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Prediction of fiber orientation components of ultra-high-performance fiber-reinforced concrete

  • Su-Tae Kang (Department of Architecture Engineering, Daegu University) ;
  • Nilam Adsul (Department of Civil Engineering, Daegu University)
  • Received : 2024.07.29
  • Accepted : 2025.02.22
  • Published : 2025.02.25

Abstract

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is characterized by its exceptional flowability, achieved through the absence of coarse aggregate. However, this characteristic causes the fibers in UHPFRC to change direction depending on the initial pouring direction or structure shape, making it anisotropic and influencing its mechanical performance. This study adopts an innovative approach for predicting fiber orientation based on tensor analysis by simulating the casting process of cementitious materials. Understanding these changes can help optimize the structural performance of UHPFRC by leveraging its anisotropy and minimizing performance variability caused by uncontrolled fiber orientation. To generate the steady simple shear flow, fresh UHPFRC was poured from one end of the mould, allowing it to flow and distribute evenly. Different dosages of Water-Reducing Agent (WRA) were used to study their effects on fiber orientation and mechanical properties. The UHPFRC mixes were prepared with 2% steel fiber by volume of concrete (% vol.) and varying WRA dosages (1.2% to 3% of binder mass). The results revealed that optimal compressive and flexural tensile strengths were achieved at 1.2% and 1.8% WRA, while higher dosages (2.4% and 3%) led to a reduction in strength. Fiber orientation distribution was examined through image analysis at four distinct sections (35 mm, 110 mm, 220 mm, and 270 mm) from the flow-generating end. Additionally, an analytical modelling framework was developed using MATLAB to quantify fiber orientation through orientation tensors, fiber spacing (ac), and interaction coefficient (Cl) by selecting suitable values for the proportionality constant (K1). A strong correlation was observed between experimental and analytical results, with K1 values ranging from 0.013 to 0.015 and Cl values between 0.107 and 0.123, demonstrating the model's predictive accuracy. Overall, this research establishes a quantitative framework for fiber orientation prediction, enabling performance optimization through controlled casting techniques.

Keywords

Acknowledgement

This research was supported by the Daegu University Research Grant No. 2024-0423.

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