Adv. : New insights from statistical analysis and machine learning methods. 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. 2018, 110 (2018). Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). 27, 102278 (2021). Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Heliyon 5(1), e01115 (2019). Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. Young, B. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). R2 is a metric that demonstrates how well a model predicts the value of a dependent variable and how well the model fits the data. The reviewed contents include compressive strength, elastic modulus . 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 . Materials 13(5), 1072 (2020). Flexural strength may range from 10% to 15% of the compressive strength depending on the concrete mix. 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 Materials 15(12), 4209 (2022). ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. Mater. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. 37(4), 33293346 (2021). The value for s then becomes: s = 0.09 (550) s = 49.5 psi Build. 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. Compressive strength, Flexural strength, Regression Equation I. Table 4 indicates the performance of ML models by various evaluation metrics. The flexural strengths of all the laminates tested are significantly higher than their tensile strengths, and are also higher than or similar to their compressive strengths. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. Ren, G., Wu, H., Fang, Q. 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 contrast, others reported that SVR showed weak performance in predicting the CS of concrete. 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. the input values are weighted and summed using Eq. Mater. (4). The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. 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. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. In addition, Fig. 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. . (b) Lay the specimen on its side as a beam with the faces of the units uppermost, and support the beam symmetrically on two straight steel bars placed so as to provide bearing under the centre of . Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete, $$R_{XY} = \frac{{COV_{XY} }}{{\sigma_{X} \sigma_{Y} }}$$, $$x_{norm} = \frac{{x - x_{\min } }}{{x_{\max } - x_{\min } }}$$, $$\hat{y} = \alpha_{0} + \alpha_{1} x_{1} + \alpha_{2} x_{2} + \cdots + \alpha_{n} x_{n}$$, \(y = \left\langle {\alpha ,x} \right\rangle + \beta\), $$net_{j} = \sum\limits_{i = 1}^{n} {w_{ij} } x_{i} + b$$, https://doi.org/10.1038/s41598-023-30606-y. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. For example compressive strength of M20concrete is 20MPa. 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. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Today Commun. Determine the available strength of the compression members shown. Based on this, CNN had the closest distribution to the normal distribution and produced the best results for predicting the CS of SFRC, followed by SVR and RF. The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. Date:2/1/2023, Publication:Special Publication
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The KNN method is a simple supervised ML technique that can be utilized in order to solve both classification and regression problems. Materials IM Index. This index can be used to estimate other rock strength parameters. 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). 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. Build. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . Eng. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. 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. 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. 2021, 117 (2021). PubMed As shown in Fig. 147, 286295 (2017). Build. 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. 49, 554563 (2013). Skaryski, & Suchorzewski, J. Eng. 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. Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. [1] Mater. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. Regarding Fig. 45(4), 609622 (2012). The rock strength determined by . Accordingly, 176 sets of data are collected from different journals and conference papers. Zhang, Y. 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. Adv. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. Sci. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. The primary rationale for using an SVR is that the problem may not be separable linearly. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. 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. Flexural strength is an indirect measure of the tensile strength of concrete. In other words, the predicted CS decreases as the W/C ratio increases. Deng, F. et al. 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. 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. Struct. An. Kabiru, O. 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. 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. This property of concrete is commonly considered in structural design. Importance of flexural strength of . The testing of flexural strength in concrete is generally undertaken using a third point flexural strength test on a beam of concrete. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. Constr. Compressive strength test was performed on cubic and cylindrical samples, having various sizes. Adv. 41(3), 246255 (2010). One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. The current 4th edition of TR 34 includes the same method of correlation as BS EN 1992. 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. MLR is the most straightforward supervised ML algorithm for solving regression problems. Mater. Effects of steel fiber content and type on static mechanical properties of UHPCC. Build. INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. Eng. 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Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). J. Zhejiang Univ. Article The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Struct. Parametric analysis between parameters and predicted CS in various algorithms. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Chou, J.-S. & Pham, A.-D. Moreover, GB is an AdaBoost development model, a meta-estimator that consists of many sequential decision trees that uses a step-by-step method to build an additive model6. 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. For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Today Proc. Schapire, R. E. Explaining adaboost. Polymers 14(15), 3065 (2022). 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 feature importance of the ML algorithms was compared in Fig. The brains functioning is utilized as a foundation for the development of ANN6. These equations are shown below. Mater. Constr. 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. Limit the search results from the specified source. 12. & Tran, V. Q. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Date:9/30/2022, Publication:Materials Journal