26(7), 16891697 (2013). 41(3), 246255 (2010). 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. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. Evaluation metrics can be seen in Table 2, where \(N\), \(y_{i}\), \(y_{i}^{\prime }\), and \(\overline{y}\) represent the total amount of data, the true CS of the sample \(i{\text{th}}\), the estimated CS of the sample \(i{\text{th}}\), and the average value of the actual strength values, respectively. The primary rationale for using an SVR is that the problem may not be separable linearly. Southern California Mater. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Constr. 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). Adv. Flexural strength is measured by using concrete beams. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. Google Scholar. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. 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). 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! A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? Then, among K neighbors, each category's data points are counted. Article ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. 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. 34(13), 14261441 (2020). Is there such an equation, and, if so, how can I get a copy? 33(3), 04019018 (2019). Struct. Overall, it is possible to conclude that CNN produces more accurate predictions of the CS of SFRC with less uncertainty, followed by SVR and XGB. Flexural strength is however much more dependant on the type and shape of the aggregates used. Build. This effect is relatively small (only. Mater. Civ. Date:11/1/2022, Publication:IJCSM If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. Huang, J., Liew, J. Difference between flexural strength and compressive strength? Constr. Effects of steel fiber content and type on static mechanical properties of UHPCC. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. ASTM C 293 or ASTM C 78 techniques are used to measure the Flexural strength. The analyses of this investigation were focused on conversion factors for compressive strengths of different samples. Google Scholar. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). 12. fck = Characteristic Concrete Compressive Strength (Cylinder). In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). ANN model consists of neurons, weights, and activation functions18. Mater. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Materials 13(5), 1072 (2020). The stress block parameter 1 proposed by Mertol et al. 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. Schapire, R. E. Explaining adaboost. Mater. 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. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. Build. 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. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A. Eur. 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 . Mater. 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. Cite this article. As is reported by Kang et al.18, among implemented tree-based models, XGB performed superiorly in predicting the CS of SFRC. 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. These are taken from the work of Croney & Croney. A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. Civ. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). According to Table 1, input parameters do not have a similar scale. A parametric analysis was carried out to determine how well the developed ML algorithms can predict the effect of various input parameters on the CS behavior of SFRC. 4) has also been used to predict the CS of concrete41,42. 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. Shade denotes change from the previous issue. All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. Soft Comput. Mansour Ghalehnovi. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Zhang, Y. the input values are weighted and summed using Eq. Constr. Struct. The rock strength determined by . Based upon the results in this study, tree-based models performed worse than SVR in predicting the CS of SFRC. Bending occurs due to development of tensile force on tension side of the structure. This is a result of the use of the linear relationship in equation 3.1 of BS EN 1996-1-1 and was taken into account in the UK calibration. Constr. 266, 121117 (2021). The value of flexural strength is given by . Mater. Modulus of rupture is the behaviour of a material under direct tension. 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. 161, 141155 (2018). 38800 Country Club Dr. Buy now for only 5. Intersect. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Source: Beeby and Narayanan [4]. 49, 554563 (2013). 16, e01046 (2022). (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 . Abuodeh, O. R., Abdalla, J. 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. 9, the minimum and maximum interquartile ranges (IQRs) belong to AdaBoost and MLR, respectively. The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. Infrastructure Research Institute | Infrastructure Research Institute However, the understanding of ISF's influence on the compressive strength (CS) behavior of . Asadi et al.6 also reported that KNN performed poorly in predicting the CS of concrete containing waste marble powder. Flexural strength is commonly correlated to the compressive strength of a concrete mix, which allows field testing procedures to be consistent for all concrete applications on a project. Constr. The raw data is also available from the corresponding author on reasonable request. & Nitesh, K. S. Study on the effect of steel and glass fibers on fresh and hardened properties of vibrated concrete and self-compacting concrete. Struct. & Lan, X. From Table 2, it can be observed that the ratio of flexural to compressive strength for all OPS concrete containing different aggregate saturation is in the range of 12.7% to 16.9% which is. Article Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. Mahesh et al.19 noted that after tuning the model (number of hidden layers=20, activation function=Tansin Purelin), ANN showed superior performance in predicting the CS of SFRC (R2=0.95). Article Constr. Mater. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. (4). MATH Nguyen-Sy, T. et al. 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. Google Scholar. Karahan, O., Tanyildizi, H. & Atis, C. D. An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash. Privacy Policy | Terms of Use The result of this analysis can be seen in Fig. Compressive strength test was performed on cubic and cylindrical samples, having various sizes.

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