Build. Chou, J.-S. & Pham, A.-D. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. Eventually, among all developed ML algorithms, CNN (with R2=0.928, RMSE=5.043, MAE=3.833) demonstrated superior performance in predicting the CS of SFRC. Compressive Strength to Flexural Strength Conversion, Grading of Aggregates in Concrete Analysis, Compressive Strength of Concrete Calculator, Modulus of Elasticity of Concrete Formula Calculator, Rigid Pavement Design xls Suite - Full Suite of Concrete Pavement Design Spreadsheets. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner 36(1), 305311 (2007). Flexural strength = 0.7 x fck Where f ck is the compressive strength cylinder of concrete in MPa (N/mm 2 ). The flexural strength of concrete was found to be 8 to 11% of the compressive strength of concrete of higher strength concrete of the order of 25 MPa (250 kg/cm2) and 9 to 12.8% for concrete of strength less than 25 MPa (250 kg/cm2) see Table 13.1: In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. 324, 126592 (2022). MLR predicts the value of the dependent variable (\(y\)) based on the value of the independent variable (\(x\)) by establishing the linear relationship between inputs (independent parameters) and output (dependent parameter) based on Eq. East. For the prediction of CS behavior of NC, Kabirvu et al.5 implemented SVR, and observed that SVR showed high accuracy (with R2=0.97). The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. 27, 102278 (2021). Constr. Normalised and characteristic compressive strengths in Google Scholar. Evaluation metrics can be seen in Table 2, where \(N\), \(y_{i}\), \(y_{i}^{\prime }\), and \(\overline{y}\) represent the total amount of data, the true CS of the sample \(i{\text{th}}\), the estimated CS of the sample \(i{\text{th}}\), and the average value of the actual strength values, respectively.