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. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. Mater. All three proposed ML algorithms demonstrate superior performance in predicting the correlation between the amount of fly-ash and the predicted CS of SFRC. Deng, F. et al. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. Constr. In addition, the studies based on ML techniques that have been done to predict the CS of SFRC are limited since it is difficult to collect inclusive experimental data to develop models regarding all contributing features (such as the properties of fibers, aggregates, and admixtures). 33(3), 04019018 (2019). Recommended empirical relationships between flexural strength and compressive strength of plain concrete. Cem. Development of deep neural network model to predict the compressive strength of rubber concrete. However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. 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. 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. Abuodeh, O. R., Abdalla, J. The site owner may have set restrictions that prevent you from accessing the site. 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. Constr. Constr. A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. & Aluko, O. 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. Date:1/1/2023, Publication:Materials Journal Khan, M. A. et al. Today Commun. It was observed that overall, the ANN model outperformed the genetic algorithm in predicting the CS of SFRC. Polymers 14(15), 3065 (2022). Add to Cart. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Mater. In contrast, the splitting tensile strength was decreased by only 26%, as illustrated in Figure 3C. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Article ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. 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. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. ; The values of concrete design compressive strength f cd are given as . Therefore, as can be perceived from Fig. 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. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. Constr. Get the most important science stories of the day, free in your inbox. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. Compressive strength result was inversely to crack resistance. The performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. 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 . Further information can be found in our Compressive Strength of Concrete post. 26(7), 16891697 (2013). Investigation of mechanical characteristics and specimen size effect of steel fibers reinforced concrete. How is the required strength selected, measured, and obtained? Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . The user accepts ALL responsibility for decisions made as a result of the use of this design tool. 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. Flexural strength is however much more dependant on the type and shape of the aggregates used. 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 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. PubMed 27, 102278 (2021). 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). 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. Constr. Buy now for only 5. Source: Beeby and Narayanan [4]. Phys. Strength Converter; Concrete Temperature Calculator; Westergaard; Maximum Joint Spacing Calculator; BCOA Thickness Designer; Gradation Analyzer; Apple iOS Apps. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. J. Devries. Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. 12. A. The flexural loaddeflection responses, shown in Fig. 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. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. Constr. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. Date:9/30/2022, Publication:Materials Journal Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Values in inch-pound units are in parentheses for information. By submitting a comment you agree to abide by our Terms and Community Guidelines. Eur. 301, 124081 (2021). 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). In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. PubMedGoogle Scholar. Build. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. A comparative investigation using machine learning methods for concrete compressive strength estimation. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. 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. Constr. Li, Y. et al. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. Infrastructure Research Institute | Infrastructure Research Institute Build. CAS 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. ANN model consists of neurons, weights, and activation functions18. Please enter this 5 digit unlock code on the web page. Bending occurs due to development of tensile force on tension side of the structure. 1 and 2. Struct. 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. The reason is the cutting embedding destroys the continuity of carbon . Correspondence to It is also observed that a lower flexural strength will be measured with larger beam specimens. 11(4), 1687814019842423 (2019). 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. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. Mech. 3) was used to validate the data and adjust the hyperparameters. Chen, H., Yang, J. 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. To develop this composite, sugarcane bagasse ash (SA), glass . Privacy Policy | Terms of Use Civ. J. Zhejiang Univ. Invalid Email Address The raw data is also available from the corresponding author on reasonable request. As can be seen in Fig. It concluded that the addition of banana trunk fiber could reduce compressive strength, but could raise the concrete ability in crack resistance Keywords: Concrete . ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. 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. Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). Cem. In many cases it is necessary to complete a compressive strength to flexural strength conversion. The flexural response showed a similar trend in the individual and combined effect of MWCNT and GNP, which increased the flexural strength and flexural modulus in all GE composites, as shown in Figure 11. In todays market, it is imperative to be knowledgeable and have an edge over the competition. You are using a browser version with limited support for CSS. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. \(R\) shows the direction and strength of a two-variable relationship. 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% . 313, 125437 (2021). Build. This is much more difficult and less accurate than the equivalent concrete cube test, which is why it is common to test the compressive strength and then convert to flexural strength when checking the concrete's compliance with the specification. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. SVR model (as can be seen in Fig. 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. 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. 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. 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). 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. Buildings 11(4), 158 (2021). Build. Build. 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. Constr. 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. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. (4). Second Floor, Office #207 Geopolymer recycled aggregate concrete (GPRAC) is a new type of green material with broad application prospects by replacing ordinary Portland cement with geopolymer and natural aggregates with recycled aggregates. Use of this design tool implies acceptance of the terms of use. 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. Constr. 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.