Google Scholar. Then, among K neighbors, each category's data points are counted. 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. 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. Eng. Heliyon 5(1), e01115 (2019). Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. This effect is relatively small (only. It is equal to or slightly larger than the failure stress in tension. The results of the experiment reveal that the EVA-modified mortar had a high rate of strength development early on, making the material advantageous for use in 3DAC. 115, 379388 (2019). Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. 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. 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. Intersect. Mater. Therefore, these results may have deficiencies. Consequently, it is frequently required to locate a local maximum near the global minimum59. 6) has been increasingly used to predict the CS of concrete34,46,47,48,49. 209, 577591 (2019). Google Scholar. Khan, K. et al. Infrastructure Research Institute | Infrastructure Research Institute According to Table 1, input parameters do not have a similar scale. 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. 1.2 The values in SI units are to be regarded as the standard. ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. Is there such an equation, and, if so, how can I get a copy? ANN can be used to model complicated patterns and predict problems. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. 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). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. & Hawileh, R. A. 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 simplest and most commonly applied method of quality control for concrete pavements is to test compressive strength and then use this as an indirect measure of the flexural strength. Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. 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. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. Importance of flexural strength of . In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. Shamsabadi, E. A. et al. ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . Eng. The value for s then becomes: s = 0.09 (550) s = 49.5 psi Adv. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. J. Enterp. 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. Constr. It uses two general correlations commonly used to convert concrete compression and floral strength. Article Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. Therefore, as can be perceived from Fig. 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. Constr. MLR is the most straightforward supervised ML algorithm for solving regression problems. 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. The raw data is also available from the corresponding author on reasonable request. 248, 118676 (2020). Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. As is reported by Kang et al.18, among implemented tree-based models, XGB performed superiorly in predicting the CS of SFRC. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Appl. Also, to prevent overfitting, the leave-one-out cross-validation method (LOOCV) is implemented, and 8 different metrics are used to assess the efficiency of developed models. Finally, the model is created by assigning the new data points to the category with the most neighbors. 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. Cem. Compressive strength result was inversely to crack resistance. J. 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. The rock strength determined by . 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. In SVR, \(\{ x_{i} ,y_{i} \} ,i = 1,2,,k\) is the training set, where \(x_{i}\) and \(y_{i}\) are the input and output values, respectively. 27, 102278 (2021). 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. 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 . This index can be used to estimate other rock strength parameters. 2, it is obvious that the CS increased with increasing the SP (R=0.792) followed by fly ash (R=0.688) and C (R=0.501). 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. & Lan, X. Erdal, H. I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Mater. 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. This can be due to the difference in the number of input parameters. For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). Constr. As shown in Fig. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. . 2(2), 4964 (2018). Thank you for visiting nature.com. PubMedGoogle Scholar. CAS 7). 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. Sci Rep 13, 3646 (2023). Soft Comput. Constr. Intersect. For example compressive strength of M20concrete is 20MPa. Modulus of rupture is the behaviour of a material under direct tension. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. Deng, F. et al. Technol. Cite this article. & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. Finally, it is observed that ANN performs weaker than SVR and XGB in terms of R2 in the validation set due to the non-convexity of the multilayer perceptron's loss surface. Sci. 175, 562569 (2018). Mater. 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 develop this composite, sugarcane bagasse ash (SA), glass . Article These are taken from the work of Croney & Croney. 147, 286295 (2017). Flexural test evaluates the tensile strength of concrete indirectly. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. Phys. 95, 106552 (2020). Materials 15(12), 4209 (2022). Invalid Email Address. 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. Polymers 14(15), 3065 (2022). Mater. As with any general correlations this should be used with caution. The current 4th edition of TR 34 includes the same method of correlation as BS EN 1992. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. : Validation, WritingReview & Editing. 12. PubMed Central & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. Google Scholar. Eng. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. Mater. & LeCun, Y. In the meantime, to ensure continued support, we are displaying the site without styles 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). However, there are certain commonalities: Types of cement that may be used Cement quantity, quality, and brand 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. 301, 124081 (2021). Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. 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. 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. Caution should always be exercised when using general correlations such as these for design work. 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. PubMed Mater. CNN model is a new architecture for DL which is comprised of several layers that process and transform an input to produce an output. 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. Flexural strength is commonly correlated to the compressive strength of a concrete mix, which allows field testing procedures to be consistent for all concrete applications on a project. 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. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in http://creativecommons.org/licenses/by/4.0/. Tree-based models performed worse than SVR in predicting the CS of SFRC. 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. Constr. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. Mater. An. Difference between flexural strength and compressive strength? Shade denotes change from the previous issue. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. Recently, ML algorithms have been widely used to predict the CS of concrete. Source: Beeby and Narayanan [4]. Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. Fax: 1.248.848.3701, ACI Middle East Regional Office This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. The Offices 2 Building, One Central Most common test on hardened concrete is compressive strength test' It is because the test is easy to perform. Int. 183, 283299 (2018). Mater. 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. Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014). Based upon the results in this study, tree-based models performed worse than SVR in predicting the CS of SFRC. Compos. How is the required strength selected, measured, and obtained? Al-Abdaly et al.50 also reported that RF (R2=0.88, RMSE=5.66, MAE=3.8) performed better than MLR (R2=0.64, RMSE=8.68, MAE=5.66) in predicting the CS of SFRC. 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. Also, Fig. Build. 36(1), 305311 (2007). Mater. Mater. Materials IM Index. S.S.P. Chen, H., Yang, J. Concr. Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. A more useful correlations equation for the compressive and flexural strength of concrete is shown below. Build. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. The sugar industry produces a huge quantity of sugar cane bagasse ash in India. Chou, J.-S. & Pham, A.-D. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. Figure No. 103, 120 (2018). 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). 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. Compos. Build. On the other hand, K-nearest neighbor (KNN) algorithm with R2=0.881, RMSE=6.477, and MAE=4.648 results in the weakest performance. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. 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). Scientific Reports (Sci Rep) Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. Further information on this is included in our Flexural Strength of Concrete post. Today Proc. Flexural strength = 0.7 x fck Where f ck is the compressive strength cylinder of concrete in MPa (N/mm 2 ). Plus 135(8), 682 (2020). 2 illustrates the correlation between input parameters and the CS of SFRC. Buy now for only 5. 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. Adv. fck = Characteristic Concrete Compressive Strength (Cylinder). Marcos-Meson, V. et al. Search results must be an exact match for the keywords. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Constr. The same results are also reported by Kang et al.18. 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. ; The values of concrete design compressive strength f cd are given as . It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. The primary sensitivity analysis is conducted to determine the most important features. 12, the SP has a medium impact on the predicted CS of SFRC. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Build. It uses two commonly used general correlations to convert concrete compressive and flexural strength. Mater. 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. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. 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%. Build. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. Struct. Explain mathematic . Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. Article Build. Knag et al.18 reported that silica fume, W/C ratio, and DMAX are the most influential parameters that predict the CS of SFRC. A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. October 18, 2022. Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) Answer (1 of 5): For design of the beams we need flexuralstrength which is obtained from the characteristic strength by the formula Fcr=0.7FckFcr=0.7Fck Fck - is the characteristic strength Characteristic strength is found by applying compressive stress on concrete cubes after 28 days of cur. Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. Eng. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. J. Comput. 94, 290298 (2015). Kang, M.-C., Yoo, D.-Y. : New insights from statistical analysis and machine learning methods. 33(3), 04019018 (2019). 308, 125021 (2021). Experimental study on bond behavior in fiber-reinforced concrete with low content of recycled steel fiber. The brains functioning is utilized as a foundation for the development of ANN6. The formula to calculate compressive strength is F = P/A, where: F=The compressive strength (MPa) P=Maximum load (or load until failure) to the material (N) A=A cross-section of the area of the material resisting the load (mm2) Introduction Of Compressive Strength Eng. Constr. 4) has also been used to predict the CS of concrete41,42. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). 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. A. In other words, the predicted CS decreases as the W/C ratio increases. Table 3 displays the modified hyperparameters of each convolutional, flatten, hidden, and pooling layer, including kernel and filter size and learning rate. You do not have access to www.concreteconstruction.net. Question: How is the required strength selected, measured, and obtained? Article Cloudflare is currently unable to resolve your requested domain. 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. Setti, F., Ezziane, K. & Setti, B. Eng. 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 . 45(4), 609622 (2012). Mater. 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. Download Solution PDF Share on Whatsapp Latest MP Vyapam Sub Engineer Updates Last updated on Feb 21, 2023 MP Vyapam Sub Engineer (Civil) Revised Result Out on 21st Feb 2023! Eng. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Compressive strength, Flexural strength, Regression Equation I. Constr. Mater. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. 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. Deng et al.47 also observed that CNN was better at predicting the CS of recycled concrete (average relative error=3.65) than other methods. What factors affect the concrete strength? The loss surfaces of multilayer networks. 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 sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. However, the understanding of ISF's influence on the compressive strength (CS) behavior of . Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Various orders of marked and unmarked errors in predictions are demonstrated by MSE, RMSE, MAE, and MBE6. Constr. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Farmington Hills, MI The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). 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. You've requested a page on a website (cloudflarepreview.com) that is on the Cloudflare network. Ati, C. D. & Karahan, O. Civ. 16, e01046 (2022). Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. 12 illustrates the impact of SP on the predicted CS of SFRC. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. 11(4), 1687814019842423 (2019). From the open literature, a dataset was collected that included 176 different concrete compressive test sets. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Sci. Flexural strength is about 10 to 15 percent of compressive strength depending on the mixture proportions and type, size and volume of coarse aggregate used. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . As can be seen in Fig. 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. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. CAS The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Second Floor, Office #207 Date:9/30/2022, Publication:Materials Journal It is a measure of the maximum stress on the tension face of an unreinforced concrete beam or slab at the point of. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Parametric analysis between parameters and predicted CS in various algorithms. 1.1 This test method provides guidelines for testing the flexural strength of cured geosynthetic cementitious composite mat (GCCM) products in a three (3)-point bend apparatus. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Adv. The flexural strength is the higher of: f ctm,fl = (1.6 - h/1000)f ctm (6) or, f ctm,fl = f ctm where; h is the total member depth in mm Strength development of tensile strength Res. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. 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. The site owner may have set restrictions that prevent you from accessing the site. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. Google Scholar. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. A comparative investigation using machine learning methods for concrete compressive strength estimation. & Aluko, O. Based on the developed models to predict the CS of SFRC (Fig. The flexural strength of a material is defined as its ability to resist deformation under load. The best-fitting line in SVR is a hyperplane with the greatest number of points. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). Mater. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. Build. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. Properties of steel fiber reinforced fly ash concrete. J. Young, B. Today Proc. Huang, J., Liew, J. Flexural strength is measured by using concrete beams. A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units.