The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". Golafshani, E. M., Behnood, A. Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). ASTM C 293 or ASTM C 78 techniques are used to measure the Flexural strength. Mater. 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. Mater. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. 27, 102278 (2021). However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). Internet Explorer). Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. Article 163, 826839 (2018). 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. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Constr. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. 267, 113917 (2021). It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. Han, J., Zhao, M., Chen, J. 49, 20812089 (2022). Article Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). 6(5), 1824 (2010). Email Address is required 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. Mater. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. The raw data is also available from the corresponding author on reasonable request. 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. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. 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. Constr. As the simplest ML technique, MLR was implemented to predict the CS of SFRC and showed R2 of 0.888, RMSE of 6.301, and MAE of 5.317. 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. Flexural strength, also known as modulus of rupture, or bend strength, or transverse rupture strengthis a material property, defined as the stressin a material just before it yieldsin a flexure test. 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 2021, 117 (2021). 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. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. Properties of steel fiber reinforced fly ash concrete. For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. Build. Today Commun. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Mater. Determine the available strength of the compression members shown. This effect is relatively small (only. 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. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. ADS KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. 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. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Build. As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Explain mathematic . & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Mater. Infrastructure Research Institute | Infrastructure Research Institute 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. Flexural strength is commonly correlated to the compressive strength of a concrete mix, which allows field testing procedures to be consistent for all concrete applications on a project. What factors affect the concrete strength? Materials IM Index. & Tran, V. Q. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. 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 . 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 However, it is suggested that ANN can be utilized to predict the CS of SFRC. : New insights from statistical analysis and machine learning methods. Mater. Mater. Build. Sci. Civ. ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . Farmington Hills, MI 1 and 2. Eng. Constr. 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. Khan, M. A. et al. Adv. 26(7), 16891697 (2013). Res. 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. 34(13), 14261441 (2020). Eng. Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. 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. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Phone: +971.4.516.3208 & 3209, ACI Resource Center Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. 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. Constr. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. In contrast, the XGB and KNN had the most considerable fluctuation rate. Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). Build. 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. In many cases it is necessary to complete a compressive strength to flexural strength conversion. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Eur. (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 . Google Scholar. The current 4th edition of TR 34 includes the same method of correlation as BS EN 1992. Table 3 displays the modified hyperparameters of each convolutional, flatten, hidden, and pooling layer, including kernel and filter size and learning rate. Materials 15(12), 4209 (2022). Midwest, Feedback via Email the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Further information can be found in our Compressive Strength of Concrete post. The stress block parameter 1 proposed by Mertol et al. Meanwhile, AdaBoost predicted the CS of SFRC with a broader range of errors. Compressive strength prediction of recycled concrete based on deep learning. Hence, the presented study aims to compare various ML algorithms for CS prediction of SFRC based on all the influential parameters. Therefore, as can be perceived from Fig. Convert. Case Stud. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. & LeCun, Y. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. 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. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. Use of this design tool implies acceptance of the terms of use. Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. 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. Therefore, the data needs to be normalized to avoid the dominance effect caused by magnitude differences among input parameters34. 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). It is a measure of the maximum stress on the tension face of an unreinforced concrete beam or slab at the point of. Date:4/22/2021, Publication:Special Publication Constr. Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete. The minimum 28-day characteristic compressive strength and flexural strength for low-volume roads are 30 MPa and 3.8 MPa, respectively. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . Behbahani, H., Nematollahi, B. ANN model consists of neurons, weights, and activation functions18. : Validation, WritingReview & Editing. Article Effects of steel fiber content and type on static mechanical properties of UHPCC. 5) as a powerful tool for estimating the CS of concrete is now well-known6,38,44,45. Provided by the Springer Nature SharedIt content-sharing initiative. The CivilWeb Compressive Strength to Flexural Conversion worksheet is included in the CivilWeb Flexural Strength spreadsheet suite. Buy now for only 5. 115, 379388 (2019). Ren, G., Wu, H., Fang, Q. Build. 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. Constr. Date:11/1/2022, Publication:IJCSM 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 Flexural strength of concrete = 0.7 . Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. A. Date:11/1/2022, Publication:Structural Journal 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. Article 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. 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). Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. 175, 562569 (2018). Compressive strength test was performed on cubic and cylindrical samples, having various sizes. Eng. Constr. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. SVR model (as can be seen in Fig. Google Scholar. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. You are using a browser version with limited support for CSS. 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. CAS . 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. Mater. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. 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. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. Flexural strength is an indirect measure of the tensile strength of concrete. Mater. Materials 8(4), 14421458 (2015). The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. 1.2 The values in SI units are to be regarded as the standard. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. For CEM 1 type cements a very general relationship has often been applied; This provides only the most basic correlation between flexural strength and compressive strength and should not be used for design purposes. CAS Deng, F. et al. An. 12, the W/C ratio is the parameter that intensively affects the predicted CS. 2020, 17 (2020). Constr. To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. However, ANN performed accurately in predicting the CS of NC incorporating waste marble powder (R2=0.97) in the test set. Comput. 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. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. The findings show that up to a certain point, adding both HS and SF increases the compressive, tensile, and flexural strength of concrete at all curing ages. Flexural strength = 0.7 x fck Where f ck is the compressive strength cylinder of concrete in MPa (N/mm 2 ). 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. On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. Setti et al.12 also introduced ISF with different volume fractions (VISF) to the concrete and reported the improvement of CS of SFRC by increasing the content of ISF. 2018, 110 (2018). Please enter search criteria and search again, Informational Resources on flexural strength and compressive strength, Web Pages on flexural strength and compressive strength, FREQUENTLY ASKED QUESTIONS ON FLEXURAL STRENGTH AND COMPRESSIVE STRENGTH. 147, 286295 (2017). Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Difference between flexural strength and compressive strength? 12 illustrates the impact of SP on the predicted CS of SFRC. These are taken from the work of Croney & Croney. 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. Finally, the model is created by assigning the new data points to the category with the most neighbors. Limit the search results modified within the specified time. Young, B. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. 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. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. CAS 95, 106552 (2020). Second Floor, Office #207 In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. It was observed that among the concrete mixture properties, W/C ratio, fly-ash, and SP had the most significant effect on the CS of SFRC (W/C ratio was the most effective parameter). 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. 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 sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Mater. I Manag. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). Cem. Build. 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). Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Invalid Email Address. Bending occurs due to development of tensile force on tension side of the structure. Get the most important science stories of the day, free in your inbox. Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . 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. Eng. Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. Design of SFRC structural elements: post-cracking tensile strength measurement.
