flexural strength to compressive strength converter

Comput. Plus 135(8), 682 (2020). ANN can be used to model complicated patterns and predict problems. Huang, J., Liew, J. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. World Acad. Constr. Mater. Build. Shade denotes change from the previous issue. Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. 2 illustrates the correlation between input parameters and the CS of SFRC. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. 1 and 2. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. Behbahani, H., Nematollahi, B. These cross-sectional forms included V-stiffeners in the web compression zone at 1/3 height near the compressed flange and no V-stiffeners on the flange . Phone: +971.4.516.3208 & 3209, ACI Resource Center Build. 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). 45(4), 609622 (2012). I Manag. 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. However, ANN performed accurately in predicting the CS of NC incorporating waste marble powder (R2=0.97) in the test set. Mater. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. 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: 301, 124081 (2021). Martinelli, E., Caggiano, A. In this paper, two factors of width-to-height ratio and span-to-height ratio are considered and 10 side-pressure laminated bamboo beams are prepared and tested for flexural capacity to study the flexural performance when they are used as structural members. 5(7), 113 (2021). 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. Build. Cem. Your IP: 103.74.122.237, Requested URL: www.concreteconstruction.net/how-to/correlating-compressive-and-flexural-strength_o, User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. 115, 379388 (2019). 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. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Sci Rep 13, 3646 (2023). PubMedGoogle Scholar. Values in inch-pound units are in parentheses for information. Build. 6) has been increasingly used to predict the CS of concrete34,46,47,48,49. 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. 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). 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). Properties of steel fiber reinforced fly ash concrete. Mater. Flexural strength is an indirect measure of the tensile strength of concrete. ADS 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. 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. Sci. : Investigation, Conceptualization, Methodology, Data Curation, Formal analysis, WritingOriginal Draft; N.R. 118 (2021). The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Constr. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. 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. 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. Constr. & Nitesh, K. S. Study on the effect of steel and glass fibers on fresh and hardened properties of vibrated concrete and self-compacting concrete. J. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. J. Enterp. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. As you can see the range is quite large and will not give a comfortable margin of certitude. Materials 15(12), 4209 (2022). Mater. Heliyon 5(1), e01115 (2019). Explain mathematic . Moreover, the regression function is \(y = \left\langle {\alpha ,x} \right\rangle + \beta\) and the aim of SVR is to flat the function as more as possible18. 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). & Tran, V. Q. Mater. Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. Constr. 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. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. Angular crushed aggregates achieve much greater flexural strength than rounded marine aggregates. Adam was selected as the optimizer function with a learning rate of 0.01. Normal distribution of errors (Actual CSPredicted CS) for different methods. Today Commun. Buildings 11(4), 158 (2021). 12. Invalid Email Address Limit the search results with the specified tags. 11(4), 1687814019842423 (2019). The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Skaryski, & Suchorzewski, J. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. Table 3 provides the detailed information on the tuned hyperparameters of each model. The feature importance of the ML algorithms was compared in Fig. Mater. Technol. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Li, Y. et al. Technol. Date:7/1/2022, Publication:Special Publication 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. Phone: 1.248.848.3800 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. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. 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. 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. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Date:11/1/2022, Publication:IJCSM J. Zhejiang Univ. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. The forming embedding can obtain better flexural strength. CAS In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. The flexural strength of UD, CP, and AP laminates was increased by 39-53%, 51-57%, and 25-37% with the addition of 0.1-0.2% MWCNTs. Build. Today Proc. Mater. Adv. 12, the SP has a medium impact on the predicted CS of SFRC. Then, among K neighbors, each category's data points are counted. 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. 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. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). Kabiru, O. 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). Further information can be found in our Compressive Strength of Concrete post. Use of this design tool implies acceptance of the terms of use. The primary sensitivity analysis is conducted to determine the most important features. It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete .