163, 376389 (2018). Meanwhile, AdaBoost predicted the CS of SFRC with a broader range of errors. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). Investigation of Compressive Strength of Slag-based - ResearchGate PDF Infrastructure Research Institute | Infrastructure Research Institute To adjust the validation sets hyperparameters, random search and grid search algorithms were used. Cite this article. Correspondence to 1 and 2. Date:10/1/2022, Publication:Special Publication Adv. [1] The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. October 18, 2022. This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. 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. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. The relationship between compressive strength and flexural strength of Provided by the Springer Nature SharedIt content-sharing initiative. Buildings 11(4), 158 (2021). This highlights the role of other mixs components (like W/C ratio, aggregate size, and cement content) on CS behavior of SFRC. An. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. Sci Rep 13, 3646 (2023). 95, 106552 (2020). 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. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. MathSciNet 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. Eng. The maximum value of 25.50N/mm2 for the 5% replacement level is found suitable and recommended having attained a 28- day compressive strength of more than 25.0N/mm2. Constr. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. The rock strength determined by . 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. Mater. Google Scholar. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. Moreover, according to the results reported by Kang et al.18, it was shown that using MLR led to a significant difference between actual and predicted values for prediction of SFRCs CS (RMSE=12.4273, MAE=11.3765). Today Proc. Properties of steel fiber reinforced fly ash concrete. Based on the developed models to predict the CS of SFRC (Fig. Behbahani, H., Nematollahi, B. This method has also been used in other research works like the one Khan et al.60 did. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. 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). PMLR (2015). Pengaruh Campuran Serat Pisang Terhadap Beton 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. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. Hence, the presented study aims to compare various ML algorithms for CS prediction of SFRC based on all the influential parameters. 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. J. Comput. Flexural Test on Concrete - Significance, Procedure and Applications Constr. Mater. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. Normal distribution of errors (Actual CSPredicted CS) for different methods. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Convert newton/millimeter [N/mm] to psi [psi] Pressure, Stress This index can be used to estimate other rock strength parameters. 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. Abuodeh, O. R., Abdalla, J. Mater. Commercial production of concrete with ordinary . 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. 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. In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. 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. Use of this design tool implies acceptance of the terms of use. Flexural strength calculator online | Math Workbook - Compasscontainer.com Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete, $$R_{XY} = \frac{{COV_{XY} }}{{\sigma_{X} \sigma_{Y} }}$$, $$x_{norm} = \frac{{x - x_{\min } }}{{x_{\max } - x_{\min } }}$$, $$\hat{y} = \alpha_{0} + \alpha_{1} x_{1} + \alpha_{2} x_{2} + \cdots + \alpha_{n} x_{n}$$, \(y = \left\langle {\alpha ,x} \right\rangle + \beta\), $$net_{j} = \sum\limits_{i = 1}^{n} {w_{ij} } x_{i} + b$$, https://doi.org/10.1038/s41598-023-30606-y. MLR is the most straightforward supervised ML algorithm for solving regression problems. Modulus of rupture is the behaviour of a material under direct tension. In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. S.S.P. PubMed Appl. This is much more difficult and less accurate than the equivalent concrete cube test, which is why it is common to test the compressive strength and then convert to flexural strength when checking the concrete's compliance with the specification. The site owner may have set restrictions that prevent you from accessing the site. 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. 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. Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. Search results must be an exact match for the keywords. Article The reason is the cutting embedding destroys the continuity of carbon . Flexural and fracture performance of UHPC exposed to - ScienceDirect Relation Between Compressive and Tensile Strength of Concrete ASTM C 293 or ASTM C 78 techniques are used to measure the Flexural strength. Caution should always be exercised when using general correlations such as these for design work. Mech. 11, and the correlation between input parameters and the CS of SFRC shown in Figs. 27, 102278 (2021). Adv. Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. Materials 15(12), 4209 (2022). In other words, the predicted CS decreases as the W/C ratio increases. Source: Beeby and Narayanan [4]. The primary rationale for using an SVR is that the problem may not be separable linearly. 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. 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. Design of SFRC structural elements: post-cracking tensile strength measurement. 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. Comparing ML models with regard to MAE and MAPE, it is seen that CNN performs superior in predicting the CS of SFRC, followed by GB and XGB. A. Frontiers | Behavior of geomaterial composite using sugar cane bagasse Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. . (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 . Nguyen-Sy, T. et al. Eng. Constr. 266, 121117 (2021). Finally, the model is created by assigning the new data points to the category with the most neighbors. Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. The flexural strength is stress at failure in bending. Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. All data generated or analyzed during this study are included in this published article. INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. Sci. It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. The formula to calculate compressive strength is F = P/A, where: F=The compressive strength (MPa) P=Maximum load (or load until failure) to the material (N) A=A cross-section of the area of the material resisting the load (mm2) Introduction Of Compressive Strength Cem. Build. 37(4), 33293346 (2021). MathSciNet Bending occurs due to development of tensile force on tension side of the structure. J. Ly, H.-B., Nguyen, T.-A. 2020, 17 (2020). The sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. 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. 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. Compressive Strength The main measure of the structural quality of concrete is its compressive strength. The main focus of this study is the development of a sustainable geomaterial composite with higher strength capabilities (compressive and flexural). Specifying Concrete Pavements: Compressive Strength or Flexural Strength A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units. Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. 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. 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. Southern California Constr. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. Mater. 11. Normalised and characteristic compressive strengths in Parametric analysis between parameters and predicted CS in various algorithms. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. J. Enterp. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. 12, the W/C ratio is the parameter that intensively affects the predicted CS. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. 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. 115, 379388 (2019). Whereas, Koya et al.39 and Li et al.54 reported that SVR showed a high difference between experimental and anticipated values in predicting the CS of NC. Based on the results obtained from the implementation of SVR in predicting the CS of SFRC and outcomes from previous studies in using the SVR to predict the CS of NC and SFRC, it was concluded that in some research, SVR demonstrated acceptable performance. PubMed Central flexural strength and compressive strength Topic Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Flexural strength of concrete = 0.7 . PDF Compressive strength to flexural strength conversion 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. Khademi, F., Akbari, M. & Jamal, S. M. Prediction of compressive strength of concrete by data-driven models. Consequently, it is frequently required to locate a local maximum near the global minimum59. Limit the search results from the specified source. One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. 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. Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . Gupta, S. Support vector machines based modelling of concrete strength. Limit the search results with the specified tags. 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). An appropriate relationship between flexural strength and compressive On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). 45(4), 609622 (2012). the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Therefore, these results may have deficiencies. Mater. Mater. Google Scholar. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. Constr. Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. Nowadays, For the production of prefabricated and in-situ concrete structures, SFRC is gaining acceptance such as (a) secondary reinforcement for temporary load scenarios, arresting shrinkage cracks, limiting micro-cracks occurring during transportation or installation of precast members (like tunnel lining segments), (b) partial substitution of the conventional reinforcement, i.e., hybrid reinforcement systems, and (c) total replacement of the typical reinforcement in compression-exposed elements, e.g., thin-shell structures, ground-supported slabs, foundations, and tunnel linings9.
Wayne County Newspaper Archives,
Biden Approval Rating Rasmussen,
Articles F