flexural strength to compressive strength converter

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. 94, 290298 (2015). Question: How is the required strength selected, measured, and obtained? Concr. Based on the results obtained from the implementation of SVR in predicting the CS of SFRC and outcomes from previous studies in using the SVR to predict the CS of NC and SFRC, it was concluded that in some research, SVR demonstrated acceptable performance. 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). The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. 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Date:11/1/2022, Publication:Structural Journal All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. ISSN 2045-2322 (online). Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. The result of this analysis can be seen in Fig. 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 . One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. To adjust the validation sets hyperparameters, random search and grid search algorithms were used. 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. Mech. Constr. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Setti, F., Ezziane, K. & Setti, B. & Lan, X. 260, 119757 (2020). As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Li, Y. et al. 3- or 7-day test results are used to monitor early strength gain, especially when high early-strength concrete is used. CAS Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. 183, 283299 (2018). A more useful correlations equation for the compressive and flexural strength of concrete is shown below. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Date:11/1/2022, Publication:IJCSM Flexural strength of concrete = 0.7 . 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. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. Table 3 provides the detailed information on the tuned hyperparameters of each model. Google Scholar. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Azimi-Pour, M., Eskandari-Naddaf, H. & Pakzad, A. In LOOCV, the number of folds is equal the number of instances in the dataset (n=176). Artif. 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. 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. By submitting a comment you agree to abide by our Terms and Community Guidelines. 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. 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. 2020, 17 (2020). 232, 117266 (2020). 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. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. Corrosion resistance of steel fibre reinforced concrete-A literature review. 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. Also, Fig. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. 12, the SP has a medium impact on the predicted CS of SFRC. From the open literature, a dataset was collected that included 176 different concrete compressive test sets. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014). The feature importance of the ML algorithms was compared in Fig. Zhang, Y. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Appl. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. J Civ Eng 5(2), 1623 (2015). 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. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . 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. 6(5), 1824 (2010). 266, 121117 (2021). It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete . Nguyen-Sy, T. et al. 73, 771780 (2014). Meanwhile, AdaBoost predicted the CS of SFRC with a broader range of errors. According to the results obtained from parametric analysis, among the developed models, SVR can accurately predict the impact of W/C ratio, SP, and fly-ash on the CS of SFRC, followed by CNN. Dao, D. V., Ly, H.-B., Vu, H.-L.T., Le, T.-T. & Pham, B. T. Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete. 313, 125437 (2021). East. Modulus of rupture is the behaviour of a material under direct tension. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab : New insights from statistical analysis and machine learning methods. Experimental study on bond behavior in fiber-reinforced concrete with low content of recycled steel fiber. & Chen, X. 7). http://creativecommons.org/licenses/by/4.0/. The sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. PubMed Central 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. Cloudflare is currently unable to resolve your requested domain. 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. 12). The predicted values were compared with the actual values to demonstrate the feasibility of ML algorithms (Fig. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Southern California and JavaScript. 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. Cem. Google Scholar. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. Behbahani, H., Nematollahi, B. It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. The stress block parameter 1 proposed by Mertol et al. J. Devries. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. Comparing implemented ML algorithms in terms of Tstat, it is observed that XGB shows the best performance, followed by ANN and SVR in predicting the CS of SFRC. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Limit the search results with the specified tags. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. Properties of steel fiber reinforced fly ash concrete. The compressive strength of the ordinary Portland cement / Pulverized Bentonitic Clay (PBC) generally decreases as the percentage of Pulverized Bentonitic Clay (PBC) content increases. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). In the meantime, to ensure continued support, we are displaying the site without styles 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. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. 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. 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. 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. However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. 48331-3439 USA Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. Al-Abdaly et al.50 reported that MLR algorithm (with R2=0.64, RMSE=8.68, MAE=5.66) performed poorly in predicting the CS behavior of SFRC. Normalised and characteristic compressive strengths in Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). 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). The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. The proposed regression equations exhibit small errors when compared to the experimental results, which allow for efficient and accurate predictions of the flexural strength. Mater. 161, 141155 (2018). The dimension of stress is the same as that of pressure, and therefore the SI unit for stress is the pascal (Pa), which is equivalent to one newton per square meter (N/m). ANN model consists of neurons, weights, and activation functions18. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). A good rule-of-thumb (as used in the ACI Code) is: Mater. The reviewed contents include compressive strength, elastic modulus . Constr. 163, 826839 (2018). Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete.