Moreover, GB is an AdaBoost development model, a meta-estimator that consists of many sequential decision trees that uses a step-by-step method to build an additive model6. 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. 230, 117021 (2020). Second Floor, Office #207 Flexural strength may range from 10% to 15% of the compressive strength depending on the concrete mix. However, ANN performed accurately in predicting the CS of NC incorporating waste marble powder (R2=0.97) in the test set. Mansour Ghalehnovi. SI is a standard error measurement, whose smaller values indicate superior model performance. Date:11/1/2022, Publication:IJCSM Source: Beeby and Narayanan [4]. Constr. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. ; The values of concrete design compressive strength f cd are given as . : Investigation, Conceptualization, Methodology, Data Curation, Formal analysis, WritingOriginal Draft; N.R. Chen, H., Yang, J. B Eng. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses Song, H. et al. 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 overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. The impact of the fly-ash on the predicted CS of SFRC can be seen in Fig. & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr. Invalid Email Address Gupta, S. Support vector machines based modelling of concrete strength. Most common test on hardened concrete is compressive strength test' It is because the test is easy to perform. 36(1), 305311 (2007). The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). 5) as a powerful tool for estimating the CS of concrete is now well-known6,38,44,45. Build. 248, 118676 (2020). 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. c - specified compressive strength of concrete [psi]. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. What factors affect the concrete strength? 2020, 17 (2020). It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. 6) has been increasingly used to predict the CS of concrete34,46,47,48,49. Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. For this purpose, 176 experimental data containing 11 features of SFRC are gathered from different journal papers. The forming embedding can obtain better flexural strength. PubMed 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). Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. 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. MathSciNet 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. J. Comput. The factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. Phys. Golafshani, E. M., Behnood, A. Huang, J., Liew, J. The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). 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. Mater. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. J. 12. 4: Flexural Strength Test. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. 324, 126592 (2022). Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. Table 3 provides the detailed information on the tuned hyperparameters of each model. The site owner may have set restrictions that prevent you from accessing the site. Build. In the current study, the architecture used was made up of a one-dimensional convolutional layer, a one-dimensional maximum pooling layer, a one-dimensional average pooling layer, and a fully-connected layer. This property of concrete is commonly considered in structural design. 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. Technol. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . MathSciNet How is the required strength selected, measured, and obtained? Mater. 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. Heliyon 5(1), e01115 (2019). fck = Characteristic Concrete Compressive Strength (Cylinder). 7). 1. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Table 3 shows the results of using a grid and a random search to tune the other hyperparameters. Invalid Email Address. It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. Ly, H.-B., Nguyen, T.-A. Date:10/1/2020, There are no Education Publications on flexural strength and compressive strength, View all ACI Education Publications on flexural strength and compressive strength , View all free presentations on flexural strength and compressive strength , There are no Online Learning Courses on flexural strength and compressive strength, View all ACI Online Learning Courses on flexural strength and compressive strength , Question: The effect of surface texture and cleanness on concrete strength, Question: The effect of maximum size of aggregate on concrete strength. 12, the SP has a medium impact on the predicted CS of SFRC. To obtain Eng. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. The flexural strength of a material is defined as its ability to resist deformation under load. PubMed Central Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. It's hard to think of a single factor that adds to the strength of concrete. MLR predicts the value of the dependent variable (\(y\)) based on the value of the independent variable (\(x\)) by establishing the linear relationship between inputs (independent parameters) and output (dependent parameter) based on Eq. & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced concrete. 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). 11(4), 1687814019842423 (2019). More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. Ray ID: 7a2c96f4c9852428 301, 124081 (2021). Mater. 118 (2021). It was observed that among the concrete mixture properties, W/C ratio, fly-ash, and SP had the most significant effect on the CS of SFRC (W/C ratio was the most effective parameter). 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. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Kang et al.18 collected a datasets containing 7 features (VISF and L/DISF as the properties of fibers) and developed 11 various ML techniques and observed that the tree-based models had the best performance in predicting the CS of SFRC. Artif. Comparing implemented ML algorithms in terms of Tstat, it is observed that XGB shows the best performance, followed by ANN and SVR in predicting the CS of SFRC. A. 2.9.1 Compressive strength of pervious concrete: Compressive strength of a concrete is a measure of its ability to resist static load, which tends to crush it. and JavaScript. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. However, regarding the Tstat, the outcomes show that CNN performance was approximately 58% lower than XGB. Flexural strength calculator online - We'll provide some tips to help you select the best Flexural strength calculator online for your needs. Several statistical parameters are also used as metrics to evaluate the performance of implemented models, such as coefficient of determination (R2), mean absolute error (MAE), and mean of squared error (MSE). Limit the search results modified within the specified time. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Constr. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. Dao, D. V., Ly, H.-B., Vu, H.-L.T., Le, T.-T. & Pham, B. T. Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete. 115, 379388 (2019). 2, it is obvious that the CS increased with increasing the SP (R=0.792) followed by fly ash (R=0.688) and C (R=0.501). 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). However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. Build. 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. A more useful correlations equation for the compressive and flexural strength of concrete is shown below. The stress block parameter 1 proposed by Mertol et al. 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 . Compressive strength test was performed on cubic and cylindrical samples, having various sizes. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. Constr. Mater. Mater. The proposed regression equations exhibit small errors when compared to the experimental results, which allow for efficient and accurate predictions of the flexural strength. 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. Adv. Struct. INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. In LOOCV, the number of folds is equal the number of instances in the dataset (n=176). Jamshidi Avanaki, M., Abedi, M., Hoseini, A. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. Also, Fig. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. Adv. Constr. PubMedGoogle Scholar. Khademi, F., Akbari, M. & Jamal, S. M. Prediction of compressive strength of concrete by data-driven models. Int. Limit the search results with the specified tags. World Acad. Among different ML algorithms, convolutional neural network (CNN) with R2=0.928, RMSE=5.043, and MAE=3.833 shows higher accuracy. : New insights from statistical analysis and machine learning methods. Civ. 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! Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Deng, F. et al. This index can be used to estimate other rock strength parameters. As shown in Fig. Cite this article. Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014). The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. In addition, Fig. Answer (1 of 5): For design of the beams we need flexuralstrength which is obtained from the characteristic strength by the formula Fcr=0.7FckFcr=0.7Fck Fck - is the characteristic strength Characteristic strength is found by applying compressive stress on concrete cubes after 28 days of cur. These equations are shown below. Res. 161, 141155 (2018). Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. Article 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. 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. 12). A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. 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). CAS 313, 125437 (2021). 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. 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. Build. 2021, 117 (2021). & Aluko, O. 11. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. The best-fitting line in SVR is a hyperplane with the greatest number of points. Mater. Mater. However, it is suggested that ANN can be utilized to predict the CS of SFRC. 27, 102278 (2021). To perform the parametric analysis to analyze the influence of one specific parameter (for example, W/C ratio) on the predicted CS of SFRC, the actual values of that parameter (W/C ratio) were considered, while the mean values for all the other input parameters values were introduced. 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. Khan, K. et al. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. 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). Where flexural strength is critical to the design a correlation specific to the concrete mix should be developed from testing and this relationship used for the specification and quality control. Adv. Normalised and characteristic compressive strengths in (3): where \(\hat{y}\), \(x_{n}\), and \(\alpha\) are the dependent parameter, independent parameter, and bias, respectively18. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. It is observed that in comparison models with R2, MSE, RMSE, and SI, CNN shows the best result in predicting the CS of SFRC, followed by SVR, and XGB. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Google Scholar. Beyond limits of material strength, this can lead to a permanent shape change or structural failure. MATH However, the understanding of ISF's influence on the compressive strength (CS) behavior of . The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. 73, 771780 (2014). J. Enterp. Mater. Martinelli, E., Caggiano, A. Finally, the model is created by assigning the new data points to the category with the most neighbors. Concr. 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 . In todays market, it is imperative to be knowledgeable and have an edge over the competition. Properties of steel fiber reinforced fly ash concrete. The compressive strength of the ordinary Portland cement / Pulverized Bentonitic Clay (PBC) generally decreases as the percentage of Pulverized Bentonitic Clay (PBC) content increases. 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). The brains functioning is utilized as a foundation for the development of ANN6. The simplest and most commonly applied method of quality control for concrete pavements is to test compressive strength and then use this as an indirect measure of the flexural strength. The primary sensitivity analysis is conducted to determine the most important features. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in http://creativecommons.org/licenses/by/4.0/. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Fluctuations of errors (Actual CSpredicted CS) for different algorithms. Zhang, Y. Build. where fr = modulus of rupture (flexural strength) at 28 days in N/mm 2. fc = cube compressive strength at 28 days in N/mm 2, and f c = cylinder compressive strength at 28 days in N/mm 2. Constr. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). Build. 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. Article 183, 283299 (2018). Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. 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. 163, 826839 (2018). Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems.
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