Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014). Khan, M. A. et al. STANDARDS, PRACTICES and MANUALS ON FLEXURAL STRENGTH AND COMPRESSIVE STRENGTH ACI CODE-350-20: Code Requirements for Environmental Engineering Concrete Structures (ACI 350-20) and Commentary (ACI 350R-20) ACI PRC-441.1-18: Report on Equivalent Rectangular Concrete Stress Block and Transverse Reinforcement for High-Strength Concrete Columns Build. Correspondence to Further information can be found in our Compressive Strength of Concrete post. However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). 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! 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. The authors declare no competing interests. Therefore, the data needs to be normalized to avoid the dominance effect caused by magnitude differences among input parameters34. All data generated or analyzed during this study are included in this published article. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). 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. Duan, J., Asteris, P. G., Nguyen, H., Bui, X.-N. & Moayedi, H. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. . Constr. Fluctuations of errors (Actual CSpredicted CS) for different algorithms. Build. Google Scholar. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). Thank you for visiting nature.com. fck = Characteristic Concrete Compressive Strength (Cylinder). It is a measure of the maximum stress on the tension face of an unreinforced concrete beam or slab at the point of. Most common test on hardened concrete is compressive strength test' It is because the test is easy to perform. Constr. Song, H. et al. & Chen, X. Among different ML algorithms, convolutional neural network (CNN) with R2=0.928, RMSE=5.043, and MAE=3.833 shows higher accuracy. Build. Int. 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). Setti, F., Ezziane, K. & Setti, B. Adv. Design of SFRC structural elements: post-cracking tensile strength measurement. Adam was selected as the optimizer function with a learning rate of 0.01. Date:10/1/2022, Publication:Special Publication 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. Eng. Artif. Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. Build. 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. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. 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). Al-Abdaly et al.50 also reported that RF (R2=0.88, RMSE=5.66, MAE=3.8) performed better than MLR (R2=0.64, RMSE=8.68, MAE=5.66) in predicting the CS of SFRC. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). Struct. Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. Phone: 1.248.848.3800, Home > Topics in Concrete > topicdetail, View all Documents on flexural strength and compressive strength , Publication:Materials Journal Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. Date:4/22/2021, Publication:Special Publication 11(4), 1687814019842423 (2019). It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. Date:11/1/2022, Publication:IJCSM 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. 209, 577591 (2019). PubMed Central The primary rationale for using an SVR is that the problem may not be separable linearly. Huang, J., Liew, J. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. The site owner may have set restrictions that prevent you from accessing the site. 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 . 23(1), 392399 (2009). Compressive strength, Flexural strength, Regression Equation I. This algorithm first calculates K neighbors euclidean distance. Bending occurs due to development of tensile force on tension side of the structure. the input values are weighted and summed using Eq. Golafshani, E. M., Behnood, A. You do not have access to www.concreteconstruction.net. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). What factors affect the concrete strength? Table 3 provides the detailed information on the tuned hyperparameters of each model. The raw data is also available from the corresponding author on reasonable request. Flexural strength of concrete = 0.7 . Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. Asadi et al.6 also reported that KNN performed poorly in predicting the CS of concrete containing waste marble powder. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Article If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. 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. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. As can be seen in Fig. 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. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. 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). Constr. 38800 Country Club Dr. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. 266, 121117 (2021). 2020, 17 (2020). 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. Flexural strength = 0.7 x fck Where f ck is the compressive strength cylinder of concrete in MPa (N/mm 2 ). How is the required strength selected, measured, and obtained? Limit the search results modified within the specified time. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). & Aluko, O. Is there such an equation, and, if so, how can I get a copy? PubMedGoogle Scholar. Chen, H., Yang, J. The reviewed contents include compressive strength, elastic modulus . A comparative investigation using machine learning methods for concrete compressive strength estimation. The stress block parameter 1 proposed by Mertol et al. Appl. Build. 26(7), 16891697 (2013). Phys. 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). Infrastructure Research Institute | Infrastructure Research Institute Performance of implimented algorithms in predicting CS of steel fiber-reinforced sconcrete (SFRC). ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab Today Proc. The air content was found to be the most significant fresh field property and has a negative correlation with both the compressive and flexural strengths. 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. 11. 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. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. 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. In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. Shade denotes change from the previous issue. The new concept and technology reveal that the engineering advantages of placing fiber in concrete may improve the flexural . Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete. Zhang, Y. CNN model is a new architecture for DL which is comprised of several layers that process and transform an input to produce an output. Res. Despite the enhancement of CS of normal strength concrete incorporating ISF, no significant change of CS is obtained for high-performance concrete mixes by increasing VISF14,15. Mater. These are taken from the work of Croney & Croney. Various orders of marked and unmarked errors in predictions are demonstrated by MSE, RMSE, MAE, and MBE6. Sci. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Six groups of austenitic 022Cr19Ni10 stainless steel bending specimens with three types of cross-sectional forms were used to study the impact of V-stiffeners on the failure mode and flexural behavior of stainless steel lipped channel beams. Privacy Policy | Terms of Use Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Constr. 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). 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. PubMed Therefore, as can be perceived from Fig. Use of this design tool implies acceptance of the terms of use. To perform the parametric analysis to analyze the influence of one specific parameter (for example, W/C ratio) on the predicted CS of SFRC, the actual values of that parameter (W/C ratio) were considered, while the mean values for all the other input parameters values were introduced. As shown in Fig. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. Erdal, H. I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. 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. According to Table 1, input parameters do not have a similar scale. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. 5) as a powerful tool for estimating the CS of concrete is now well-known6,38,44,45. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Build. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. Feature importance of CS using various algorithms. Sci. Mater. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Mater. Kabiru, O. [1] 95, 106552 (2020). Distributions of errors in MPa (Actual CSPredicted CS) for several methods. 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. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Eng. Tanyildizi, H. Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-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). Hameed et al.52 developed an MLR model to predict the CS of high-performance concrete (HPC) and noted that MLR had a poor correlation between the actual and predicted CS of HPC (R=0.789, RMSE=8.288). 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. & Lan, X. & LeCun, Y. & Liu, J. Civ. c - specified compressive strength of concrete [psi]. Where an accurate elasticity value is required this should be determined from testing. Article Regarding Fig. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. Case Stud. \(R\) shows the direction and strength of a two-variable relationship. In other words, the predicted CS decreases as the W/C ratio increases. 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. 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. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Kang, M.-C., Yoo, D.-Y. Question: How is the required strength selected, measured, and obtained? Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. Modulus of rupture is the behaviour of a material under direct tension. R2 is a metric that demonstrates how well a model predicts the value of a dependent variable and how well the model fits the data. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. Scientific Reports (Sci Rep) Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. These equations are shown below. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Hence, the presented study aims to compare various ML algorithms for CS prediction of SFRC based on all the influential parameters. Normalised and characteristic compressive strengths in Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. The compressive strength also decreased and the flexural strength increased when the EVA/cement ratio was increased. 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. Mater. Behbahani, H., Nematollahi, B. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. The flexural strengths of all the laminates tested are significantly higher than their tensile strengths, and are also higher than or similar to their compressive strengths. Phone: 1.248.848.3800 Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. Appl. Dubai World Trade Center Complex In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. Build. As you can see the range is quite large and will not give a comfortable margin of certitude. It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. 147, 286295 (2017). CAS 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. & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. To develop this composite, sugarcane bagasse ash (SA), glass . Pakzad, S.S., Roshan, N. & Ghalehnovi, M. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. 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. 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. Commercial production of concrete with ordinary . 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. Nguyen-Sy, T. et al. Supersedes April 19, 2022. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. The sugar industry produces a huge quantity of sugar cane bagasse ash in India. Constr. 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. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. 101. In Artificial Intelligence and Statistics 192204. SVR is considered as a supervised ML technique that predicts discrete values. Deng, F. et al. In recent years, CNN algorithm (Fig. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. Importance of flexural strength of . 308, 125021 (2021). 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. Mater.
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