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According to the presented literature, the scientific community is still uncertain about the CS behavior of SFRC. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. So, more complex ML models such as KNN, SVR tree-based models, ANN, and CNN were proposed and implemented to study the CS of SFRC. The factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. Therefore, as can be perceived from Fig. J. Comput. 147, 286295 (2017). This indicates that the CS of SFRC cannot be predicted by only the amount of ISF in the mix. Build. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. 103, 120 (2018). Normalised and characteristic compressive strengths in In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. Constr. Influence of different embedding methods on flexural and actuation Six groups of austenitic 022Cr19Ni10 stainless steel bending specimens with three types of cross-sectional forms were used to study the impact of V-stiffeners on the failure mode and flexural behavior of stainless steel lipped channel beams. Hu, H., Papastergiou, P., Angelakopoulos, H., Guadagnini, M. & Pilakoutas, K. Mechanical properties of SFRC using blended manufactured and recycled tyre steel fibres. J Civ Eng 5(2), 1623 (2015). Evidently, SFRC comprises a bigger number of components than NC including LISF, L/DISF, fiber type, diameter of ISF (DISF) and the tensile strength of ISFs. Cem. Build. A comparative investigation using machine learning methods for concrete compressive strength estimation. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. PubMed Central Eurocode 2 Table of concrete design properties - EurocodeApplied Struct. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. Khan, M. A. et al. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. 45(4), 609622 (2012). Golafshani, E. M., Behnood, A. Experimental Study on Flexural Properties of Side-Pressure - Hindawi Ray ID: 7a2c96f4c9852428 This web applet, based on various established correlation equations, allows you to quickly convert between compressive strength, flexural strength, split tensile strength, and modulus of elasticity of concrete. Today Proc. J. Zhejiang Univ. Res. 12). SVR model (as can be seen in Fig. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Moreover, among the proposed ML models, SVR performed better in predicting the influence of the SP on the predicted CS of SFRC with a correlation of R=0.999, followed by CNN and XGB with a correlation of R=0.992 and R=0.95, respectively.