS of CSB 19,20 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning to estimate the bond strength of the corroded steel bar‐concrete

Wang,

Li,

et al. 2023

Structural Concrete

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Steel bar corrosion is prone to serious degeneration of the bond strength (τBS) of reinforced concrete, which leads to structural failure. There are many factors affecting the τBS; thus, the assessment of the τBS of corroded steel bar‐concrete becomes very complicated. In this research, a τBS evolution database of 430 samples containing 14 important factors, such as concrete compressive strength, yield strength and corrosion rate of steel bar, was established. Six machine learning algorithms were employed for comparative study to achieve optimal prediction results. The results showed that random forest and extreme random trees models have the best performance with coefficient of determination (R2) of 0.83 and 0.82, mean square error (MSE) of 13.59 and 13.98, root mean square error of 3.68 and 3.74, mean absolute error values of 2.35 and 2.31, and a20 values of 0.62 and 0.70, respectively. In addition, both models demonstrated shorter training times compared to the others. The running times are 589.75 and 418.31 ms, respectively. The superior feasibility and reliability of the machine learning algorithm were validated by contrasting with the computation of the empirical formulas. The empirical formulas exhibited higher error values, with the majority of MSE indicators exceeding 50 and the lowest R2 value even presenting negative values. Furthermore, feature importance calculations were used to understand the ability of the machine learning models to perceive each influencing factor. Among them, the corrosion rate, the yield strength of the longitudinal bar, and the stirrup emerged as the three most important influential factors.

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“…However, geopolymer concrete exhibits a high brittleness, which is a serious drawback [5]. In order to improve the mechanical properties of brittle materials, various types of fiber-reinforced geopolymer concrete have received extensive attention [6][7][8][9][10][11]. The fibers are added to the geopolymer not only to stop crack expansion, but also to bridge the cracks.…”

Section: Introductionmentioning

confidence: 99%

Surface Cracking and Fractal Characteristics of Bending Fractured Polypropylene Fiber-Reinforced Geopolymer Mortar

Sun

Zhang

et al. 2021

Fractal Fract

Self Cite

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Fiber is effective in restricting cracks and improving the toughness of geopolymer composites, but few studies have focused on the surface crack characteristics of fiber-reinforced geopolymer composites. In this paper, after flexural tests of polypropylene fiber-reinforced geopolymer mortar, the surface cracking image was collected by a digital camera and cracking information was extract by deep learning. Finally, the cracking and fractal characteristics were specifically discussed. The semantic segmentation network can accurately extract surface cracks for calculating various parameters. The results showed that the mean intersection over union (mIoU) and mean pixel accuracy (mPA) of the cracks are 0.8451 and 0.9213, respectively. Generally, the crack length, width, area, and fractal dimension of the specimen are all increased with the increase in the fiber volume fraction. These crack parameters grow rapidly when the fiber content is small, and the growth of the crack parameters gradually slows down as the fiber volume fraction increases to approximately 1.5%. The highest crack parameter values were found in the geopolymer mortar, with a 0.48 water–binder ratio and 12 mm fiber length. The variation of the bottom crack length and the side crack fractal dimension can be used to represent the overall crack variation patterns. Meanwhile, the crack parameters increase with the increased fiber factor in a quadratic function. Based on these crack parameters, the critical fiber factor and dense fiber factor of polypropylene fiber-reinforced geopolymer mortar were 200 and 550, respectively. They are greater than those of fiber-reinforced Portland cementitious composites. The influence of various crack parameters on the flexural strength is in the order of the crack area, width, length, and fractal dimension.

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Characterization of bending performance of reinforced cementitious composites beams with hybrid fibers after exposure to high temperatures

Cited by 16 publications

References 43 publications

Impact of Steel Fiber Volume Fraction and Morphology on the Strength of Recycled Aggregate Concrete

Impact of Steel Fiber Volume Fraction and Morphology on the Strength of Recycled Aggregate Concrete

Machine learning to estimate the bond strength of the corroded steel bar‐concrete

Surface Cracking and Fractal Characteristics of Bending Fractured Polypropylene Fiber-Reinforced Geopolymer Mortar

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