Computers and Concrete

  • 제34권3호
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  • Pages.367-378
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  • 2024
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Evaluation of direct tensile strength for ultra-high-performance concrete using machine learning algorithms

  • Sanghee Kim (Department of Architectural Engineering, Kyonggi University) ;
  • Woo-Young Lim (Department of Architectural Engineering, Wonkwang University)
  • 투고 : 2022.11.11
  • 심사 : 2024.02.26
  • 발행 : 2024.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study evaluates the direct tensile strength of ultra-high-performance concrete (UHPC) using tests. A total of 45 dogbone-shaped specimens are tested, with the test variables being the fiber volume fraction and notch length. The test results showed that the material properties of UHPC were largely dependent on the fiber volume fraction and compressive strength. When steel fibers with more than 1% fiber volume fraction are mixed in the manufacturing of UHPC, the tensile strength can be more than twice that of plain UHPC. In addition, the incorporation of steel fibers enabled the significant improvement of the initial cracking strength. However, the effect of the notch length on the tensile behavior was insignificant. An assessment of the direct tensile strength is conducted using machine-learning algorithms (ML). For evaluation of the direct tensile strength of UHPC using ML, a total of 98 test data, including 53 data from other research works and 45 data from this experimental program, were collected. In total, 67 data with a 70% confidence interval on a normal distribution curve were selected, with 47 data among 67 used for ML training and 20 data used for ML testing. As a result, the machine-learning algorithm with a steel fiber volume fraction predicted that the tensile strength has an average of 0.98 and the lowest values of regression evaluation metrics among analytical and ML-based models. It is considered that an ML-based model can help to predict a more accurate tensile strength of UHPC.

키워드

과제정보

This research was supported by a grant from Wonkang University (2023). The authors are grateful to those sponsors for their financial support. The opinions expressed in this paper are those of the authors and do not necessarily reflect those of the sponsors.

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