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. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. In many cases it is necessary to complete a compressive strength to flexural strength conversion. 12. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. It uses two general correlations commonly used to convert concrete compression and floral strength. 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 . 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. fck = Characteristic Concrete Compressive Strength (Cylinder). Phone: +971.4.516.3208 & 3209, ACI Resource Center 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. 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. 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. Compressive strength prediction of recycled concrete based on deep learning. Thank you for visiting nature.com. 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. Experimental study on bond behavior in fiber-reinforced concrete with low content of recycled steel fiber. In Artificial Intelligence and Statistics 192204. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. In contrast, the XGB and KNN had the most considerable fluctuation rate. For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). Eng. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). 2(2), 4964 (2018). Performance of implimented algorithms in predicting CS of steel fiber-reinforced sconcrete (SFRC). sqrt(fck) Where, fck is the characteristic compressive strength of concrete in MPa. 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. 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 As shown in Fig. 163, 376389 (2018). 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. Build. Where an accurate elasticity value is required this should be determined from testing. The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC) incorporating hooked ISF based on the data collected from the open literature. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. & Hawileh, R. A. 26(7), 16891697 (2013). Mech. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. 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. Mater. Get the most important science stories of the day, free in your inbox. Mater. Mater. Mater. Nowadays, For the production of prefabricated and in-situ concrete structures, SFRC is gaining acceptance such as (a) secondary reinforcement for temporary load scenarios, arresting shrinkage cracks, limiting micro-cracks occurring during transportation or installation of precast members (like tunnel lining segments), (b) partial substitution of the conventional reinforcement, i.e., hybrid reinforcement systems, and (c) total replacement of the typical reinforcement in compression-exposed elements, e.g., thin-shell structures, ground-supported slabs, foundations, and tunnel linings9. Mater. PubMed Figure No. volume13, Articlenumber:3646 (2023) Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. Sci. 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. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. 12, the W/C ratio is the parameter that intensively affects the predicted CS. . Constr. A. The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. Mater. Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. 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. Eng. Review of Materials used in Construction & Maintenance Projects. Artif. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. Struct. Constr. & Lan, X. : Investigation, Conceptualization, Methodology, Data Curation, Formal analysis, WritingOriginal Draft; N.R. Jamshidi Avanaki, M., Abedi, M., Hoseini, A. J. Enterp. 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. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. Hence, various types of fibers are added to increase the tensile load-bearing capability of concrete. These are taken from the work of Croney & Croney. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. Appl. The Offices 2 Building, One Central Phone: 1.248.848.3800 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. Flexural test evaluates the tensile strength of concrete indirectly. 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). It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. Supersedes April 19, 2022. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842. Also, the CS of SFRC was considered as the only output parameter. 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. Parametric analysis between parameters and predicted CS in various algorithms. 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. Explain mathematic . It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. Further information on this is included in our Flexural Strength of Concrete post. In addition, Fig. However, it is depicted that the weak correlation between the amount of ISF in the SFRC mix and the predicted CS. Date:3/3/2023, Publication:Materials Journal https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. Add to Cart. 308, 125021 (2021). The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Mater. Table 3 provides the detailed information on the tuned hyperparameters of each model. 161, 141155 (2018). Difference between flexural strength and compressive strength? Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International In the meantime, to ensure continued support, we are displaying the site without styles 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. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Question: How is the required strength selected, measured, and obtained? B Eng. 6(5), 1824 (2010). Google Scholar. & Aluko, O. In other words, the predicted CS decreases as the W/C ratio increases. To obtain Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). 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. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. PMLR (2015). 1.2 The values in SI units are to be regarded as the standard. 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). 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. Commercial production of concrete with ordinary . However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). Constr. Constr. Constr. J. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. 103, 120 (2018). 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. Eng. Abuodeh, O. R., Abdalla, J. This effect is relatively small (only. For materials that deform significantly but do not break, the load at yield, typically measured at 5% deformation/strain of the outer surface, is reported as the flexural strength or flexural yield strength. 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. This method has also been used in other research works like the one Khan et al.60 did. Therefore, these results may have deficiencies. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . Flexural strength calculator online - We'll provide some tips to help you select the best Flexural strength calculator online for your needs. Flexural strength may range from 10% to 15% of the compressive strength depending on the concrete mix. Build. 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. However, it is worth noting that their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. In LOOCV, the number of folds is equal the number of instances in the dataset (n=176). The flexural strength is stress at failure in bending. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. 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. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. . Article & Kim, H. Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. 12 illustrates the impact of SP on the predicted CS of SFRC. Mater. 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. The forming embedding can obtain better flexural 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. 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. Google Scholar. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. Date:11/1/2022, Publication:Structural Journal Civ. It uses two commonly used general correlations to convert concrete compressive and flexural strength. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. Scientific Reports Mater. Moreover, it is essential to mention that only 26% of the presented mixes contained fly-ash, and the results obtained were according to these mixes. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. 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. 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. Constr. where \(x_{i} ,w_{ij} ,net_{j} ,\) and \(b\) are the input values, the weight of each signal, the weighted sum of the \(j{\text{th}}\) neuron, and bias, respectively18. All data generated or analyzed during this study are included in this published article. Skaryski, & Suchorzewski, J. By submitting a comment you agree to abide by our Terms and Community Guidelines.
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