Mater. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. 48331-3439 USA 313, 125437 (2021). Struct. Invalid Email Address. To perform the parametric analysis to analyze the influence of one specific parameter (for example, W/C ratio) on the predicted CS of SFRC, the actual values of that parameter (W/C ratio) were considered, while the mean values for all the other input parameters values were introduced. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. Flexural strength calculator online - We'll provide some tips to help you select the best Flexural strength calculator online for your needs. Young, B. & Hawileh, R. A. This research leads to the following conclusions: Among the several ML techniques used in this research, CNN attained superior performance (R2=0.928, RMSE=5.043, MAE=3.833), followed by SVR (R2=0.918, RMSE=5.397, MAE=4.559). & Tran, V. Q. Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. Dao, D. V., Ly, H.-B., Vu, H.-L.T., Le, T.-T. & Pham, B. T. Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete. 3.4 Flexural Strength 3.5 Tensile Strength 3.6 Shear, Torsion and Combined Stresses 3.7 Relationship of Test Strength to the Structure MEASUREMENT OF STRENGTH . The result of this analysis can be seen in Fig. 3- or 7-day test results are used to monitor early strength gain, especially when high early-strength concrete is used. To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. As is reported by Kang et al.18, among implemented tree-based models, XGB performed superiorly in predicting the CS of SFRC. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. Where the modulus of elasticity of the concrete is required to complete a design there is a correlation equation relating flexural strength with the modulus of elasticity, shown below. To obtain Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. consequently, the maxmin normalization method is adopted to reshape all datasets to a range from \(0\) to \(1\) using Eq. S.S.P. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Eng. 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. These measurements are expressed as MR (Modules of Rupture). It is a measure of the maximum stress on the tension face of an unreinforced concrete beam or slab at the point of. Privacy Policy | Terms of Use 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. Khan, M. A. et al. This method has also been used in other research works like the one Khan et al.60 did. Eng. Moreover, GB is an AdaBoost development model, a meta-estimator that consists of many sequential decision trees that uses a step-by-step method to build an additive model6. Tree-based models performed worse than SVR in predicting the CS of SFRC. 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. Constr. The flexural strength of UD, CP, and AP laminates was increased by 39-53%, 51-57%, and 25-37% with the addition of 0.1-0.2% MWCNTs. The value for s then becomes: s = 0.09 (550) s = 49.5 psi Moreover, in a study conducted by Awolusi et al.20 only 3 features (L/DISF as the fiber properties) were considered, and ANN and the genetic algorithm models were implemented to predict the CS of SFRC. 2.9.1 Compressive strength of pervious concrete: Compressive strength of a concrete is a measure of its ability to resist static load, which tends to crush it. Build. Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. Build. Build. Mater. Various orders of marked and unmarked errors in predictions are demonstrated by MSE, RMSE, MAE, and MBE6. 12, the SP has a medium impact on the predicted CS of SFRC. Difference between flexural strength and compressive strength? Constr. The use of an ANN algorithm (Fig. A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? Constr. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Development of deep neural network model to predict the compressive strength of rubber concrete. It uses two general correlations commonly used to convert concrete compression and floral strength. In fact, SVR tries to determine the best fit line. Tanyildizi, H. Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine. Compressive strength prediction of recycled concrete based on deep learning. However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). Google Scholar. The site owner may have set restrictions that prevent you from accessing the site. 38800 Country Club Dr. 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). A. The test jig used in this video has a scale on the receiver, and the distance between the external fulcrums (distance between the two outer fulcrums . & Lan, X. Caution should always be exercised when using general correlations such as these for design work. Unquestionably, one of the barriers preventing the use of fibers in structural applications has been the difficulty in calculating the FRC properties (especially CS behavior) that should be included in current design techniques10. Provided by the Springer Nature SharedIt content-sharing initiative. The same results are also reported by Kang et al.18. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Mater. Shade denotes change from the previous issue. Cem. Consequently, it is frequently required to locate a local maximum near the global minimum59. This online unit converter allows quick and accurate conversion . 2020, 17 (2020). As shown in Fig. Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. Compressive strength, Flexural strength, Regression Equation I. The Offices 2 Building, One Central [1] It is observed that in comparison models with R2, MSE, RMSE, and SI, CNN shows the best result in predicting the CS of SFRC, followed by SVR, and XGB. According to section 19.2.1.3 of ACI 318-19 the specified compressive strength shall be based on the 28-day test results unless otherwise specified in the construction documents. RF consists of many parallel decision trees and calculates the average of fitted models on different subsets of the dataset to enhance the prediction accuracy6. & Nitesh, K. S. Study on the effect of steel and glass fibers on fresh and hardened properties of vibrated concrete and self-compacting concrete. Constr. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: Asadi et al.6 also used ANN in estimating the CS of NC containing waste marble powder (LOOCV was used to tune the hyperparameters) and reported that in the validation set, ANN was unable to reach an R2 as high as GB and XGB. Compos. 163, 376389 (2018). Characteristic compressive strength (MPa) Flexural Strength (MPa) 20: 3.13: 25: 3.50: 30: Li, Y. et al. Further information can be found in our Compressive Strength of Concrete post. Compressive strength result was inversely to crack resistance. Also, Fig. Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. Civ. 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. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. Setti et al.12 also introduced ISF with different volume fractions (VISF) to the concrete and reported the improvement of CS of SFRC by increasing the content of ISF. Therefore, owing to the difficulty of CS prediction through linear or nonlinear regression analysis, data-driven models are put into practice for accurate CS prediction of SFRC. percent represents the compressive strength indicated by a standard 6- by 12-inch cylinder with a length/diameter (L/D) ratio of 2.0, then a 6-inch-diameter specimen 9 inches long . Ati, C. D. & Karahan, O. The new concept and technology reveal that the engineering advantages of placing fiber in concrete may improve the flexural . A 9(11), 15141523 (2008). The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. & Kim, H. Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. 2018, 110 (2018). ; The values of concrete design compressive strength f cd are given as . Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. 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. However, it is depicted that the weak correlation between the amount of ISF in the SFRC mix and the predicted CS. Concr. Email Address is required 12). The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. However, the understanding of ISFs influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. Build. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. However, it is worth noting that their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Since the specified strength is flexural strength, a conversion factor must be used to obtain an approximate compressive strength in order to use the water-cement ratio vs. compressive strength table. Flexural strength = 0.7 x fck Where f ck is the compressive strength cylinder of concrete in MPa (N/mm 2 ). Get the most important science stories of the day, free in your inbox. Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. Mahesh et al.19 noted that after tuning the model (number of hidden layers=20, activation function=Tansin Purelin), ANN showed superior performance in predicting the CS of SFRC (R2=0.95). Mech. CAS Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. The CivilWeb Flexural Strength of Concrete suite of spreadsheets includes the two methods described above, as well as the modulus of elasticity to flexural strength converter. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. PubMed Central This algorithm first calculates K neighbors euclidean distance. In other words, the predicted CS decreases as the W/C ratio increases. Date:7/1/2022, Publication:Special Publication In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. Behbahani, H., Nematollahi, B. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses 118 (2021). 1. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. 34(13), 14261441 (2020). Struct. Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Skaryski, & Suchorzewski, J. In comparison to the other discussed methods, CNN was able to accurately predict the CS of SFRC with a significantly reduced dispersion degree in the figures displaying the relationship between actual and expected CS of SFRC. 37(4), 33293346 (2021). Adv. Mater. The testing of flexural strength in concrete is generally undertaken using a third point flexural strength test on a beam of concrete. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Mater. & Xargay, H. An experimental study on the post-cracking behaviour of Hybrid Industrial/Recycled Steel Fibre-Reinforced Concrete. According to Table 1, input parameters do not have a similar scale. Geopolymer recycled aggregate concrete (GPRAC) is a new type of green material with broad application prospects by replacing ordinary Portland cement with geopolymer and natural aggregates with recycled aggregates. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. 33(3), 04019018 (2019). Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. Is there such an equation, and, if so, how can I get a copy? Phone: +971.4.516.3208 & 3209, ACI Resource Center Build. Build. New Approaches Civ. Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength. Kang, M.-C., Yoo, D.-Y. Marcos-Meson, V. et al. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. de-Prado-Gil, J., Palencia, C., Silva-Monteiro, N. & Martnez-Garca, R. To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). Constr. Flexural strength of concrete = 0.7 . http://creativecommons.org/licenses/by/4.0/. However, regarding the Tstat, the outcomes show that CNN performance was approximately 58% lower than XGB. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. In many cases it is necessary to complete a compressive strength to flexural strength conversion. Correlating Compressive and Flexural Strength By Concrete Construction Staff Q. I've heard about an equation that allows you to get a fairly decent prediction of concrete flexural strength based on compressive strength. Mater. Materials 13(5), 1072 (2020). Constr. For CEM 1 type cements a very general relationship has often been applied; This provides only the most basic correlation between flexural strength and compressive strength and should not be used for design purposes. It is seen that all mixes, except mix C10 and B4C6, comply with the requirement of the compressive strength and flexural strength from application point of view in the construction of rigid pavement. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. There is a dropout layer after each hidden layer (The dropout layer sets input units to zero at random with a frequency rate at each training step, hence preventing overfitting). 94, 290298 (2015). Sci. The sensitivity analysis demonstrated that, among different input variables, W/C ratio, fly ash, and SP had the most contributing effect on the CS behavior of SFRC, followed by the amount of ISF. The flexural strength of a material is defined as its ability to resist deformation under load. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. the input values are weighted and summed using Eq. 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. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. In the current study, The ANN model was made up of one output layer and four hidden layers with 50, 150, 100, and 150 neurons each. Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Technol. Martinelli, E., Caggiano, A. This can be due to the difference in the number of input parameters. Constr. Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Zhu, H., Li, C., Gao, D., Yang, L. & Cheng, S. Study on mechanical properties and strength relation between cube and cylinder specimens of steel fiber reinforced concrete. The rock strength determined by . This is particularly common in the design and specification of concrete pavements where flexural strengths are critical while compressive strengths are often specified. Based on the results obtained from the implementation of SVR in predicting the CS of SFRC and outcomes from previous studies in using the SVR to predict the CS of NC and SFRC, it was concluded that in some research, SVR demonstrated acceptable performance. Mater. To avoid overfitting, the dataset was split into train and test sets, with 80% of the data used for training the model and 20% for testing. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). The focus of this paper is to present the data analysis used to correlate the point load test index (Is50) with the uniaxial compressive strength (UCS), and to propose appropriate Is50 to UCS conversion factors for different coal measure rocks. For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). The flexural properties and fracture performance of UHPC at low-temperature environment ( T = 20, 30, 60, 90, 120, and 160 C) were experimentally investigated in this paper. Select Baseline, Compressive Strength, Flexural Strength, Split Tensile Strength, Modulus of Determine mathematic problem I need help determining a mathematic problem. Deng, F. et al. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. Li et al.54 noted that the CS of SFRC increased with increasing amounts of C and silica fume, and decreased with increasing amounts of water and SP. PubMed A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. Res. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Google Scholar. To adjust the validation sets hyperparameters, random search and grid search algorithms were used. Sci. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. & Chen, X. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. All data generated or analyzed during this study are included in this published article. Date:11/1/2022, Publication:Structural Journal The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Figure No. Date:10/1/2020, There are no Education Publications on flexural strength and compressive strength, View all ACI Education Publications on flexural strength and compressive strength , View all free presentations on flexural strength and compressive strength , There are no Online Learning Courses on flexural strength and compressive strength, View all ACI Online Learning Courses on flexural strength and compressive strength , Question: The effect of surface texture and cleanness on concrete strength, Question: The effect of maximum size of aggregate on concrete strength. Compressive Strength The main measure of the structural quality of concrete is its compressive strength. Constr. Mater. Build. The authors declare no competing interests. Supersedes April 19, 2022. Flexural strength is however much more dependant on the type and shape of the aggregates used. Build. Ren, G., Wu, H., Fang, Q. Recently, ML algorithms have been widely used to predict the CS of concrete. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Cem. 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: Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. The SFRC mixes containing hooked ISF and their 28-day CS (tested by 150mm cubic samples) were collected from the literature11,13,21,22,23,24,25,26,27,28,29,30,31,32,33. Farmington Hills, MI Date:1/1/2023, Publication:Materials Journal (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. As there is a correlation between the compressive and flexural strength of concrete and a correlation between compressive strength and the modulus of elasticity of the concrete, there must also be a reasonably accurate correlation between flexural strength and elasticity. In the current study, the architecture used was made up of a one-dimensional convolutional layer, a one-dimensional maximum pooling layer, a one-dimensional average pooling layer, and a fully-connected layer. Build. Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. XGB makes GB more regular and controls overfitting by increasing the generalizability6. 2(2), 4964 (2018). 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. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. It is equal to or slightly larger than the failure stress in tension. The raw data is also available from the corresponding author on reasonable request. These equations are shown below. 12. Accordingly, several statistical parameters such as R2, MSE, mean absolute percentage error (MAPE), root mean squared error (RMSE), average bias error (MBE), t-statistic test (Tstat), and scatter index (SI) were used. A comparative investigation using machine learning methods for concrete compressive strength estimation. 4: Flexural Strength Test. Article 5(7), 113 (2021). & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. Commercial production of concrete with ordinary . Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International Question: How is the required strength selected, measured, and obtained? In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. From Table 2, it can be observed that the ratio of flexural to compressive strength for all OPS concrete containing different aggregate saturation is in the range of 12.7% to 16.9% which is. Correspondence to Therefore, based on expert opinion and primary sensitivity analysis, two features (length and tensile strength of ISF) were omitted and only nine features were left for training the models. Mater. 11, and the correlation between input parameters and the CS of SFRC shown in Figs. Further information on this is included in our Flexural Strength of Concrete post. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. ADS Flexural test evaluates the tensile strength of concrete indirectly. Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. In Artificial Intelligence and Statistics 192204. The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. Karahan et al.58 implemented ANN with the LevenbergMarquardt variant as the backpropagation learning algorithm and reported that ANN predicted the CS of SFRC accurately (R2=0.96). In the meantime, to ensure continued support, we are displaying the site without styles Al-Abdaly, N. M., Al-Taai, S. R., Imran, H. & Ibrahim, M. Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation. The forming embedding can obtain better flexural strength. Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. ADS In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). Today Proc. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Mater. 12, the W/C ratio is the parameter that intensively affects the predicted CS. 45(4), 609622 (2012). 147, 286295 (2017). Intersect. Area and Volume Calculator; Concrete Mixture Proportioner (iPhone) Concrete Mixture Proportioner (iPad) Evaporation Rate Calculator; Joint Noise Estimator; Maximum Joint Spacing Calculator Determine the available strength of the compression members shown. Therefore, based on MLR performance in the prediction CS of SFRC and consistency with previous studies (in using the MLR to predict the CS of NC, HPC, and SFRC), it was suggested that, due to the complexity of the correlation between the CS and concrete mix properties, linear models (such as MLR) could not explain the complicated relationship among independent variables. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. Dubai World Trade Center Complex Southern California Adv. Corrosion resistance of steel fibre reinforced concrete-A literature review. If a model's residualerror distribution is closer to the normal distribution, there is a greater likelihood of prediction mistakes occurring around the mean value6. In other words, in CS prediction of SFRC, all the mixes components must be presented (such as the developed ML algorithms in the current study).