Constitutive behavior of hybrid fiber reinforced concrete subject to uniaxial cyclic tension: Experimental study and analytical modeling

Deng, Fangqian; Chi, Yin; Xu, Lihua; Huang, Le; Hu, Xuan

doi:10.1016/j.conbuildmat.2021.123650

Cited by 34 publications

(12 citation statements)

References 45 publications

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“…They also discussed plastic strain, stiffness deterioration, and damage index of the reinforced concrete with the help of their model. The model predictions agreed with the test results [ 38 , 39 , 40 ].…”

Section: Introductionsupporting

confidence: 78%

Modelling Fibre-Reinforced Concrete for Predicting Optimal Mechanical Properties

Khalel

Khan

2023

Materials

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Fibre-reinforced cementitious composites are highly effective for construction due to their enhanced mechanical properties. The selection of fibre material for this reinforcement is always challenging as it is mainly dominated by the properties required at the construction site. Materials like steel and plastic fibres have been rigorously used for their good mechanical properties. Academic researchers have comprehensively discussed the impact and challenges of fibre reinforcement to obtain optimal properties of resultant concrete. However, most of this research concludes its analysis without considering the collective influence of key fibre parameters such as its shape, type, length, and percentage. There is still a need for a model that can consider these key parameters as input, provide the properties of reinforced concrete as output, and facilitate the user to analyse the optimal fibre addition per the construction requirement. Thus, the current work proposes a Khan Khalel model that can predict the desirable compressive and flexural strengths for any given values of key fibre parameters. The accuracy of the numerical model in this study, the flexural strength of SFRC, had the lowest and most significant errors, and the MSE was between 0.121% and 0.926%. Statistical tools are used to develop and validate the model with numerical results. The proposed model is easy to use but predicts compressive and flexural strengths with errors under 6% and 15%, respectively. This error primarily represents the assumption made for the input of fibre material during model development. It is based on the material’s elastic modulus and hence neglects the plastic behaviour of the fibre. A possible modification in the model for considering the plastic behaviour of the fibre will be considered as future work.

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Section: Introductionsupporting

confidence: 78%

Modelling Fibre-Reinforced Concrete for Predicting Optimal Mechanical Properties

Khalel

Khan

2023

Materials

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“…Contributions of hybrid fiber on the cracking resistance at multiple length scales can result in the ductile failure mode of hybrid fiber-reinforced concrete material subjected to uniaxial cyclic tension. It was also found that increasing the steel fiber content had a considerable effect on enhancing the concrete cyclic tensile properties [29]. A combination of hybrid fibers and stirrups reinforcement can be provided in concrete to achieve enhanced shear behavior in reinforced concrete beams [30].…”

Section: Introductionmentioning

confidence: 99%

Influence of Fiber Hybridization on Strength and Toughness of RC Beams

Sahib¹,

Salim

2022

Civ Eng J

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This paper focuses on the experimental investigation designed to study the behavior of hybrid fiber-reinforced concrete (HFRC) beams under flexure and impact loading. The addition of fibers to concrete can improve a number of its properties. For optimal response, different types of fibers may be suitably combined to produce HFRC. Optimized combinations of different fiber types in concrete can produce a composite with better engineering properties than that with only one type. The study compared the mechanical properties of fresh and hardened HFRC, Steel Fiber Reinforced Concrete (SFRC), and conventional concrete to arrive at the optimum fiber content for improved behavior of concrete by testing 135 specimens. Subsequently, the behavior of steel fiber-reinforced concrete beams was investigated with and without fiber hybridization under flexural and impact loading, followed by a comparison of the results. Fiber hybridization was achieved by developing concrete containing a combination of steel and polypropylene fibers. Eighteen beam specimens of size 1650×200×150 mm were tested in the investigation. Test outcomes demonstrated that the inclusion of fibers in a hybrid form could ensure superior composite performance in terms of flexure and impact resistance when compared to the incorporation of a single type of fibers in reinforced concrete. Doi: 10.28991/CEJ-2022-08-03-010 Full Text: PDF

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“…Despite the method's costly and time-consuming activities, they produce accurate and relevant findings since cores are mechanically tested to failure. The test findings should be carefully evaluated, as core strengths depend on various variables, including the specimen's diameter, length, slenderness ratio, aggregate mixing, drilling direction, and reinforcing steel bars [1][2][3]. The core's diameter is crucial when analyzing the results of core strength tests.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning

Situmorang¹,

Alisjahbana²,

Riyanto³

et al. 2023

MMEP

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The slenderness ratio, length to diameter, of the cylindrical concrete samples of the slab block by the core-drilling method is believed to affect the compressive strength other than the aggregates in the concrete. In this study, the relationship between the compressive strength with mixing and slenderness ratio of cylindrical concrete specimens was investigated by statistics. Further, the discrimination model for mixing cylindrical concrete specimens has been developed by using machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and random forest (RF). A total of 180 cylindrical concrete specimens have been measured for compressive strength using UTM. The sample consisted of a mixture of type-A and type-B with a slenderness ratio of 2.48, 2.72, and 3.28, respectively. Samples were obtained by the core-drilling method from slab block concrete. The ANOVA tests showed that the aggregate and slenderness ratio caused a significant difference in the compressive strength of the concrete (p<0.05). This indicates that the type of aggregate mixture in concrete and the slenderness ratio of cylindrical concrete specimens significantly affect the compressive strength of the concrete. The model for discrimination of mixing cylindrical concrete specimens using machine learning algorithms can be used with satisfactory results. LDA is a machine learning algorithm that can show stability in the training and testing stages with accuracy reaching 78% and inconsistency of less than 2.63% (the smallest compared to others). The descending order of machine learning algorithms based on their consistency is LDA > RF > SVM > k-NN. Subsequently, this model can discriminate the aggregate mixture on cylindrical concrete specimens obtained from the core-drilling method.

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Constitutive behavior of hybrid fiber reinforced concrete subject to uniaxial cyclic tension: Experimental study and analytical modeling

Cited by 34 publications

References 45 publications

Modelling Fibre-Reinforced Concrete for Predicting Optimal Mechanical Properties

Modelling Fibre-Reinforced Concrete for Predicting Optimal Mechanical Properties

Influence of Fiber Hybridization on Strength and Toughness of RC Beams

Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning

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