flexural strength to compressive strength converterflexural strength to compressive strength converter

On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. Importance of flexural strength of . As shown in Fig. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Constr. These are taken from the work of Croney & Croney. Adam was selected as the optimizer function with a learning rate of 0.01. The ideal ratio of 20% HS, 2% steel . Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. 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. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. 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. & Lan, X. 48331-3439 USA Date:1/1/2023, Publication:Materials Journal Dubai, UAE According to the results obtained from parametric analysis, among the developed models, SVR can accurately predict the impact of W/C ratio, SP, and fly-ash on the CS of SFRC, followed by CNN. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Constr. Ati, C. D. & Karahan, O. Google Scholar. 26(7), 16891697 (2013). A calculator tool is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets with this equation converted to metric units. & Xargay, H. An experimental study on the post-cracking behaviour of Hybrid Industrial/Recycled Steel Fibre-Reinforced Concrete. Corrosion resistance of steel fibre reinforced concrete-A literature review. Various orders of marked and unmarked errors in predictions are demonstrated by MSE, RMSE, MAE, and MBE6. In other words, in CS prediction of SFRC, all the mixes components must be presented (such as the developed ML algorithms in the current study). (3): where \(\hat{y}\), \(x_{n}\), and \(\alpha\) are the dependent parameter, independent parameter, and bias, respectively18. 2020, 17 (2020). Further information on the elasticity of concrete is included in our Modulus of Elasticity of Concrete post. From the open literature, a dataset was collected that included 176 different concrete compressive test sets. These equations are shown below. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. Constr. . 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. Tanyildizi, H. Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine. In Artificial Intelligence and Statistics 192204. 3) was used to validate the data and adjust the hyperparameters. The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. Mech. Fax: 1.248.848.3701, ACI Middle East Regional Office de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Compressive strength test was performed on cubic and cylindrical samples, having various sizes. 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. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Build. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. It is equal to or slightly larger than the failure stress in tension. Where the modulus of elasticity of the concrete is required to complete a design there is a correlation equation relating flexural strength with the modulus of elasticity, shown below. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. 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. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. 49, 554563 (2013). To obtain Hu, H., Papastergiou, P., Angelakopoulos, H., Guadagnini, M. & Pilakoutas, K. Mechanical properties of SFRC using blended manufactured and recycled tyre steel fibres. 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. 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 . Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. This is particularly common in the design and specification of concrete pavements where flexural strengths are critical while compressive strengths are often specified. World Acad. Explain mathematic . Article Build. Mech. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Mater. In many cases it is necessary to complete a compressive strength to flexural strength conversion. ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. 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% . Eng. Consequently, it is frequently required to locate a local maximum near the global minimum59. 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). Accordingly, 176 sets of data are collected from different journals and conference papers. Han, J., Zhao, M., Chen, J. Struct. Flexural strength is measured by using concrete beams. Table 3 shows the results of using a grid and a random search to tune the other hyperparameters. It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. Setti, F., Ezziane, K. & Setti, B. Iex 2010 20 ft 21121 12 ft 8 ft fim S 12 x 35 A36 A=10.2 in, rx=4.72 in, ry=0.98 in b. Iex 34 ft 777777 nutt 2010 12 ft 12 ft W 10 ft 4000 fim MC 8 . 260, 119757 (2020). In contrast, the XGB and KNN had the most considerable fluctuation rate. MLR is the most straightforward supervised ML algorithm for solving regression problems. Flexural strength, also known as modulus of rupture, bend strength, or fracture strength, a mechanical parameter for brittle material, is defined as a materi. 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. Article 73, 771780 (2014). Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. 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. Development of deep neural network model to predict the compressive strength of rubber concrete. In recent years, CNN algorithm (Fig. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. 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. The site owner may have set restrictions that prevent you from accessing the site. 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. Based on the developed models to predict the CS of SFRC (Fig. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. : Validation, WritingReview & Editing. Also, the characteristics of ISF (VISF, L/DISF) have a minor effect on the CS of SFRC. Date:3/3/2023, Publication:Materials Journal where \(x_{i} ,w_{ij} ,net_{j} ,\) and \(b\) are the input values, the weight of each signal, the weighted sum of the \(j{\text{th}}\) neuron, and bias, respectively18. All data generated or analyzed during this study are included in this published article. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. 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. Mater. This useful spreadsheet can be used to convert concrete cube test results from compressive strength to flexural strength to check whether the concrete used satisfies the specification. 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! 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. Mater. Plus 135(8), 682 (2020). Therefore, the data needs to be normalized to avoid the dominance effect caused by magnitude differences among input parameters34. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. The factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. 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. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. This index can be used to estimate other rock strength parameters. Constr. 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%. Buildings 11(4), 158 (2021). Build. 33(3), 04019018 (2019). 12 illustrates the impact of SP on the predicted CS of SFRC. 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 . Date:9/1/2022, Search all Articles on flexural strength and compressive strength », Publication:Concrete International Ray ID: 7a2c96f4c9852428 Abuodeh, O. R., Abdalla, J. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. & Chen, X. 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. 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. Mater. 230, 117021 (2020). East. Civ. October 18, 2022. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. Therefore, as can be perceived from Fig. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. The implemented procedure was repeated for other parameters as well, considering the three best-performed algorithms, which are SVR, XGB, and ANN. . J. Devries. XGB makes GB more regular and controls overfitting by increasing the generalizability6. 11. This online unit converter allows quick and accurate conversion . Jamshidi Avanaki, M., Abedi, M., Hoseini, A. Adv. 37(4), 33293346 (2021). 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). By submitting a comment you agree to abide by our Terms and Community Guidelines. Build. Difference between flexural strength and compressive strength? As is reported by Kang et al.18, among implemented tree-based models, XGB performed superiorly in predicting the CS of SFRC. Constr. Limit the search results with the specified tags. Terms of Use The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Flexural strength is however much more dependant on the type and shape of the aggregates used. 324, 126592 (2022). Constr. Sci. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. This method has also been used in other research works like the one Khan et al.60 did. 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. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. Struct. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. Mater. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. SI is a standard error measurement, whose smaller values indicate superior model performance. Dubai World Trade Center Complex MathSciNet In terms of comparing ML algorithms with regard to IQR index, CNN modelling showed an error dispersion about 31% lower than SVR technique. Res. 232, 117266 (2020). Mater. Table 3 displays the modified hyperparameters of each convolutional, flatten, hidden, and pooling layer, including kernel and filter size and learning rate. PubMed Central Civ. Flexural strength of concrete = 0.7 . 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. Cloudflare is currently unable to resolve your requested domain. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. 49, 20812089 (2022). However, the understanding of ISFs influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. 45(4), 609622 (2012). Scientific Reports (Sci Rep) Martinelli, E., Caggiano, A. Limit the search results modified within the specified time. J. Sci. ANN can be used to model complicated patterns and predict problems. In contrast, KNN shows the worst performance among developed ML models in predicting the CS of SFRC. 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). Farmington Hills, MI ADS 267, 113917 (2021). : Investigation, Conceptualization, Methodology, Data Curation, Formal analysis, WritingOriginal Draft; N.R. 163, 376389 (2018). 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. 36(1), 305311 (2007). Polymers 14(15), 3065 (2022). Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). The best-fitting line in SVR is a hyperplane with the greatest number of points. Date:10/1/2022, Publication:Special Publication Moreover, some others were omitted because of lacking the information of mixing components (such as FA, SP, etc.). 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. It is seen that all mixes, except mix C10 and B4C6, comply with the requirement of the compressive strength and flexural strength from application point of view in the construction of rigid pavement. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. MathSciNet J. Adhes. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. 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. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). New Approaches Civ. Google Scholar. 308, 125021 (2021). Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. The feature importance of the ML algorithms was compared in Fig. 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. Intersect. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. 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. ISSN 2045-2322 (online). 41(3), 246255 (2010). In todays market, it is imperative to be knowledgeable and have an edge over the competition. Eng. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). Build. 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 . Appl. 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. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. & Tran, V. Q. Unquestionably, one of the barriers preventing the use of fibers in structural applications has been the difficulty in calculating the FRC properties (especially CS behavior) that should be included in current design techniques10. 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. For example compressive strength of M20concrete is 20MPa. Case Stud. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. 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. 313, 125437 (2021). Finally, the model is created by assigning the new data points to the category with the most neighbors. Article 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. Artif. 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. Today Commun. fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . 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. Flexural strength, also known as modulus of rupture, or bend strength, or transverse rupture strengthis a material property, defined as the stressin a material just before it yieldsin a flexure test. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Mater. Commercial production of concrete with ordinary . Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. 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). In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). Mater. This algorithm first calculates K neighbors euclidean distance. The same results are also reported by Kang et al.18. 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. 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. Mater. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Khan, K. et al. Chen, H., Yang, J. It was observed that overall, the ANN model outperformed the genetic algorithm in predicting the CS of SFRC. More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. 94, 290298 (2015). Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. http://creativecommons.org/licenses/by/4.0/. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. Mater. Res. However, it is depicted that the weak correlation between the amount of ISF in the SFRC mix and the predicted CS. Shade denotes change from the previous issue. 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. SVR is considered as a supervised ML technique that predicts discrete values. Also, Fig. In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. 175, 562569 (2018). 95, 106552 (2020). 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. In contrast, KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed the weakest performance in predicting the CS of SFRC. Southern California Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. 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. Mater. 2(2), 4964 (2018). Sci. 2018, 110 (2018). Google Scholar. Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete. It is essential to note that, normalization generally speeds up learning and leads to faster convergence. Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete. 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. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. consequently, the maxmin normalization method is adopted to reshape all datasets to a range from \(0\) to \(1\) using Eq. Google Scholar. 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). 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.
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