East. 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 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. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. Invalid Email Address In recent years, CNN algorithm (Fig. \(R\) shows the direction and strength of a two-variable relationship. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Also, a significant difference between actual and predicted values was reported by Kang et al.18 in predicting the CS of SFRC (RMSE=18.024). Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. Li et al.54 noted that the CS of SFRC increased with increasing amounts of C and silica fume, and decreased with increasing amounts of water and SP. 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. Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. The KNN method is a simple supervised ML technique that can be utilized in order to solve both classification and regression problems. 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). 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. 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 impact of the fly-ash on the predicted CS of SFRC can be seen in Fig. CAS de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. However, ANN performed accurately in predicting the CS of NC incorporating waste marble powder (R2=0.97) in the test set. SVR model (as can be seen in Fig. B Eng. However, the understanding of ISF's influence on the compressive strength (CS) behavior of . Erdal, H. I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Han, J., Zhao, M., Chen, J. Also, the CS of SFRC was considered as the only output parameter. The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. Soft Comput. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses Mater. Most common test on hardened concrete is compressive strength test' It is because the test is easy to perform. 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. 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. Mater. Buildings 11(4), 158 (2021). Source: Beeby and Narayanan [4]. 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. Build. PubMed Central Mater. 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. Build. 12, the SP has a medium impact on the predicted CS of SFRC. Eng. 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). Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Seyed Soroush Pakzad,Naeim Roshan&Mansour Ghalehnovi, You can also search for this author in 73, 771780 (2014). Ren, G., Wu, H., Fang, Q. 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. & Xargay, H. An experimental study on the post-cracking behaviour of Hybrid Industrial/Recycled Steel Fibre-Reinforced Concrete. . Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. Intersect. Today Proc. Properties of steel fiber reinforced fly ash concrete. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. 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. Tree-based models performed worse than SVR in predicting the CS of SFRC. It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete . 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. 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. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Mater. Infrastructure Research Institute | Infrastructure Research Institute Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. Eng. (4). Internet Explorer). For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. 45(4), 609622 (2012). 12. Eng. Materials 15(12), 4209 (2022). Today Proc. Ray ID: 7a2c96f4c9852428 37(4), 33293346 (2021). Constr. Golafshani, E. M., Behnood, A. Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. In contrast, KNN shows the worst performance among developed ML models in predicting the CS of SFRC. CAS A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). 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. Mater. Date:3/3/2023, Publication:Materials Journal Article Abuodeh, O. R., Abdalla, J. A. 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. 26(7), 16891697 (2013). Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. Mater. In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. Build. Compressive strength test was performed on cubic and cylindrical samples, having various sizes. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength. Mater. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Based on the developed models to predict the CS of SFRC (Fig. Further information can be found in our Compressive Strength of Concrete post. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Mater. 6(4) (2009). Huang, J., Liew, J. Materials 13(5), 1072 (2020). Question: Are there data relating w/cm to flexural strength that are as reliable as those for compressive View all Frequently Asked Questions on flexural strength and compressive strength», View all flexural strength and compressive strength Events , The Concrete Industry in the Era of Artificial Intelligence, There are no Committees on flexural strength and compressive strength, Concrete Laboratory Testing Technician - Level 1. Adv. Cloudflare is currently unable to resolve your requested domain. 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. 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). Mater. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. Cem. The minimum 28-day characteristic compressive strength and flexural strength for low-volume roads are 30 MPa and 3.8 MPa, respectively. Constr. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab 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. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. There is a dropout layer after each hidden layer (The dropout layer sets input units to zero at random with a frequency rate at each training step, hence preventing overfitting). 115, 379388 (2019). 163, 826839 (2018). Mater. 260, 119757 (2020). Gupta, S. Support vector machines based modelling of concrete strength. Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). Adv. 12 illustrates the impact of SP on the predicted CS of SFRC. It is equal to or slightly larger than the failure stress in tension. Table 3 shows the results of using a grid and a random search to tune the other hyperparameters. Appl. Khan, K. et al. Date:7/1/2022, Publication:Special Publication Jang, Y., Ahn, Y. 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. This research leads to the following conclusions: Among the several ML techniques used in this research, CNN attained superior performance (R2=0.928, RMSE=5.043, MAE=3.833), followed by SVR (R2=0.918, RMSE=5.397, MAE=4.559). S.S.P. Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. 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. Constr. Also, Fig. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. 2018, 110 (2018). Table 4 indicates the performance of ML models by various evaluation metrics. Thank you for visiting nature.com. Mater. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. 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. It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. 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. 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. Sci. In contrast, the XGB and KNN had the most considerable fluctuation rate. Date:10/1/2020, There are no Education Publications on flexural strength and compressive strength, View all ACI Education Publications on flexural strength and compressive strength , View all free presentations on flexural strength and compressive strength , There are no Online Learning Courses on flexural strength and compressive strength, View all ACI Online Learning Courses on flexural strength and compressive strength , Question: The effect of surface texture and cleanness on concrete strength, Question: The effect of maximum size of aggregate on concrete strength. 12). A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? J. Devries. Caution should always be exercised when using general correlations such as these for design work. As shown in Fig. Mater. Civ. Constr. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. Flexural strength is however much more dependant on the type and shape of the aggregates used. J. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. The value of flexural strength is given by . Invalid Email Address. Sci. Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. In many cases it is necessary to complete a compressive strength to flexural strength conversion. This can be due to the difference in the number of input parameters. The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. Civ. Explain mathematic . 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. In addition, Fig. The stress block parameter 1 proposed by Mertol et al. Constr. Phone: 1.248.848.3800, Home > Topics in Concrete > topicdetail, View all Documents on flexural strength and compressive strength , Publication:Materials Journal The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. Mater. In Artificial Intelligence and Statistics 192204. 28(9), 04016068 (2016). (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. Determine the available strength of the compression members shown. Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . This online unit converter allows quick and accurate conversion . The feature importance of the ML algorithms was compared in Fig. Among different ML algorithms, convolutional neural network (CNN) with R2=0.928, RMSE=5.043, and MAE=3.833 shows higher accuracy. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. Constr. 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. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Skaryski, & Suchorzewski, J. Convert. Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Finally, it is observed that ANN performs weaker than SVR and XGB in terms of R2 in the validation set due to the non-convexity of the multilayer perceptron's loss surface. Res. What factors affect the concrete strength? 267, 113917 (2021). In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. 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! PubMed Central Date:2/1/2023, Publication:Special Publication Schapire, R. E. Explaining adaboost. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). The raw data is also available from the corresponding author on reasonable request. Transcribed Image Text: SITUATION A. Accordingly, 176 sets of data are collected from different journals and conference papers. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. 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. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. However, the understanding of ISFs influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. 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. Article Constr. PubMed Central ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. sqrt(fck) Where, fck is the characteristic compressive strength of concrete in MPa. Area and Volume Calculator; Concrete Mixture Proportioner (iPhone) Concrete Mixture Proportioner (iPad) Evaporation Rate Calculator; Joint Noise Estimator; Maximum Joint Spacing Calculator 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. 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).

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