Marcos-Meson, V. et al. Comput. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. Compos. Build. 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. CAS Constr. Phone: 1.248.848.3800, Home > Topics in Concrete > topicdetail, View all Documents on flexural strength and compressive strength , Publication:Materials Journal A. Jamshidi Avanaki, M., Abedi, M., Hoseini, A. J. Zhejiang Univ. Case Stud. 1.2 The values in SI units are to be regarded as the standard. This property of concrete is commonly considered in structural design. Sci. Li, Y. et al. However, it is worth noting that their performance in predicting the CS of SFRC was superior to that of KNN and MLR. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. In Artificial Intelligence and Statistics 192204. Khademi et al.51 used MLR to predict the CS of NC and found that it cannot be considered an accurate model (with R2=0.518). A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. volume13, Articlenumber:3646 (2023) Mater. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. 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 sugar industry produces a huge quantity of sugar cane bagasse ash in India. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. J. Adhes. 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. As shown in Fig. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. Han, J., Zhao, M., Chen, J. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). All data generated or analyzed during this study are included in this published article. Sci. Characteristic compressive strength (MPa) Flexural Strength (MPa) 20: 3.13: 25: 3.50: 30: 38800 Country Club Dr. Constr. Midwest, Feedback via Email (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. 2021, 117 (2021). If a model's residualerror distribution is closer to the normal distribution, there is a greater likelihood of prediction mistakes occurring around the mean value6. Google Scholar. 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). In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. 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. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Article The brains functioning is utilized as a foundation for the development of ANN6. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. 2.9.1 Compressive strength of pervious concrete: Compressive strength of a concrete is a measure of its ability to resist static load, which tends to crush it. Mater. Caution should always be exercised when using general correlations such as these for design work. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. 163, 376389 (2018). The site owner may have set restrictions that prevent you from accessing the site. Golafshani, E. M., Behnood, A. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. 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. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. Accordingly, 176 sets of data are collected from different journals and conference papers. Khan, K. et al. [1] Adv. Experimental study on bond behavior in fiber-reinforced concrete with low content of recycled steel fiber. Shade denotes change from the previous issue. Constr. Limit the search results with the specified tags. Also, Fig. 301, 124081 (2021). Mater. 248, 118676 (2020). PubMed Central Depending on the test method used to determine the flex strength (center or third point loading) an ESTIMATE of f'c would be obtained by multiplying the flex by 4.5 to 6. Article 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. The primary sensitivity analysis is conducted to determine the most important features. Date:10/1/2022, Publication:Special Publication In recent years, CNN algorithm (Fig. Al-Abdaly, N. M., Al-Taai, S. R., Imran, H. & Ibrahim, M. Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation. Invalid Email Address Eng. & Chen, X. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. The authors declare no competing interests. Source: Beeby and Narayanan [4]. J. Comput. 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. 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. : Validation, WritingReview & Editing. Build. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. 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. Cem. Google Scholar. Artif. MATH 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. Eng. 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. ASTM C 293 or ASTM C 78 techniques are used to measure the Flexural strength. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. 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. Eng. Khan et al.55 also reported that RF (R2=0.96, RMSE=3.1) showed more acceptable outcomes than XGB and GB with, an R2 of 0.9 and 0.95 in the prediction CS of SFRC, respectively. Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. However, the understanding of ISFs influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. Mansour Ghalehnovi. Mater. In other words, in CS prediction of SFRC, all the mixes components must be presented (such as the developed ML algorithms in the current study). 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. 4: Flexural Strength Test. MathSciNet 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. Build. Normal distribution of errors (Actual CSPredicted CS) for different methods. This algorithm first calculates K neighbors euclidean distance. 12). Schapire, R. E. Explaining adaboost. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. ADS Mater. Ray ID: 7a2c96f4c9852428 INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. The testing of flexural strength in concrete is generally undertaken using a third point flexural strength test on a beam of concrete. Where an accurate elasticity value is required this should be determined from testing. 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. Mater. For the prediction of CS behavior of NC, Kabirvu et al.5 implemented SVR, and observed that SVR showed high accuracy (with R2=0.97). 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. Dubai World Trade Center Complex Struct. Res. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). 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 You are using a browser version with limited support for CSS. 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. Constr. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Constr. Supersedes April 19, 2022. 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. Article Build. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. 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. In contrast, the XGB and KNN had the most considerable fluctuation rate. 12. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). Build. Behbahani, H., Nematollahi, B. In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete . Constr. Ati, C. D. & Karahan, O. Huang, J., Liew, J. 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. According to Table 1, input parameters do not have a similar scale. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. 49, 20812089 (2022). Civ. As with any general correlations this should be used with caution. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. 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). ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. It is observed that in comparison models with R2, MSE, RMSE, and SI, CNN shows the best result in predicting the CS of SFRC, followed by SVR, and XGB. The rock strength determined by . Constr. Finally, the model is created by assigning the new data points to the category with the most neighbors. ; The values of concrete design compressive strength f cd are given as . 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. 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. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. \(R\) shows the direction and strength of a two-variable relationship. A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. J Civ Eng 5(2), 1623 (2015). 1 and 2. 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. Mech. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Chen, H., Yang, J. Flexural strength is measured by using concrete beams. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. What factors affect the concrete strength? In addition, CNN achieved about 28% lower residual error fluctuation than SVR. The loss surfaces of multilayer networks. 16, e01046 (2022). In fact, SVR tries to determine the best fit line. Mater. J. Convert. Parametric analysis between parameters and predicted CS in various algorithms. Adv. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. Chou, J.-S. & Pham, A.-D. 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. http://creativecommons.org/licenses/by/4.0/. Eng. Thank you for visiting nature.com. 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. CAS 175, 562569 (2018). The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Area and Volume Calculator; Concrete Mixture Proportioner (iPhone) Concrete Mixture Proportioner (iPad) Evaporation Rate Calculator; Joint Noise Estimator; Maximum Joint Spacing Calculator Kang, M.-C., Yoo, D.-Y. Polymers 14(15), 3065 (2022). 324, 126592 (2022). PubMed Figure No. 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! Flexural strength is an indirect measure of the tensile strength of concrete. 313, 125437 (2021). Mater. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. You've requested a page on a website (cloudflarepreview.com) that is on the Cloudflare network. Soft Comput. 3- or 7-day test results are used to monitor early strength gain, especially when high early-strength concrete is used. 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). 34(13), 14261441 (2020). 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). Build. Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Google Scholar. The predicted values were compared with the actual values to demonstrate the feasibility of ML algorithms (Fig. Transcribed Image Text: SITUATION A. However, it is depicted that the weak correlation between the amount of ISF in the SFRC mix and the predicted CS. Intersect. Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). the input values are weighted and summed using Eq. It's hard to think of a single factor that adds to the strength of concrete. As you can see the range is quite large and will not give a comfortable margin of certitude. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. 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%. 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. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. I Manag. Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. Provided by the Springer Nature SharedIt content-sharing initiative. The Offices 2 Building, One Central Google Scholar. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. 7). Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). Constr. Materials IM Index. On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. Feature importance of CS using various algorithms. Scientific Reports Compos. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters.