In recent years, an emerging technology termed, “High-Performance Fiber-Reinforced Concrete (HPFRC)” has become popular in the construction industry. The materials used in HPFRC depend on the desired characteristics and the availability of suitable local economic alternative materials. Concrete is a common building material, generally weak in tension, often ridden with cracks due to plastic and drying shrinkage. The introduction of short discrete fibers into the concrete can be used to counteract and prevent the propagation of cracks. Despite an increase in interest to use HPFRC in concrete structures, some doubts still remain regarding the effect of fibers on the properties of concrete. This paper presents the most comprehensive review to date on the mechanical, physical, and durability-related features of concrete. Specifically, this literature review aims to provide a comprehensive review of the mechanism of crack formation and propagation, compressive strength, modulus of elasticity, stress–strain behavior, tensile strength (TS), flexural strength, drying shrinkage, creep, electrical resistance, and chloride migration resistance of HPFRC. In general, the addition of fibers in high-performance concrete has been proven to improve the mechanical properties of concrete, particularly the TS, flexural strength, and ductility performance. Furthermore, incorporation of fibers in concrete results in reductions in the shrinkage and creep deformations of concrete. However, it has been shown that fibers may also have negative effects on some properties of concrete, such as the workability, which get reduced with the addition of steel fibers. The addition of fibers, particularly steel fibers, due to their conductivity leads to a significant reduction in the electrical resistivity of the concrete, and it also results in some reduction in the chloride penetration resistance of the concrete.

High-performance fiber-reinforced concrete: a review

Evaluation of mechanical properties of steel fiber reinforced concrete with different strengths of concrete

Recent trends in steel fibered high-strength concrete

The results of approximately 4000 published and unpublished tensile tests on deformed reinforcing bars are studied to obtain statistical relationships for various mechanical properties. The sample included No. 3 through No. 18, Grades 40 and 60, bars. The means and coefficients of variation of the mill test yield strengths were found to be 48.8 ksi (337 MN/sq m) and 10.7% for Grade 40 and 71 ksi (MN/sq m) and 9.3% for Grade 60 bars. Beta distributions were used to represent both of these sets of data. The static yield strength was found to be 3.5 ksi (24 MN/sq m) lower on the average than the mill test yield strength in both cases, with a coefficient of variation of 13.4%. The trend to lower yield strengths with increased bar sizes observed early in the 1970's was not present in more resent test data, apparently due to more stringent production control in recent years. /Authors/

Variability of Mechanical Properties of Reinforcing Bars

Lightweight aggregate concrete fiber reinforcement – A review

This research explains an experimental investigation of the concrete applying nonlinear ultrasonic testing technique. Eighteen cubic specimens, 150 150 150 mm, were prepared from three concrete batches with w/c of 40, 50, and 60%, respectively. The hardened concrete specimens after 28-day of curing were simultaneously subjected to uniaxial compression and nonlinear ultrasonic testing. The specimens were damaged by gradually loading them in compression in several steps representing 0, 20, 40, 60, and 80%, respectively, of the specimen's ultimate strength. At the end of each loading step ultrasonic evaluation was performed and the time domain waveforms were recorded at different power levels. Fast Fourier transformation FFT of the time domain waveforms was conducted to produce the frequency spectra. The data obtained from frequency spectra was used to study the change in signal amplitude or signal attenuation. It was found that attenuation is quite sensitive to different damage and power levels. The Fourier amplitudes obtained from FFT of the time domain data were normalized at fundamental frequency. Normalized spectra were used to examine the generation of second harmonic wave components. It was also found that the formation of second harmonic components are highly sensitive to increase in damage and change in power levels.

Nonlinear Ultrasonic Investigation of Concrete Damaged under Uniaxial Compression Step Loading

Effectiveness of nonlinear ultrasonic and acoustic emission evaluation of concrete with distributed damages

Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

Abid A. Shah

Papers

Recent trends in steel fibered high-strength concrete

Nonlinear Ultrasonic Investigation of Concrete Damaged under Uniaxial Compression Step Loading

Effectiveness of nonlinear ultrasonic and acoustic emission evaluation of concrete with distributed damages

Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network

Prediction of compressive strength of concrete using neural networks