6(4) (2009). Constr. 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. Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. : New insights from statistical analysis and machine learning methods. To adjust the validation sets hyperparameters, random search and grid search algorithms were used. On the other hand, MLR shows the highest MAE in predicting the CS of SFRC. [1] 34(13), 14261441 (2020). It uses two commonly used general correlations to convert concrete compressive and flexural strength. For example compressive strength of M20concrete is 20MPa. The flexural strengths of all the laminates tested are significantly higher than their tensile strengths, and are also higher than or similar to their compressive strengths. The primary rationale for using an SVR is that the problem may not be separable linearly. Marcos-Meson, V. et al. A. 49, 554563 (2013). Thank you for visiting nature.com. This method has also been used in other research works like the one Khan et al.60 did. Struct. Where an accurate elasticity value is required this should be determined from testing. 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: 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. To obtain PubMedGoogle Scholar. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. 12. Build. Use of this design tool implies acceptance of the terms of use. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. 12, the W/C ratio is the parameter that intensively affects the predicted CS. Second Floor, Office #207
Eur. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. Mater. R2 is a metric that demonstrates how well a model predicts the value of a dependent variable and how well the model fits the data. 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). 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. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. October 18, 2022. 118 (2021). Eur. Constr. SVR is considered as a supervised ML technique that predicts discrete values. 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. 11. 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 . Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. Compressive strength prediction of recycled concrete based on deep learning. Struct. Adv. SVR model (as can be seen in Fig. The reason is the cutting embedding destroys the continuity of carbon . Average 28-day flexural strength of at least 4.5 MPa (650 psi) Coarse aggregate: . CNN model is a new architecture for DL which is comprised of several layers that process and transform an input to produce an output. A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? These equations are shown below. 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This is particularly common in the design and specification of concrete pavements where flexural strengths are critical while compressive strengths are often specified. Limit the search results with the specified tags. 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 sugar industry produces a huge quantity of sugar cane bagasse ash in India. CAS 36(1), 305311 (2007). Schapire, R. E. Explaining adaboost. Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. & Arashpour, M. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Constr. 27, 102278 (2021). SI is a standard error measurement, whose smaller values indicate superior model performance. Article PubMed Central 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. J. Google Scholar. 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. 267, 113917 (2021). Mater. The predicted values were compared with the actual values to demonstrate the feasibility of ML algorithms (Fig. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. Asadi et al.6 also reported that KNN performed poorly in predicting the CS of concrete containing waste marble powder. 115, 379388 (2019). 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 . Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Adv. More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. Figure No. Mater. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. The stress block parameter 1 proposed by Mertol et al. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. By submitting a comment you agree to abide by our Terms and Community Guidelines. (4). The CivilWeb Compressive Strength to Flexural Conversion worksheet is included in the CivilWeb Flexural Strength spreadsheet suite. 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. Please enter this 5 digit unlock code on the web page. Ren, G., Wu, H., Fang, Q. Constr. XGB makes GB more regular and controls overfitting by increasing the generalizability6. Article \(R\) shows the direction and strength of a two-variable relationship. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. Khan, M. A. et al. Eng. Invalid Email Address
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. 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. Polymers 14(15), 3065 (2022). 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. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Mater. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. 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. the input values are weighted and summed using Eq. Privacy Policy | Terms of Use
Equation(1) is the covariance between two variables (\(COV_{XY}\)) divided by their standard deviations (\(\sigma_{X}\), \(\sigma_{Y}\)). Values in inch-pound units are in parentheses for information. Kang, M.-C., Yoo, D.-Y. http://creativecommons.org/licenses/by/4.0/. Use AISC to compute both the ff: 1. design strength for LRFD 2. allowable strength for ASD. Development of deep neural network model to predict the compressive strength of rubber concrete. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Further information on this is included in our Flexural Strength of Concrete post. 1 and 2. The value of the multiplier can range between 0.58 and 0.91 depending on the aggregate type and other mix properties. Select Baseline, Compressive Strength, Flexural Strength, Split Tensile Strength, Modulus of Determine mathematic problem I need help determining a mathematic problem. J. Effects of steel fiber content and type on static mechanical properties of UHPCC. 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. Struct. 7). J. Enterp. Ly, H.-B., Nguyen, T.-A. PubMed Central 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. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. Flexural test evaluates the tensile strength of concrete indirectly. Also, C, DMAX, L/DISF, and CA have relatively little effect on the CS of SFRC. 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. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. Civ. 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. Build. Cite this article. The compressive strength of the ordinary Portland cement / Pulverized Bentonitic Clay (PBC) generally decreases as the percentage of Pulverized Bentonitic Clay (PBC) content increases. : Investigation, Conceptualization, Methodology, Data Curation, Formal analysis, WritingOriginal Draft; N.R. Flexural Strengthperpendicular: 650Mpa: Arc Resistance: 180 sec: Contact Now. 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. 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. Build. 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. 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). Al-Abdaly et al.50 reported that MLR algorithm (with R2=0.64, RMSE=8.68, MAE=5.66) performed poorly in predicting the CS behavior of SFRC. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. 12. As shown in Fig. Build. MATH 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. 163, 826839 (2018). 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 performance of the XGB algorithm is also reasonable by resulting in a value of R=0.867 for correlation. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. New Approaches Civ. I Manag. Today Proc. 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). Beyond limits of material strength, this can lead to a permanent shape change or structural failure. Transcribed Image Text: SITUATION A. Olivito, R. & Zuccarello, F. An experimental study on the tensile strength of steel fiber reinforced concrete. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. Build. Constr.
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