However, it is suggested that ANN can be utilized to predict the CS of SFRC. Sci. Cem. In addition, the studies based on ML techniques that have been done to predict the CS of SFRC are limited since it is difficult to collect inclusive experimental data to develop models regarding all contributing features (such as the properties of fibers, aggregates, and admixtures). 45(4), 609622 (2012). J. Devries. Deng, F. et al. 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. percent represents the compressive strength indicated by a standard 6- by 12-inch cylinder with a length/diameter (L/D) ratio of 2.0, then a 6-inch-diameter specimen 9 inches long . Mater. 28(9), 04016068 (2016). 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. Nguyen-Sy, T. et al. 12 illustrates the impact of SP on the predicted CS of SFRC. Flexural strength calculator online - We'll provide some tips to help you select the best Flexural strength calculator online for your needs. A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. Table 4 indicates the performance of ML models by various evaluation metrics. Among different ML algorithms, convolutional neural network (CNN) with R2=0.928, RMSE=5.043, and MAE=3.833 shows higher accuracy. The minimum 28-day characteristic compressive strength and flexural strength for low-volume roads are 30 MPa and 3.8 MPa, respectively. The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. Build. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. Constr. American Concrete Pavement Association, its Officers, Board of Directors and Staff are absolved of any responsibility for any decisions made as a result of your use. Date:9/30/2022, Publication:Materials Journal Google Scholar. 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! Marcos-Meson, V. et al. Constr. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. Res. Flexural test evaluates the tensile strength of concrete indirectly. This index can be used to estimate other rock strength parameters. Also, Fig. 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). 34(13), 14261441 (2020). Mater. 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. Based on the developed models to predict the CS of SFRC (Fig. Build. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. However, the addition of ISF into the concrete and producing the SFRC may also provide additional strength capacity or act as the primary reinforcement in structural elements. The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. Google Scholar. Technol. This method has also been used in other research works like the one Khan et al.60 did. 94, 290298 (2015). Compos. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. Answer (1 of 5): For design of the beams we need flexuralstrength which is obtained from the characteristic strength by the formula Fcr=0.7FckFcr=0.7Fck Fck - is the characteristic strength Characteristic strength is found by applying compressive stress on concrete cubes after 28 days of cur. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Mater. Compressive strength result was inversely to crack resistance. Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. 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. Appl. Mansour Ghalehnovi. Eng. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. fck = Characteristic Concrete Compressive Strength (Cylinder). As can be seen in Fig. Phys. Case Stud. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. Modulus of rupture is the behaviour of a material under direct tension. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. Adv. Build. For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). Sci. Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). The loss surfaces of multilayer networks. 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 result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Constr. The KNN method is a simple supervised ML technique that can be utilized in order to solve both classification and regression problems. Based upon the results in this study, tree-based models performed worse than SVR in predicting the CS of SFRC. This indicates that the CS of SFRC cannot be predicted by only the amount of ISF in the mix. Mahesh, R. & Sathyan, D. Modelling the hardened properties of steel fiber reinforced concrete using ANN. To obtain J. Schapire, R. E. Explaining adaboost. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). (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 . Mater. The raw data is also available from the corresponding author on reasonable request. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. 118 (2021). 5(7), 113 (2021). where fr = modulus of rupture (flexural strength) at 28 days in N/mm 2. fc = cube compressive strength at 28 days in N/mm 2, and f c = cylinder compressive strength at 28 days in N/mm 2. . 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. 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. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: Constr. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. J. Zhejiang Univ. In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. 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. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 163, 826839 (2018). From the open literature, a dataset was collected that included 176 different concrete compressive test sets. Build. October 18, 2022. Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. & Kim, H. Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. Article Therefore, these results may have deficiencies. Table 3 displays the modified hyperparameters of each convolutional, flatten, hidden, and pooling layer, including kernel and filter size and learning rate. One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. and JavaScript. The simplest and most commonly applied method of quality control for concrete pavements is to test compressive strength and then use this as an indirect measure of the flexural strength. Compos. Limit the search results with the specified tags. A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. It is equal to or slightly larger than the failure stress in tension. Song, H. et al. As shown in Fig. Build. These are taken from the work of Croney & Croney. 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). Eur. 36(1), 305311 (2007). All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. 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. Build. Second Floor, Office #207 Limit the search results from the specified source. Use of this design tool implies acceptance of the terms of use. 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). Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Effects of steel fiber content and type on static mechanical properties of UHPCC. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Google Scholar. Build. Convert. ADS Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . 1. This is particularly common in the design and specification of concrete pavements where flexural strengths are critical while compressive strengths are often specified. 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. 2(2), 4964 (2018). Also, the CS of SFRC was considered as the only output parameter. 11(4), 1687814019842423 (2019). Constr. 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. Table 3 shows the results of using a grid and a random search to tune the other hyperparameters. Properties of steel fiber reinforced fly ash concrete. Article Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Skaryski, & Suchorzewski, J. In todays market, it is imperative to be knowledgeable and have an edge over the competition. PubMed These measurements are expressed as MR (Modules of Rupture). Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. 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. In SVR, \(\{ x_{i} ,y_{i} \} ,i = 1,2,,k\) is the training set, where \(x_{i}\) and \(y_{i}\) are the input and output values, respectively. The best-fitting line in SVR is a hyperplane with the greatest number of points. Provided by the Springer Nature SharedIt content-sharing initiative. ; The values of concrete design compressive strength f cd are given as . Civ. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. Eng. & Tran, V. Q. 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. Mater. The capabilities of ML algorithms were demonstrated through a sensitivity analysis and parametric analysis. Cem. An. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Mater. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Eng. The Offices 2 Building, One Central Invalid Email Address. The SFRC mixes containing hooked ISF and their 28-day CS (tested by 150mm cubic samples) were collected from the literature11,13,21,22,23,24,25,26,27,28,29,30,31,32,33. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. Get the most important science stories of the day, free in your inbox. However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF).
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