Calculating the elastic moduli of steel-fiber reinforced concrete using a dedicated empirical formula

Teng, Tso–Liang; Chu, Yi-An; Chang, Fwu‐An; Chin, Hua-Sheng

doi:10.1016/j.commatsci.2004.04.003

Cited by 33 publications

(15 citation statements)

References 10 publications

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“…It is complicated and often impossible to precisely describe the concrete's microstructure, which includes different constituents at different length scales (Bernard et al., 2003; Constantinides and Ulm, 2004; Gu et al., 2020; Li et al., 2020). In this paper, as shown in Figure 1, FRC is seen as the four-phase composite consisting of the fibers, the bulk cement phase, the aggregates, and the interfacial transition zones (ITZs) between them (Chen et al., 2016; Dutra et al., 2010; Gal and Kryvoruk, 2011; Teng et al., 2004). The aggregate is supposed to be spherical (Chen et al., 2016; Dutra et al., 2010; Gal and Kryvoruk, 2011; Teng et al., 2004).…”

Section: Deterministic Micromechanical Frameworkmentioning

confidence: 99%

“…On one hand, the common probability density functions (PDFs) are employed to represent the FRC's real statistical characters, which will lead to the biased results (Bai and Liu, 2016; Jaynes, 1957; Li et al., 2014; Zhu et al., 2015). On the other hand, the micromechanics-based models were presented to set up the relationship between the microstructure and the macroscopic properties, which do not consider the material's random behavior (Chen et al., 2016, 2019; Dutra et al., 2010; Galand and Kryvoruk, 2011; Ju and Yanase, 2011; Ju and Zhang, 1998; Teng et al., 2004). To address these issues, this study aims to develop a stochastic multiphase micromechanical framework to investigate the unbiased probabilistic behavior of the FRC's moduli with Legendre orthogonal polynomial.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the unbiased probabilistic behavior of the fiber-reinforced concrete's elastic moduli using stochastic micromechanical approach

Zhu

et al. 2020

International Journal of Damage Mechanics

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The probabilistic behavior of the fiber-reinforced concrete is usually represented by the common probability density functions, which will lead to the biased results. This study aims to develop a stochastic multiphase micromechanical framework with Legendre orthogonal polynomial to investigate the unbiased probabilistic behavior of the fiber-reinforced concrete's moduli. The different phase volume fractions are analytically calculated based on the aggregate grading and the material's effective properties are quantitatively reached by employing the multilevel micromechanical homogenization schemes. The Monte Carlo simulations are adopted to attain the different order moments of fiber-reinforced concrete's effective properties, with which the unbiased probability density functions are reached by using the Legendre orthogonal polynomial approximations and the linear transformations. Numerical examples indicate that the proposed framework is accurate and computationally efficient to characterize the fiber-reinforced concrete's probabilistic behavior.

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Section: Deterministic Micromechanical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of the unbiased probabilistic behavior of the fiber-reinforced concrete's elastic moduli using stochastic micromechanical approach

Zhu

et al. 2020

International Journal of Damage Mechanics

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“…The Halpin-Tsai model has been extensively used to predict the elastic modulus of brittle and ductile materials containing CNTs such as polymers, metals and ceramics [128-130, 132, 149]. Also, the Halpin-Tsai model was used to evaluate the effective elastic moduli of steel-fiber reinforced concrete [125]. Each step (model) has its own assumptions and findings to further simplify the previous researchers' findings.…”

Section: Elastic Modulusmentioning

confidence: 99%

Design and predicting performance of carbon nanotube reinforced cementitious materials : mechanical properties and dispersion characteristics.

Ramezani¹

2020

Preprint

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Recently, Carbon Nanotubes (CNTs) are drawing considerable attention of researchers for reinforcing cementitious materials due to their excellent mechanical properties and high aspect ratio (length-to-diameter ratio). However, CNTs might not disperse well within the cement matrix, resulting in little improvement or even degradation of concrete properties. The uncertainty in producing the consistent results in different studies might be attributed to multiple interactions between the experimental variables affecting the nanotube dispersion and the final properties of CNT-cement nanocomposites. Therefore, this research mainly focused on proposing equations that can reliably capture these interactions in order to correlate CNT dispersion with the mechanical properties. The main experimental variables studied included CNT concentration, aspect ratio, ultrasonication energy, ultrasonication amplitude, surfactant-to-CNTs ratio, water-to-cement ratio, sand-to-cement ratio, and hydration age of specimen. The study reported in this research was conducted in two parts: experimental program and modeling. In the experimental part of this research, a total of 63 different mix proportions were used to evaluate the flowability, mechanical properties, and durability characteristics of cement pastes and mortars containing CNTs. Using experimental test results reported in this study and the literature, three critical relations were proposed to consider the CNT dispersion, cement matrix composition, and hydration age of cement. The proposed critical relations were then added to available theoretical models in the literature. The flexural strength and elastic modulus of CNT-cement nanocomposites were predicted through a state-of-the-art probabilistic model using a Bayesian methodology. Finally, the developed probabilistic models were used to identify the optimum ranges of the experimental variables to maximize the mechanical properties. This was done through computing the conditional probability of not meeting the specified design requirement. The experimental results indicated that addition of CNTs could significantly improve different properties of cementitious materials, if the optimum range of each variable was used. Also, to achieve the desired mechanical properties, various combinations of the experimental variables might be used. The proposed prediction models were shown to capture the interactions between the experimental variables for predicting the mechanical properties within ±15% and ±18% of the experimental test results for flexural strength and elastic modulus, respectively. Based on the findings of this research, contour plots were developed to provide practical guidelines for future engineers to design CNT-cement nanocomposites.

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“…Švec et al [22] studied influences of formwork surface on the final orientation and mechanical properties of steel fibre reinforced selfcompacting OPC concrete. Teng et al [23] developed an empirical based modelling for prediction elastic moduli of steel fibre reinforced concrete.…”

Section: Introductionmentioning

confidence: 99%

FEA modelling of fracture toughness of steel fibre-reinforced geopolymer composites

Sanjayan

Pouraliakbar²

2015

Materials & Design

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Calculating the elastic moduli of steel-fiber reinforced concrete using a dedicated empirical formula

Cited by 33 publications

References 10 publications

Investigation of the unbiased probabilistic behavior of the fiber-reinforced concrete's elastic moduli using stochastic micromechanical approach

Investigation of the unbiased probabilistic behavior of the fiber-reinforced concrete's elastic moduli using stochastic micromechanical approach

Design and predicting performance of carbon nanotube reinforced cementitious materials : mechanical properties and dispersion characteristics.

FEA modelling of fracture toughness of steel fibre-reinforced geopolymer composites

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