Date:7/1/2022, Publication:Special Publication 248, 118676 (2020). Materials 15(12), 4209 (2022). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Regarding Fig. Google Scholar. Materials IM Index. Mater. 27, 15591568 (2020). Mater. Deng, F. et al. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. The primary sensitivity analysis is conducted to determine the most important features. 12). 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. Chen, H., Yang, J. Mater. 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). Eng. Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Table 4 indicates the performance of ML models by various evaluation metrics. Mater. 26(7), 16891697 (2013). Struct. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. Technol. Build. MATH & Hawileh, R. A. 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. However, regarding the Tstat, the outcomes show that CNN performance was approximately 58% lower than XGB. Date:1/1/2023, Publication:Materials 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. Huang, J., Liew, J. Erdal, H. I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. Provided by the Springer Nature SharedIt content-sharing initiative. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. 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. ISSN 2045-2322 (online). Polymers 14(15), 3065 (2022). & Chen, X. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Further details on strength testing of concrete can be found in our Concrete Cube Test and Flexural Test posts. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. 1. Mater. According to Table 1, input parameters do not have a similar scale. 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 . PubMed Mech. Get the most important science stories of the day, free in your inbox. J. Adhes. For design of building members an estimate of the MR is obtained by: , where 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. These are taken from the work of Croney & Croney. 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). 3) was used to validate the data and adjust the hyperparameters. 12, the W/C ratio is the parameter that intensively affects the predicted CS. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Characteristic compressive strength (MPa) Flexural Strength (MPa) 20: 3.13: 25: 3.50: 30: : New insights from statistical analysis and machine learning methods. The rock strength determined by . J Civ Eng 5(2), 1623 (2015). Flexural strength is measured by using concrete beams. Google Scholar. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. How is the required strength selected, measured, and obtained? Company Info. The results of flexural test on concrete expressed as a modulus of rupture which denotes as ( MR) in MPa or psi. Date:11/1/2022, Publication:Structural Journal To avoid overfitting, the dataset was split into train and test sets, with 80% of the data used for training the model and 20% for testing. The formula to calculate compressive strength is F = P/A, where: F=The compressive strength (MPa) P=Maximum load (or load until failure) to the material (N) A=A cross-section of the area of the material resisting the load (mm2) Introduction Of Compressive Strength Build. Build. ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) 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. 27, 102278 (2021). Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). 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. In this regard, developing the data-driven models to predict the CS of SFRC is a comparatively novel approach. 11. 38800 Country Club Dr. ACI World Headquarters Kabiru, O. 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. 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. As per IS 456 2000, the flexural strength of the concrete can be computed by the characteristic compressive strength of the concrete. It is worth noticing that after converting the unit from psi into MPa, the equation changes into Eq. The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". & Lan, X. However, it is worth noting that their performance in predicting the CS of SFRC was superior to that of KNN and MLR. Article 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. The KNN method is a simple supervised ML technique that can be utilized in order to solve both classification and regression problems. Build. Mater. 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). Constr. J. Devries. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. Constr. 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). Mater. For materials that deform significantly but do not break, the load at yield, typically measured at 5% deformation/strain of the outer surface, is reported as the flexural strength or flexural yield strength. 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. 7). It is a measure of the maximum stress on the tension face of an unreinforced concrete beam or slab at the point of. 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. 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. 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). World Acad. : Validation, WritingReview & Editing. 11(4), 1687814019842423 (2019). Determine the available strength of the compression members shown. Today Proc. Build. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). 73, 771780 (2014). Dubai, UAE 3.4 Flexural Strength 3.5 Tensile Strength 3.6 Shear, Torsion and Combined Stresses 3.7 Relationship of Test Strength to the Structure MEASUREMENT OF STRENGTH . 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). ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. As you can see the range is quite large and will not give a comfortable margin of certitude. Moreover, Nguyen-Sy et al.56 and Rathakrishnan et al.57, after implementing the XGB, noted that the XGB was the best model for predicting the CS of NC. Mater. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. Setti, F., Ezziane, K. & Setti, B. Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. 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. October 18, 2022. It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. 161, 141155 (2018). The same results are also reported by Kang et al.18. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. 324, 126592 (2022). Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. Article Among these techniques, AdaBoost is the most straightforward boosting algorithm that is based on the idea that a very accurate prediction rule can be made by combining a lot of less accurate regulations43. Flexural strength of concrete = 0.7 . 232, 117266 (2020). For the prediction of CS behavior of NC, Kabirvu et al.5 implemented SVR, and observed that SVR showed high accuracy (with R2=0.97). Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. Conversion factors of different specimens against cross sectional area of the same specimens were also plotted and regression analyses The experimental results show that in the case of [0/90/0] 2 ply, the bending strength of the structure increases by 2.79% in the forming embedding mode, while it decreases by 9.81% in the cutting embedding mode. 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. 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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. The impact of the fly-ash on the predicted CS of SFRC can be seen in Fig. Mater. In contrast, others reported that SVR showed weak performance in predicting the CS of concrete. 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). Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). In contrast, the XGB and KNN had the most considerable fluctuation rate. Also, the CS of SFRC was considered as the only output parameter. 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. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. J. Enterp. Appl. This is particularly common in the design and specification of concrete pavements where flexural strengths are critical while compressive strengths are often specified. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. Email Address is required Adv. 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. Al-Abdaly, N. M., Al-Taai, S. R., Imran, H. & Ibrahim, M. Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation. Kandiri, A., Golafshani, E. M. & Behnood, A. Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm.

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