flexural strength to compressive strength converter

However, regarding the Tstat, the outcomes show that CNN performance was approximately 58% lower than XGB. Performance of implimented algorithms in predicting CS of steel fiber-reinforced sconcrete (SFRC). These cross-sectional forms included V-stiffeners in the web compression zone at 1/3 height near the compressed flange and no V-stiffeners on the flange . CAS 230, 117021 (2020). Google Scholar. In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . Mater. 12. & Liu, J. Article Article Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Flexural strength, also known as modulus of rupture, or bend strength, or transverse rupture strengthis a material property, defined as the stressin a material just before it yieldsin a flexure test. Constr. Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. Build. These equations are shown below. Moreover, according to the results reported by Kang et al.18, it was shown that using MLR led to a significant difference between actual and predicted values for prediction of SFRCs CS (RMSE=12.4273, MAE=11.3765). Moreover, the regression function is \(y = \left\langle {\alpha ,x} \right\rangle + \beta\) and the aim of SVR is to flat the function as more as possible18. Constr. Email Address is required Technol. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. The ideal ratio of 20% HS, 2% steel . Gupta, S. Support vector machines based modelling of concrete strength. 1.2 The values in SI units are to be regarded as the standard. In the meantime, to ensure continued support, we are displaying the site without styles East. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). Based on the developed models to predict the CS of SFRC (Fig. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. J Civ Eng 5(2), 1623 (2015). Mater. The current 4th edition of TR 34 includes the same method of correlation as BS EN 1992. Mater. Therefore, according to the KNN results in predicting the CS of SFRC and compatibility with previous studies (in using the KNN in predicting the CS of various concrete types), it was observed that like MLR, KNN technique could not perform promisingly in predicting the CS of SFRC. The use of an ANN algorithm (Fig. Also, Fig. 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. This method converts the compressive strength to the Mean Axial Tensile Strength, then converts this to flexural strength and includes an adjustment for the depth of the slab. To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. ANN can be used to model complicated patterns and predict problems. CAS Explain mathematic . Several statistical parameters are also used as metrics to evaluate the performance of implemented models, such as coefficient of determination (R2), mean absolute error (MAE), and mean of squared error (MSE). Adv. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. In contrast, KNN shows the worst performance among developed ML models in predicting the CS of SFRC. Constr. 12, the SP has a medium impact on the predicted CS of SFRC. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. Feature importance of CS using various algorithms. Meanwhile, AdaBoost predicted the CS of SFRC with a broader range of errors. 11(4), 1687814019842423 (2019). American Concrete Pavement Association, its Officers, Board of Directors and Staff are absolved of any responsibility for any decisions made as a result of your use. Scientific Reports Recently, ML algorithms have been widely used to predict the CS of concrete. Khan, K. et al. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. 1. Concr. MATH Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. Behbahani, H., Nematollahi, B. & Nitesh, K. S. Study on the effect of steel and glass fibers on fresh and hardened properties of vibrated concrete and self-compacting concrete. Limit the search results from the specified source. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. 37(4), 33293346 (2021). Search results must be an exact match for the keywords. The brains functioning is utilized as a foundation for the development of ANN6. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses Build. A more useful correlations equation for the compressive and flexural strength of concrete is shown below. Compressive strengthis defined as resistance of material under compression prior to failure or fissure, it can be expressed in terms of load per unit area and measured in MPa. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. 16, e01046 (2022). The KNN method is a simple supervised ML technique that can be utilized in order to solve both classification and regression problems. PubMed Central Mater. Li, Y. et al. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Date:1/1/2023, Publication:Materials Journal \(R\) shows the direction and strength of a two-variable relationship. Appl. Date:7/1/2022, Publication:Special Publication 1 and 2. . 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. Struct. 267, 113917 (2021). The capabilities of ML algorithms were demonstrated through a sensitivity analysis and parametric analysis. Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. Compressive strength test was performed on cubic and cylindrical samples, having various sizes. Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). Accordingly, 176 sets of data are collected from different journals and conference papers. Compressive strength prediction of recycled concrete based on deep learning. Karahan, O., Tanyildizi, H. & Atis, C. D. An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash. The maximum value of 25.50N/mm2 for the 5% replacement level is found suitable and recommended having attained a 28- day compressive strength of more than 25.0N/mm2. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. 94, 290298 (2015). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Mater. D7 FLEXURAL STRENGTH BY BEAM TEST D7.1 Test procedure The procedure for testing each specimen using the beam test method shall be as follows: (a) Determine the mass of the specimen to within 1 kg. Therefore, based on tree-based technique outcomes in predicting the CS of SFRC and compatibility with previous studies in using tree-based models for predicting the CS of various concrete types (SFRC and NC), it was concluded that tree-based models (especially XGB) showed good performance. & Hawileh, R. A. Table 3 provides the detailed information on the tuned hyperparameters of each model. 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. Phone: 1.248.848.3800 This is a result of the use of the linear relationship in equation 3.1 of BS EN 1996-1-1 and was taken into account in the UK calibration. Among these techniques, AdaBoost is the most straightforward boosting algorithm that is based on the idea that a very accurate prediction rule can be made by combining a lot of less accurate regulations43. Civ. One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. As with any general correlations this should be used with caution. In recent years, CNN algorithm (Fig. Normalised and characteristic compressive strengths in The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. Build. 183, 283299 (2018). ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. 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. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Source: Beeby and Narayanan [4]. PubMed Intersect. 324, 126592 (2022). Correspondence to 101. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Martinelli, E., Caggiano, A. Dubai, UAE 28(9), 04016068 (2016). 6(5), 1824 (2010). The minimum performance requirements of each GCCM Classification Type have been defined within ASTM D8364, defining the appropriate GCCM specific test standards to use, such as: ASTM D8329 for compressive strength and ASTM D8058 for flexural strength. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Limit the search results modified within the specified time. Constr. Question: Are there data relating w/cm to flexural strength that are as reliable as those for compressive View all Frequently Asked Questions on flexural strength and compressive strength», View all flexural strength and compressive strength Events , The Concrete Industry in the Era of Artificial Intelligence, There are no Committees on flexural strength and compressive strength, Concrete Laboratory Testing Technician - Level 1. STANDARDS, PRACTICES and MANUALS ON FLEXURAL STRENGTH AND COMPRESSIVE STRENGTH ACI CODE-350-20: Code Requirements for Environmental Engineering Concrete Structures (ACI 350-20) and Commentary (ACI 350R-20) ACI PRC-441.1-18: Report on Equivalent Rectangular Concrete Stress Block and Transverse Reinforcement for High-Strength Concrete Columns 313, 125437 (2021). As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. & LeCun, Y. Adv. Table 3 shows the results of using a grid and a random search to tune the other hyperparameters. Mech. Compressive Strength The main measure of the structural quality of concrete is its compressive strength. 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. The experimental results show that in the case of [0/90/0] 2 ply, the bending strength of the structure increases by 2.79% in the forming embedding mode, while it decreases by 9.81% in the cutting embedding mode. 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. Mater. Get the most important science stories of the day, free in your inbox. It is also observed that a lower flexural strength will be measured with larger beam specimens. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . Internet Explorer). Chou, J.-S. & Pham, A.-D. Moreover, among the three proposed ML models here, SVR demonstrates superior performance in estimating the influence of the W/C ratio on the predicted CS of SFRC with a correlation of R=0.999, followed by CNN with a correlation of R=0.96. Jang, Y., Ahn, Y. 11, and the correlation between input parameters and the CS of SFRC shown in Figs. Buy now for only 5. However, the addition of ISF into the concrete and producing the SFRC may also provide additional strength capacity or act as the primary reinforcement in structural elements. The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. Build. However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. Please enter this 5 digit unlock code on the web page. 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. Eng. Caggiano, A., Folino, P., Lima, C., Martinelli, E. & Pepe, M. On the mechanical response of hybrid fiber reinforced concrete with recycled and industrial steel fibers. 12). Supersedes April 19, 2022. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. 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). MathSciNet The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Table 4 indicates the performance of ML models by various evaluation metrics. Deng, F. et al. Mater. Res. 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. Materials 15(12), 4209 (2022). Commercial production of concrete with ordinary . Mater. Khan, M. A. et al. Infrastructure Research Institute | Infrastructure Research Institute fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab According to EN1992-1-1 3.1.3(2) the following modifications are applicable for the value of the concrete modulus of elasticity E cm: a) for limestone aggregates the value should be reduced by 10%, b) for sandstone aggregates the value should be reduced by 30%, c) for basalt aggregates the value should be increased by 20%. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). Difference between flexural strength and compressive strength? 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. Mahesh et al.19 used ML algorithms on a 140-raw dataset considering 8 different features (LISF, VISF, and L/DISF as the fiber properties) and concluded that the artificial neural network (ANN) had the best performance in predicting the CS of SFRC with a regression coefficient of 0.97. 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. 163, 826839 (2018). 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). Abuodeh, O. R., Abdalla, J. However, ANN performed accurately in predicting the CS of NC incorporating waste marble powder (R2=0.97) in the test set. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. This can be due to the difference in the number of input parameters. Concr. Mater. 27, 102278 (2021). Therefore, as can be perceived from Fig. Case Stud. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International fck = Characteristic Concrete Compressive Strength (Cylinder). Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Therefore, these results may have deficiencies. Design of SFRC structural elements: post-cracking tensile strength measurement. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. Overall, it is possible to conclude that CNN produces more accurate predictions of the CS of SFRC with less uncertainty, followed by SVR and XGB. Hence, the presented study aims to compare various ML algorithms for CS prediction of SFRC based on all the influential parameters. Mater. Ray ID: 7a2c96f4c9852428 These equations are shown below. Further information on this is included in our Flexural Strength of Concrete post. The rock strength determined by . Among these parameters, W/C ratio was commonly found to be the most significant parameter impacting the CS of SFRC (as the W/C ratio increases, the CS of SFRC will be increased). The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Seyed Soroush Pakzad,Naeim Roshan&Mansour Ghalehnovi, You can also search for this author in Fluctuations of errors (Actual CSpredicted CS) for different algorithms. MLR is the most straightforward supervised ML algorithm for solving regression problems. 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. 49, 554563 (2013). 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. 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). Constr. 3-point bending strength test for fine ceramics that partially complies with JIS R1601 (2008) [Testing method for flexural strength of fine ceramics at room temperature] (corresponding part only). All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. 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. Scientific Reports (Sci Rep) Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete. Google Scholar. 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. [1] Build. 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. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. It was observed that ANN (with R2=0.896, RMSE=6.056, MAE=4.383) performed better than MLR, KNN, and tree-based models (except XGB) in predicting the CS of SFRC, but its accuracy was lower than the SVR and XGB (in both validation and test sets) techniques. In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. I Manag. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). Eur. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. Mater. & Arashpour, M. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Date:10/1/2022, Publication:Special Publication Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Skaryski, & Suchorzewski, J. Review of Materials used in Construction & Maintenance Projects. The reviewed contents include compressive strength, elastic modulus . 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. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. Constr. sqrt(fck) Where, fck is the characteristic compressive strength of concrete in MPa. It uses two general correlations commonly used to convert concrete compression and floral strength. Eng. & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Mater. This useful spreadsheet can be used to convert concrete cube test results from compressive strength to flexural strength to check whether the concrete used satisfies the specification. 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. ACI World Headquarters Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Marcos-Meson, V. et al. 2018, 110 (2018). Compressive strength, Flexural strength, Regression Equation I. 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. The primary sensitivity analysis is conducted to determine the most important features. 73, 771780 (2014). Determine the available strength of the compression members shown. According to Table 1, input parameters do not have a similar scale. Adam was selected as the optimizer function with a learning rate of 0.01. The best-fitting line in SVR is a hyperplane with the greatest number of points. The Offices 2 Building, One Central Constr. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. The value of flexural strength is given by . While this relationship will vary from mix to mix, there have been a number of attempts to derive a flexural strength to compressive strength converter equation. 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. Sci Rep 13, 3646 (2023). Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. 260, 119757 (2020). 36(1), 305311 (2007). Azimi-Pour, M., Eskandari-Naddaf, H. & Pakzad, A. Constr. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. Various orders of marked and unmarked errors in predictions are demonstrated by MSE, RMSE, MAE, and MBE6. 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. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). This effect is relatively small (only. & Chen, X. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. 147, 286295 (2017). 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. Cem. Provided by the Springer Nature SharedIt content-sharing initiative. Eng. Kabiru, O. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. 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. 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. However, it is suggested that ANN can be utilized to predict the CS of SFRC. Flexural strength is measured by using concrete beams. Iex 2010 20 ft 21121 12 ft 8 ft fim S 12 x 35 A36 A=10.2 in, rx=4.72 in, ry=0.98 in b. Iex 34 ft 777777 nutt 2010 12 ft 12 ft W 10 ft 4000 fim MC 8 . Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). As shown in Fig. This is much more difficult and less accurate than the equivalent concrete cube test, which is why it is common to test the compressive strength and then convert to flexural strength when checking the concrete's compliance with the specification. Midwest, Feedback via Email 23(1), 392399 (2009). Materials 8(4), 14421458 (2015). Build. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Bending occurs due to development of tensile force on tension side of the structure. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. & Kim, H. Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. (4). It was observed that among the concrete mixture properties, W/C ratio, fly-ash, and SP had the most significant effect on the CS of SFRC (W/C ratio was the most effective parameter). Adv. PubMed However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). 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. (3): where \(\hat{y}\), \(x_{n}\), and \(\alpha\) are the dependent parameter, independent parameter, and bias, respectively18. 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. As the simplest ML technique, MLR was implemented to predict the CS of SFRC and showed R2 of 0.888, RMSE of 6.301, and MAE of 5.317. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). Today Proc. Based upon the results in this study, tree-based models performed worse than SVR in predicting the CS of SFRC. The forming embedding can obtain better flexural strength. In many cases it is necessary to complete a compressive strength to flexural strength conversion. Use of this design tool implies acceptance of the terms of use. 38800 Country Club Dr. J. Comput. Buildings 11(4), 158 (2021). & Lan, X. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters.

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