| Abstrakt: |
The study presented a shear strength equation for high-strength high-performance fiber-reinforced concrete (HS-HPFRC), including ultra-high-performance concrete (UHPC). This equation was designed for straightforward implementation, catering to the regular tasks of engineers. It considers various influences on sheartransfer mechanisms, including fiber bridging, fiber distribution, dowel action, cross-sectional shapes, and beam size effects. The equation does not rely on uniaxial tensile tests or inverse analysis of flexural tests; instead, it considers the statistical impact of fibers on shear strength. To generate the coefficients for this semi-empirical closed-form equation, an evaluation database of 118 HS-HPFRC and UHPC beams was constructed. The evaluation results revealed that the proposed equation has a mean of 1.00 and a correlation coefficient of 0.92, indicating low variation and high predictive accuracy. Furthermore, it outperformed existing equations and matched the accuracy of the machine learning (ML)-based models including support vector machines (SVM), random forest (RF), and artificial neural network (ANN), despite its comparatively simpler expression. [ABSTRACT FROM AUTHOR] |
