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. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. 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. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. 1. Appl. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. 118 (2021). PubMed Central 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. 12). MLR is the most straightforward supervised ML algorithm for solving regression problems. The sugar industry produces a huge quantity of sugar cane bagasse ash in India. It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. Kang, M.-C., Yoo, D.-Y. SVR is considered as a supervised ML technique that predicts discrete values. 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. Evidently, SFRC comprises a bigger number of components than NC including LISF, L/DISF, fiber type, diameter of ISF (DISF) and the tensile strength of ISFs. Hence, the presented study aims to compare various ML algorithms for CS prediction of SFRC based on all the influential parameters. 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. Source: Beeby and Narayanan [4]. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. This index can be used to estimate other rock strength parameters. Flexural strength is an indirect measure of the tensile strength of concrete. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. Struct. Build. Farmington Hills, MI Regarding Fig. 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). To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Compressive Strength The main measure of the structural quality of concrete is its compressive strength. Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). The rock strength determined by . | Copyright ACPA, 2012, American Concrete Pavement Association (Home). & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength. The impact of the fly-ash on the predicted CS of SFRC can be seen in Fig. Compressive strengthis defined as resistance of material under compression prior to failure or fissure, it can be expressed in terms of load per unit area and measured in MPa. 5(7), 113 (2021). Mater. Limit the search results modified within the specified time. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. Constr. 45(4), 609622 (2012). This algorithm first calculates K neighbors euclidean distance. 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. As you can see the range is quite large and will not give a comfortable margin of certitude. 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. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). Today Proc. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. In contrast, the XGB and KNN had the most considerable fluctuation rate. Moreover, among the three proposed ML models here, SVR demonstrates superior performance in estimating the influence of the W/C ratio on the predicted CS of SFRC with a correlation of R=0.999, followed by CNN with a correlation of R=0.96. The flexural properties and fracture performance of UHPC at low-temperature environment ( T = 20, 30, 60, 90, 120, and 160 C) were experimentally investigated in this paper. [1] The value for s then becomes: s = 0.09 (550) s = 49.5 psi Mater. Jang, Y., Ahn, Y. Invalid Email Address the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in MATH 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). Mater. 2020, 17 (2020). 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. 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). Ray ID: 7a2c96f4c9852428 Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. In other words, the predicted CS decreases as the W/C ratio increases. : Validation, WritingReview & Editing. 163, 376389 (2018). World Acad. Mater. Phys. Mater. Concr. 11. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. The brains functioning is utilized as a foundation for the development of ANN6. 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Compressive strength, Flexural strength, Regression Equation I. 11(4), 1687814019842423 (2019). Date:3/3/2023, Publication:Materials Journal The main focus of this study is the development of a sustainable geomaterial composite with higher strength capabilities (compressive and flexural). 209, 577591 (2019). A. 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. 12, the W/C ratio is the parameter that intensively affects the predicted CS. Materials IM Index. This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). 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. Fluctuations of errors (Actual CSpredicted CS) for different algorithms. Zhu, H., Li, C., Gao, D., Yang, L. & Cheng, S. Study on mechanical properties and strength relation between cube and cylinder specimens of steel fiber reinforced concrete. 1 and 2. 49, 554563 (2013). Soft Comput. The forming embedding can obtain better flexural strength. Search results must be an exact match for the keywords. Abuodeh, O. R., Abdalla, J. 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. 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. Email Address is required 16, e01046 (2022). For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). 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. The testing of flexural strength in concrete is generally undertaken using a third point flexural strength test on a beam of concrete. Sci. Adv. Build. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. 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. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. The feature importance of the ML algorithms was compared in Fig. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC.
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