flexural strength to compressive strength converter

J. Enterp. In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. The air content was found to be the most significant fresh field property and has a negative correlation with both the compressive and flexural strengths. It's hard to think of a single factor that adds to the strength of concrete. Constr. Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) All data generated or analyzed during this study are included in this published article. Appl. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842. Article As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. Accordingly, 176 sets of data are collected from different journals and conference papers. Normalised and characteristic compressive strengths in & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. Constr. Therefore, the data needs to be normalized to avoid the dominance effect caused by magnitude differences among input parameters34. A parametric analysis was carried out to determine how well the developed ML algorithms can predict the effect of various input parameters on the CS behavior of SFRC. Article Table 4 indicates the performance of ML models by various evaluation metrics. Technol. Concr. However, regarding the Tstat, the outcomes show that CNN performance was approximately 58% lower than XGB. Importance of flexural strength of . Midwest, Feedback via Email 2020, 17 (2020). 308, 125021 (2021). Company Info. Mater. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. volume13, Articlenumber:3646 (2023) Mater. SVR is considered as a supervised ML technique that predicts discrete values. Date:1/1/2023, Publication:Materials Journal 324, 126592 (2022). 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Among these tree-based models, AdaBoost (with R2=0.888, RMSE=6.29, MAE=4.433) and XGB (with R2=0.901, RMSE=5.929, MAE=4.288) were the weakest and strongest models in predicting the CS of SFRC, respectively. 49, 20812089 (2022). Also, a significant difference between actual and predicted values was reported by Kang et al.18 in predicting the CS of SFRC (RMSE=18.024). The compressive strength and flexural strength were linearly fitted by SPSS, six regression models were obtained by linear fitting of compressive strength and flexural strength. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. Compressive strength, Flexural strength, Regression Equation I. However, the understanding of ISF's influence on the compressive strength (CS) behavior of . Comparing ML models with regard to MAE and MAPE, it is seen that CNN performs superior in predicting the CS of SFRC, followed by GB and XGB. Build. Struct. Plus 135(8), 682 (2020). Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. Whereas, Koya et al.39 and Li et al.54 reported that SVR showed a high difference between experimental and anticipated values in predicting the CS of NC. Google Scholar. In fact, SVR tries to determine the best fit line. Mater. The flexural response showed a similar trend in the individual and combined effect of MWCNT and GNP, which increased the flexural strength and flexural modulus in all GE composites, as shown in Figure 11. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Buy now for only 5. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. The correlation coefficient ($R$) is a statistical measure that shows the strength of the linear relationship between two sets of data. Where an accurate elasticity value is required this should be determined from testing. [1] & Lan, X. Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Struct. Figure No. Mater. 11, and the correlation between input parameters and the CS of SFRC shown in Figs. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Skaryski, & Suchorzewski, J. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. 73, 771780 (2014). A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. 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. 1 and 2. Sci Rep 13, 3646 (2023). The reviewed contents include compressive strength, elastic modulus . Heliyon 5(1), e01115 (2019). & Hawileh, R. A. Behbahani, H., Nematollahi, B. Eng. Mech. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). The value of flexural strength is given by . The factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. As shown in Fig. Convert. Intersect. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Appl. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. The flexural strength is stress at failure in bending. Sanjeev, J. The authors declare no competing interests. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete, $$R_{XY} = \frac{{COV_{XY} }}{{\sigma_{X} \sigma_{Y} }}$$, $$x_{norm} = \frac{{x - x_{\min } }}{{x_{\max } - x_{\min } }}$$, $$\hat{y} = \alpha_{0} + \alpha_{1} x_{1} + \alpha_{2} x_{2} + \cdots + \alpha_{n} x_{n}$$, $y = \left\langle {\alpha ,x} \right\rangle + \beta$, $$net_{j} = \sum\limits_{i = 1}^{n} {w_{ij} } x_{i} + b$$, https://doi.org/10.1038/s41598-023-30606-y. PubMed Deng, F. et al. R2 is a metric that demonstrates how well a model predicts the value of a dependent variable and how well the model fits the data. Difference between flexural strength and compressive strength? Hameed et al.52 developed an MLR model to predict the CS of high-performance concrete (HPC) and noted that MLR had a poor correlation between the actual and predicted CS of HPC (R=0.789, RMSE=8.288).