flexural strength to compressive strength converter

As can be seen in Table 3, nine different algorithms were implemented in this research, including MLR, KNN, SVR, RF, GB, XGB, AdaBoost, ANN, and CNN. 16, e01046 (2022). Sci. The sugar industry produces a huge quantity of sugar cane bagasse ash in India. The rock strength determined by . Explain mathematic . In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. 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. 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. Khan, M. A. et al. 1.2 The values in SI units are to be regarded as the standard. Build. 3- or 7-day test results are used to monitor early strength gain, especially when high early-strength concrete is used. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. 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. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). For design of building members an estimate of the MR is obtained by: , where Midwest, Feedback via Email \(R\) shows the direction and strength of a two-variable relationship. Mater. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. 118 (2021). This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. Ray ID: 7a2c96f4c9852428 Adam was selected as the optimizer function with a learning rate of 0.01. & Xargay, H. An experimental study on the post-cracking behaviour of Hybrid Industrial/Recycled Steel Fibre-Reinforced Concrete. A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. 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. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. The capabilities of ML algorithms were demonstrated through a sensitivity analysis and parametric analysis. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. Select Baseline, Compressive Strength, Flexural Strength, Split Tensile Strength, Modulus of Determine mathematic problem I need help determining a mathematic problem. 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. 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. Eur. It was observed that overall, the ANN model outperformed the genetic algorithm in predicting the CS of SFRC. Recommended empirical relationships between flexural strength and compressive strength of plain concrete. Jang, Y., Ahn, Y. It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. Abuodeh, O. R., Abdalla, J. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. http://creativecommons.org/licenses/by/4.0/. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). 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. Intersect. Ren, G., Wu, H., Fang, Q. Build. The flexural strength is stress at failure in bending. Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. This method has also been used in other research works like the one Khan et al.60 did. 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. 4: Flexural Strength Test. 175, 562569 (2018). The authors declare no competing interests. & Liu, J. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. 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. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Invalid Email Address. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. : New insights from statistical analysis and machine learning methods. 49, 554563 (2013). For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. The best-fitting line in SVR is a hyperplane with the greatest number of points. 1 and 2. Scientific Reports (Sci Rep) Mech. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. Add to Cart. 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. Mater. Build. Khan, K. et al. Heliyon 5(1), e01115 (2019). Sci. 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. 2 illustrates the correlation between input parameters and the CS of SFRC. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. The use of an ANN algorithm (Fig. Int. Eng. & Hawileh, R. A. The result of this analysis can be seen in Fig. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). 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 . SI is a standard error measurement, whose smaller values indicate superior model performance. As shown in Fig. 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. Invalid Email Address Tree-based models performed worse than SVR in predicting the CS of SFRC. Cloudflare is currently unable to resolve your requested domain. Table 3 displays the modified hyperparameters of each convolutional, flatten, hidden, and pooling layer, including kernel and filter size and learning rate. J. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. Date:1/1/2023, Publication:Materials Journal Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Six groups of austenitic 022Cr19Ni10 stainless steel bending specimens with three types of cross-sectional forms were used to study the impact of V-stiffeners on the failure mode and flexural behavior of stainless steel lipped channel beams. Constr. Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. Is there such an equation, and, if so, how can I get a copy? Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. Mater. Technol. 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. Soft Comput. 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. 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. J. Devries. Flexural strength is measured by using concrete beams. Azimi-Pour, M., Eskandari-Naddaf, H. & Pakzad, A. This index can be used to estimate other rock strength parameters. Article Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. Today Commun. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. The proposed regression equations exhibit small errors when compared to the experimental results, which allow for efficient and accurate predictions of the flexural strength. 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 However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). J. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. Comput. October 18, 2022. Ati, C. D. & Karahan, O. Build. 73, 771780 (2014). Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Chen, H., Yang, J. 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. It uses two commonly used general correlations to convert concrete compressive and flexural strength. S.S.P. 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. Mater. An. Supersedes April 19, 2022. Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International 26(7), 16891697 (2013). PubMed Central To develop this composite, sugarcane bagasse ash (SA), glass . Phone: +971.4.516.3208 & 3209, ACI Resource Center 147, 286295 (2017). Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. East. Flexural strength is an indirect measure of the tensile strength of concrete. The CivilWeb Compressive Strength to Flexural Conversion worksheet is included in the CivilWeb Flexural Strength spreadsheet suite. Mater. Adv. Depending on the mix (especially the water-cement ratio) and time and quality of the curing, compressive strength of concrete can be obtained up to 14,000 psi or more. You do not have access to www.concreteconstruction.net. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Moreover, Nguyen-Sy et al.56 and Rathakrishnan et al.57, after implementing the XGB, noted that the XGB was the best model for predicting the CS of NC. 12. A good rule-of-thumb (as used in the ACI Code) is: As shown in Fig. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . However, it is suggested that ANN can be utilized to predict the CS of SFRC. 163, 826839 (2018). 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 performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. 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. J. Adhes. 12, the W/C ratio is the parameter that intensively affects the predicted CS. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. 6(4) (2009). 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. Where flexural strength is critical to the design a correlation specific to the concrete mix should be developed from testing and this relationship used for the specification and quality control. (4). Constr. Constr. 266, 121117 (2021). Article Cite this article. Constr. 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 ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. Eng. 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. Gupta, S. Support vector machines based modelling of concrete strength. Young, B. Mater. PubMed 3) was used to validate the data and adjust the hyperparameters. In other words, the predicted CS decreases as the W/C ratio increases. This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. Mater. ACI World Headquarters ADS The predicted values were compared with the actual values to demonstrate the feasibility of ML algorithms (Fig. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. 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. Dubai World Trade Center Complex 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. 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. 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. 161, 141155 (2018). Build. 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. 232, 117266 (2020). Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. 230, 117021 (2020). Mater. 115, 379388 (2019). Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Materials IM Index. Accordingly, 176 sets of data are collected from different journals and conference papers. & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Kandiri, A., Golafshani, E. M. & Behnood, A. Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Date:11/1/2022, Publication:IJCSM 45(4), 609622 (2012). Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses Erdal, H. I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Plus 135(8), 682 (2020). Fluctuations of errors (Actual CSpredicted CS) for different algorithms. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. Sci. Consequently, it is frequently required to locate a local maximum near the global minimum59. In addition, Fig. In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. In addition, the studies based on ML techniques that have been done to predict the CS of SFRC are limited since it is difficult to collect inclusive experimental data to develop models regarding all contributing features (such as the properties of fibers, aggregates, and admixtures). The reviewed contents include compressive strength, elastic modulus . The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). 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. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. In contrast, the XGB and KNN had the most considerable fluctuation rate. All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. 11. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. 103, 120 (2018). A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Mater. Date:3/3/2023, Publication:Materials Journal Mater. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. The compressive strength of the ordinary Portland cement / Pulverized Bentonitic Clay (PBC) generally decreases as the percentage of Pulverized Bentonitic Clay (PBC) content increases. Mater. Get the most important science stories of the day, free in your inbox. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. 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. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. 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. Your IP: 103.74.122.237, Requested URL: www.concreteconstruction.net/how-to/correlating-compressive-and-flexural-strength_o, User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36. Question: How is the required strength selected, measured, and obtained? In LOOCV, the number of folds is equal the number of instances in the dataset (n=176). Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. Build. This property of concrete is commonly considered in structural design. Infrastructure Research Institute | Infrastructure Research Institute 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. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Table 3 provides the detailed information on the tuned hyperparameters of each model. & Arashpour, M. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. PubMedGoogle Scholar. You are using a browser version with limited support for CSS. However, it is worth noting that their performance in predicting the CS of SFRC was superior to that of KNN and MLR. 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. Build. All these mixes had some features such as DMAX, the amount of ISF (ISF), L/DISF, C, W/C ratio, coarse aggregate (CA), FA, SP, and fly ash as input parameters (9 features). 12, the SP has a medium impact on the predicted CS of SFRC. Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength. 209, 577591 (2019). Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. Artif. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. 49, 20812089 (2022). Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Eng. Limit the search results from the specified source. Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. Limit the search results with the specified tags. Build. The ideal ratio of 20% HS, 2% steel . The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Struct. Compressive strength prediction of recycled concrete based on deep learning. The same results are also reported by Kang et al.18. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Table 4 indicates the performance of ML models by various evaluation metrics. ANN can be used to model complicated patterns and predict problems. Cem. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Mater. Adv. SVR is considered as a supervised ML technique that predicts discrete values. The sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. PubMed 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). The primary rationale for using an SVR is that the problem may not be separable linearly. [1] Flexural strength may range from 10% to 15% of the compressive strength depending on the concrete mix. Performance of implimented algorithms in predicting CS of steel fiber-reinforced sconcrete (SFRC). Appl. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. 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. Mech. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. 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. 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. 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. Materials 15(12), 4209 (2022). Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. 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 fck = Characteristic Concrete Compressive Strength (Cylinder). 5(7), 113 (2021). Compressive strength result was inversely to crack resistance. Article To adjust the validation sets hyperparameters, random search and grid search algorithms were used. 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. Mater. Constr. Corrosion resistance of steel fibre reinforced concrete-A literature review. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). Lee, S.-C., Oh, J.-H. & Cho, J.-Y. : Validation, WritingReview & Editing. Area and Volume Calculator; Concrete Mixture Proportioner (iPhone) Concrete Mixture Proportioner (iPad) Evaporation Rate Calculator; Joint Noise Estimator; Maximum Joint Spacing Calculator 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. Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. 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. Search results must be an exact match for the keywords. Date:10/1/2022, Publication:Special Publication The flexural strength of a material is defined as its ability to resist deformation under load. Build. Constr. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C).

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