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. Constr. Constr. Build. The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). Build. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. The compressive strength also decreased and the flexural strength increased when the EVA/cement ratio was increased. Properties of steel fiber reinforced fly ash concrete. Mater. 3) was used to validate the data and adjust the hyperparameters. Ray ID: 7a2c96f4c9852428 You do not have access to www.concreteconstruction.net. Where the modulus of elasticity of the concrete is required to complete a design there is a correlation equation relating flexural strength with the modulus of elasticity, shown below. 34(13), 14261441 (2020). Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International J. Comput. Dubai, UAE Behbahani, H., Nematollahi, B. 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. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. October 18, 2022. 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. Mater. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Among these techniques, AdaBoost is the most straightforward boosting algorithm that is based on the idea that a very accurate prediction rule can be made by combining a lot of less accurate regulations43. Mater. Mater. Article Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. The minimum performance requirements of each GCCM Classification Type have been defined within ASTM D8364, defining the appropriate GCCM specific test standards to use, such as: ASTM D8329 for compressive strength and ASTM D8058 for flexural strength. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. Sanjeev, J. Khan, K. et al. 2020, 17 (2020). Lee, S.-C., Oh, J.-H. & Cho, J.-Y. 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. Mater. Provided by the Springer Nature SharedIt content-sharing initiative. ADS Sci. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. Compressive strength, Flexural strength, Regression Equation I. 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. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. Based on this, CNN had the closest distribution to the normal distribution and produced the best results for predicting the CS of SFRC, followed by SVR and RF. 41(3), 246255 (2010). 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. Gupta, S. Support vector machines based modelling of concrete strength. ANN model consists of neurons, weights, and activation functions18. Where an accurate elasticity value is required this should be determined from testing. 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. Asadi et al.6 also reported that KNN performed poorly in predicting the CS of concrete containing waste marble powder. Intersect. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. Materials IM Index. 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). MATH Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Khademi, F., Akbari, M. & Jamal, S. M. Prediction of compressive strength of concrete by data-driven models. This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. Based on the developed models to predict the CS of SFRC (Fig. Email Address is required Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. Characteristic compressive strength (MPa) Flexural Strength (MPa) 20: 3.13: 25: 3.50: 30: To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 6(5), 1824 (2010). 183, 283299 (2018). A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Zhang, Y. 5) as a powerful tool for estimating the CS of concrete is now well-known6,38,44,45. The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Mech. Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. : Validation, WritingReview & Editing. Finally, the model is created by assigning the new data points to the category with the most neighbors. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. 26(7), 16891697 (2013). Constr. Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) These equations are shown below. Sci. The test jig used in this video has a scale on the receiver, and the distance between the external fulcrums (distance between the two outer fulcrums . Mater. The factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. Accordingly, 176 sets of data are collected from different journals and conference papers. Date:11/1/2022, Publication:Structural Journal Convert. Build. The feature importance of the ML algorithms was compared in Fig. Adv. Mater. 147, 286295 (2017). 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. The best-fitting line in SVR is a hyperplane with the greatest number of points. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Struct. 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 . Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. 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. Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. The primary rationale for using an SVR is that the problem may not be separable linearly. Build. Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. & LeCun, Y. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. 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. Constr. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. Get the most important science stories of the day, free in your inbox. 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. What factors affect the concrete strength? ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. For design of building members an estimate of the MR is obtained by: , where 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. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. Song, H. et al. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. 2 illustrates the correlation between input parameters and the CS of SFRC. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Most common test on hardened concrete is compressive strength test' It is because the test is easy to perform. Build. 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. 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. 38800 Country Club Dr. Constr. Google Scholar. 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 The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. 73, 771780 (2014). 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 . The minimum 28-day characteristic compressive strength and flexural strength for low-volume roads are 30 MPa and 3.8 MPa, respectively. These equations are shown below. Build. Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. Int. Kabiru, O. Adv. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. 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. 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. On the other hand, K-nearest neighbor (KNN) algorithm with R2=0.881, RMSE=6.477, and MAE=4.648 results in the weakest performance. The flexural strength is stress at failure in bending. 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 CivilWeb Flexural Strength of Concrete suite of spreadsheets includes the two methods described above, as well as the modulus of elasticity to flexural strength converter. A good rule-of-thumb (as used in the ACI Code) is: Build. Moreover, the CS of rubberized concrete was predicted using KNN algorithm by Hadzima-Nyarko et al.53, and it was reported that KNN might not be appropriate for estimating the CS of concrete containing waste rubber (RMSE=8.725, MAE=5.87). 27, 15591568 (2020). 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. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Mater. 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). Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Build. 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). Appl. The predicted values were compared with the actual values to demonstrate the feasibility of ML algorithms (Fig. ; The values of concrete design compressive strength f cd are given as . A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. As can be seen in Table 3, nine different algorithms were implemented in this research, including MLR, KNN, SVR, RF, GB, XGB, AdaBoost, ANN, and CNN. For example compressive strength of M20concrete is 20MPa. PubMed Central 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. Date:2/1/2023, Publication:Special Publication These are taken from the work of Croney & Croney. Mater. Huang, J., Liew, J. Phone: 1.248.848.3800, Home > Topics in Concrete > topicdetail, View all Documents on flexural strength and compressive strength , Publication:Materials Journal 2021, 117 (2021). Google Scholar. Recently, ML algorithms have been widely used to predict the CS of concrete. Limit the search results modified within the specified time. & Liu, J. Then, among K neighbors, each category's data points are counted. Jang, Y., Ahn, Y. I Manag. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. sqrt(fck) Where, fck is the characteristic compressive strength of concrete in MPa. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. 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. The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. It uses two general correlations commonly used to convert concrete compression and floral strength. 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. According to Table 1, input parameters do not have a similar scale. The flexural loaddeflection responses, shown in Fig. 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! For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. 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. The flexural strength of concrete was found to be 8 to 11% of the compressive strength of concrete of higher strength concrete of the order of 25 MPa (250 kg/cm2) and 9 to 12.8% for concrete of strength less than 25 MPa (250 kg/cm2) see Table 13.1: In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. The stress block parameter 1 proposed by Mertol et al. 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.
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