Quantitative analysis of steel fibers on UHPFRC uniaxial tensile behavior using X-CT and UTT

Abstract

Steel fiber is usually used to improve ultra-high-performance fiber-reinforced concrete (UHPFRC) tensile properties and remedy its brittle failure. X-ray CT scanning (X-CT) was used to observe the orientation and distribution of steel fibers inside UHPFRC. It revealed that the steel fiber inside UHPFRC distribution angle is not equal probability, and the probability is higher along the pouring direction in uniaxial tensile specimen mold. The uniaxial tensile tests (UTT) of 19 UHPFRC specimens with different fiber volume fractions and aspect ratios were performed to analyze the steel fiber on the tensile response and tension failure patterns of UHPFRC. Test results indicated that with an increase in the volume fraction or aspect ratio of steel fiber, the first-cracking strength, tensile strength and peak tensile strain of UHPFRC increase; while they have different growth rates. In addition, with the increase of volume fraction and aspect ratio, the failure patterns of UHPFRC tension specimens gradually changed from brittle failure with single crack to ductile failure with multiple cracks. The stress superposition compositions of tensile strength were different under different failure patterns. Combining mechanical analysis and numerical statistical analysis, prediction equations of the tensile strength of strain softening UHPFRC and pseudo-strain hardening UHPFRC considering the effect steel fiber parameters were respectively proposed. Through the mesoscopic equivalent model analysis of UHPFRC uniaxial tensile specimens, the functional relationships between the effect of steel fiber parameters on the first-cracking strength and peak tensile strain were analyzed, and then their prediction equations were proposed, combined with numerical statistical analysis. The calculation accuracy of the series of prediction equations proposed in this paper were verified by the collected literature data. Meanwhile, comparing the prediction equations proposed in this paper with the existing equations in the literature, the equations proposed in this paper were better.