J. 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. The feature importance of the ML algorithms was compared in Fig. Parametric analysis between parameters and predicted CS in various algorithms. Constr. 260, 119757 (2020). Therefore, according to the KNN results in predicting the CS of SFRC and compatibility with previous studies (in using the KNN in predicting the CS of various concrete types), it was observed that like MLR, KNN technique could not perform promisingly in predicting the CS of SFRC. Eng. Buy now for only 5. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. Gupta, S. Support vector machines based modelling of concrete strength. Correspondence to ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Appl. 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). One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Mater. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. Cite this article. Flexural strength is however much more dependant on the type and shape of the aggregates used. Limit the search results from the specified source. 313, 125437 (2021). A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Sanjeev, J. Asadi et al.6 also used ANN in estimating the CS of NC containing waste marble powder (LOOCV was used to tune the hyperparameters) and reported that in the validation set, ANN was unable to reach an R2 as high as GB and XGB. As can be seen in Table 3, nine different algorithms were implemented in this research, including MLR, KNN, SVR, RF, GB, XGB, AdaBoost, ANN, and CNN. Struct. Limit the search results with the specified tags. Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. Mater. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Article 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. Knag et al.18 reported that silica fume, W/C ratio, and DMAX are the most influential parameters that predict the CS of SFRC. 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! It is essential to note that, normalization generally speeds up learning and leads to faster convergence. This online unit converter allows quick and accurate conversion . Finally, it is observed that ANN performs weaker than SVR and XGB in terms of R2 in the validation set due to the non-convexity of the multilayer perceptron's loss surface. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Mater. PubMed 6) has been increasingly used to predict the CS of concrete34,46,47,48,49. 6(5), 1824 (2010). Struct. Jang, Y., Ahn, Y. The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". Compressive strength test was performed on cubic and cylindrical samples, having various sizes. Compos. Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . Song, H. et al. 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. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. The same results are also reported by Kang et al.18. (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. These equations are shown below. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). 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. Company Info. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. & Arashpour, M. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . Based on the developed models to predict the CS of SFRC (Fig. Midwest, Feedback via Email Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. The primary rationale for using an SVR is that the problem may not be separable linearly. Constr. 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. In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. 33(3), 04019018 (2019). Constr. 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). : Validation, WritingReview & Editing. Then, among K neighbors, each category's data points are counted. 41(3), 246255 (2010). 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. The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Article American Concrete Pavement Association, its Officers, Board of Directors and Staff are absolved of any responsibility for any decisions made as a result of your use. 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 . The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. Difference between flexural strength and compressive strength? S.S.P. 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. The air content was found to be the most significant fresh field property and has a negative correlation with both the compressive and flexural strengths. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Question: How is the required strength selected, measured, and obtained? Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. 5(7), 113 (2021). The flexural loaddeflection responses, shown in Fig. 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). 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. It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. Khademi, F., Akbari, M. & Jamal, S. M. Prediction of compressive strength of concrete by data-driven models. The flexural strength is stress at failure in bending. 6(4) (2009). As can be seen in Fig. Marcos-Meson, V. et al. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. According to EN1992-1-1 3.1.3(2) the following modifications are applicable for the value of the concrete modulus of elasticity E cm: a) for limestone aggregates the value should be reduced by 10%, b) for sandstone aggregates the value should be reduced by 30%, c) for basalt aggregates the value should be increased by 20%. (3): where \(\hat{y}\), \(x_{n}\), and \(\alpha\) are the dependent parameter, independent parameter, and bias, respectively18. 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). Constr. 2(2), 4964 (2018). Among different ML algorithms, convolutional neural network (CNN) with R2=0.928, RMSE=5.043, and MAE=3.833 shows higher accuracy. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. 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. Finally, the model is created by assigning the new data points to the category with the most neighbors. 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. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. Date:3/3/2023, Publication:Materials Journal Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). 12. Google Scholar. Build. It uses two general correlations commonly used to convert concrete compression and floral strength. http://creativecommons.org/licenses/by/4.0/. 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. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. ADS If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. An. Deng et al.47 also observed that CNN was better at predicting the CS of recycled concrete (average relative error=3.65) than other methods. Koya, B. P., Aneja, S., Gupta, R. & Valeo, C. Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. 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. Accordingly, several statistical parameters such as R2, MSE, mean absolute percentage error (MAPE), root mean squared error (RMSE), average bias error (MBE), t-statistic test (Tstat), and scatter index (SI) were used. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. 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. Article 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. This property of concrete is commonly considered in structural design. (4). Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Seyed Soroush Pakzad,Naeim Roshan&Mansour Ghalehnovi, You can also search for this author in Therefore, based on MLR performance in the prediction CS of SFRC and consistency with previous studies (in using the MLR to predict the CS of NC, HPC, and SFRC), it was suggested that, due to the complexity of the correlation between the CS and concrete mix properties, linear models (such as MLR) could not explain the complicated relationship among independent variables. It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. I Manag. CAS Buildings 11(4), 158 (2021). J. The sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. Compos. ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. Mater. Farmington Hills, MI So, more complex ML models such as KNN, SVR tree-based models, ANN, and CNN were proposed and implemented to study the CS of SFRC. The loss surfaces of multilayer networks. Mater. For design of building members an estimate of the MR is obtained by: , where & LeCun, Y. 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. This index can be used to estimate other rock strength parameters. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. SVR is considered as a supervised ML technique that predicts discrete values. de-Prado-Gil, J., Palencia, C., Silva-Monteiro, N. & Martnez-Garca, R. To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models. Intersect. This research leads to the following conclusions: Among the several ML techniques used in this research, CNN attained superior performance (R2=0.928, RMSE=5.043, MAE=3.833), followed by SVR (R2=0.918, RMSE=5.397, MAE=4.559). For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. 49, 554563 (2013). As with any general correlations this should be used with caution. Mater. 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. 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: These are taken from the work of Croney & Croney. According to the presented literature, the scientific community is still uncertain about the CS behavior of SFRC. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. The reason is the cutting embedding destroys the continuity of carbon . 118 (2021). New Approaches Civ. Build. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. The presented paper aims to use machine learning (ML) and deep learning (DL) algorithms to predict the CS of steel fiber reinforced concrete (SFRC) incorporating hooked ISF based on the data collected from the open literature. 163, 826839 (2018). & Tran, V. Q. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Flexural tensile strength can also be calculated from the mean tensile strength by the following expressions. Development of deep neural network model to predict the compressive strength of rubber concrete. Eng. Ray ID: 7a2c96f4c9852428 The value for s then becomes: s = 0.09 (550) s = 49.5 psi 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. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842. ADS 11, and the correlation between input parameters and the CS of SFRC shown in Figs. 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. Constr. Based on this, CNN had the closest distribution to the normal distribution and produced the best results for predicting the CS of SFRC, followed by SVR and RF. 3- or 7-day test results are used to monitor early strength gain, especially when high early-strength concrete is used. Constr. Moreover, in a study conducted by Awolusi et al.20 only 3 features (L/DISF as the fiber properties) were considered, and ANN and the genetic algorithm models were implemented to predict the CS of SFRC. Thank you for visiting nature.com. Schapire, R. E. Explaining adaboost. 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. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Flexural strength is about 10 to 15 percent of compressive strength depending on the mixture proportions and type, size and volume of coarse aggregate used. PubMed Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. Invalid Email Address In comparison to the other discussed methods, CNN was able to accurately predict the CS of SFRC with a significantly reduced dispersion degree in the figures displaying the relationship between actual and expected CS of SFRC. 4) has also been used to predict the CS of concrete41,42. 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). Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Caggiano, A., Folino, P., Lima, C., Martinelli, E. & Pepe, M. On the mechanical response of hybrid fiber reinforced concrete with recycled and industrial steel fibers. You've requested a page on a website (cloudflarepreview.com) that is on the Cloudflare network. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. The spreadsheet is also included for free with the CivilWeb Rigid Pavement Design suite. 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. Mater. 232, 117266 (2020). TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. The CivilWeb Compressive Strength to Flexural Conversion worksheet is included in the CivilWeb Flexural Strength spreadsheet suite. Flexural strength is an indirect measure of the tensile strength of concrete. Second Floor, Office #207 MathSciNet Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Abuodeh, O. R., Abdalla, J. It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete . 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. MathSciNet Hence, various types of fibers are added to increase the tensile load-bearing capability of concrete. Adv. 11(4), 1687814019842423 (2019). ISSN 2045-2322 (online). 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. & Nitesh, K. S. Study on the effect of steel and glass fibers on fresh and hardened properties of vibrated concrete and self-compacting concrete. Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. 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. Internet Explorer). This method has also been used in other research works like the one Khan et al.60 did. This can refer to the fact that KNN considers all characteristics equally, even if they all contribute differently to the CS of concrete6. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. The use of an ANN algorithm (Fig. \(R\) shows the direction and strength of a two-variable relationship. Ly, H.-B., Nguyen, T.-A. MATH Since the specified strength is flexural strength, a conversion factor must be used to obtain an approximate compressive strength in order to use the water-cement ratio vs. compressive strength table. For CEM 1 type cements a very general relationship has often been applied; This provides only the most basic correlation between flexural strength and compressive strength and should not be used for design purposes. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. 12). The alkali activated mortar based on the ultrafine particle of GPOFA produced a maximum compressive strength (57.5 MPa), flexural strength (10.9 MPa), porosity (13.1%), water absorption (6.2% . 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). Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. It is also observed that a lower flexural strength will be measured with larger beam specimens. B Eng. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. 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. J Civ Eng 5(2), 1623 (2015). The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. Constr. According to section 19.2.1.3 of ACI 318-19 the specified compressive strength shall be based on the 28-day test results unless otherwise specified in the construction documents. Scientific Reports Consequently, it is frequently required to locate a local maximum near the global minimum59. Build. 2021, 117 (2021). 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. ANN model consists of neurons, weights, and activation functions18. 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. 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). 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). Values in inch-pound units are in parentheses for information. In the meantime, to ensure continued support, we are displaying the site without styles On the other hand, K-nearest neighbor (KNN) algorithm with R2=0.881, RMSE=6.477, and MAE=4.648 results in the weakest performance. Compressive strength prediction of recycled concrete based on deep learning. Table 3 provides the detailed information on the tuned hyperparameters of each model. Today Commun. Flexural strength of concrete = 0.7 . CNN model is a new architecture for DL which is comprised of several layers that process and transform an input to produce an output. However, it is suggested that ANN can be utilized to predict the CS of SFRC. 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. Build. Constr. 163, 376389 (2018). Recommended empirical relationships between flexural strength and compressive strength of plain concrete. The minimum 28-day characteristic compressive strength and flexural strength for low-volume roads are 30 MPa and 3.8 MPa, respectively. Build. PubMed Central 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. 1.1 This test method provides guidelines for testing the flexural strength of cured geosynthetic cementitious composite mat (GCCM) products in a three (3)-point bend apparatus. 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. 12, the SP has a medium impact on the predicted CS of SFRC. Date:11/1/2022, Publication:Structural Journal 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. Build. These equations are shown below. 4: Flexural Strength Test. 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. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. As shown in Fig. The best-fitting line in SVR is a hyperplane with the greatest number of points. J. Comput. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Intell. Build. Table 4 indicates the performance of ML models by various evaluation metrics. The testing of flexural strength in concrete is generally undertaken using a third point flexural strength test on a beam of concrete. Select Baseline, Compressive Strength, Flexural Strength, Split Tensile Strength, Modulus of Determine mathematic problem I need help determining a mathematic problem. In other words, the predicted CS decreases as the W/C ratio increases. Phys. What factors affect the concrete strength? Khademi et al.51 used MLR to predict the CS of NC and found that it cannot be considered an accurate model (with R2=0.518). Difference between flexural strength and compressive strength? 7). 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. Based upon the initial sensitivity analysis, the most influential parameters like water-to-cement (W/C) ratio and content of fine aggregates (FA) tend to decrease the CS of SFRC. 183, 283299 (2018). 230, 117021 (2020). Fax: 1.248.848.3701, ACI Middle East Regional Office Materials 8(4), 14421458 (2015). Shamsabadi, E. A. et al. 2020, 17 (2020). MLR is the most straightforward supervised ML algorithm for solving regression problems. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). This study modeled and predicted the CS of SFRC using several ML algorithms such as MLR, tree-based models, SVR, KNN, ANN, and CNN. volume13, Articlenumber:3646 (2023) However, the understanding of ISF's influence on the compressive strength (CS) behavior of . The reviewed contents include compressive strength, elastic modulus .
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