A. In addition, CNN achieved about 28% lower residual error fluctuation than SVR. : Validation, WritingReview & Editing. Farmington Hills, MI
& Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. 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). Al-Abdaly et al.50 also reported that RF (R2=0.88, RMSE=5.66, MAE=3.8) performed better than MLR (R2=0.64, RMSE=8.68, MAE=5.66) in predicting the CS of SFRC. Distributions of errors in MPa (Actual CSPredicted CS) for several methods. Table 3 provides the detailed information on the tuned hyperparameters of each model. Google Scholar. PMLR (2015). Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. 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. Several statistical parameters are also used as metrics to evaluate the performance of implemented models, such as coefficient of determination (R2), mean absolute error (MAE), and mean of squared error (MSE). 23(1), 392399 (2009). Huang, J., Liew, J. & Liew, K. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. Until now, fibers have been used mainly to improve the behavior of structural elements for serviceability purposes. 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. Internet Explorer). This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . 73, 771780 (2014). In the current research, tree-based models (GB, XGB, RF, and AdaBoost) were used to predict the CS of SFRC. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Tree-based models performed worse than SVR in predicting the CS of SFRC. Similar equations can used to allow for angular crushed rock aggregates or rounded marine aggregates as shown below. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. As you can see the range is quite large and will not give a comfortable margin of certitude. 183, 283299 (2018). 12. 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. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). Build. \(R\) shows the direction and strength of a two-variable relationship. Infrastructure Research Institute | Infrastructure Research Institute Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Lee, S.-C., Oh, J.-H. & Cho, J.-Y. Build. 5) as a powerful tool for estimating the CS of concrete is now well-known6,38,44,45. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. In todays market, it is imperative to be knowledgeable and have an edge over the competition. Mater. 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. Eur. Song, H. et al. Constr. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. One of the drawbacks of concrete as a fragile material is its low tensile strength and strain capacity. 2(2), 4964 (2018). Tensile strength - UHPC has a tensile strength over 1,200 psi, while traditional concrete typically measures between 300 and 700 psi. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Adv. The user accepts ALL responsibility for decisions made as a result of the use of this design tool. 115, 379388 (2019). Please enter search criteria and search again, Informational Resources on flexural strength and compressive strength, Web Pages on flexural strength and compressive strength, FREQUENTLY ASKED QUESTIONS ON FLEXURAL STRENGTH AND COMPRESSIVE STRENGTH. It uses two commonly used general correlations to convert concrete compressive and flexural strength. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik 3752 (2013). Get the most important science stories of the day, free in your inbox. 48331-3439 USA
301, 124081 (2021). If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. 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. In recent years, CNN algorithm (Fig. 27, 15591568 (2020). Case Stud. Eng. PubMed Central Google Scholar. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. Li, Y. et al. Eng. Struct. 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. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. Han et al.11 reported that the length of the ISF (LISF) has an insignificant effect on the CS of SFRC. 3) was used to validate the data and adjust the hyperparameters. 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. & Kim, H. Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. The reason is the cutting embedding destroys the continuity of carbon . & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). 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). Polymers 14(15), 3065 (2022). As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Adam was selected as the optimizer function with a learning rate of 0.01. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . 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. Adv. Shade denotes change from the previous issue. Khan, K. et al. Date:11/1/2022, Publication:Structural Journal
The forming embedding can obtain better flexural strength. Deng, F. et al. The primary rationale for using an SVR is that the problem may not be separable linearly. Mater. Knag et al.18 reported that silica fume, W/C ratio, and DMAX are the most influential parameters that predict the CS of SFRC. 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. Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. Gupta, S. Support vector machines based modelling of concrete strength. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. 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. A calculator tool to apply either of these methods is included in the CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet. However, the addition of ISF into the concrete and producing the SFRC may also provide additional strength capacity or act as the primary reinforcement in structural elements. Importance of flexural strength of . The use of an ANN algorithm (Fig. Supersedes April 19, 2022. Buy now for only 5. Note that for some low strength units the characteristic compressive strength of the masonry can be slightly higher than the unit strength. 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 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. However, their performance in predicting the CS of SFRC was superior to that of KNN and MLR. SVR is considered as a supervised ML technique that predicts discrete values. Mater. 11. Nguyen-Sy, T. et al. Mater. Shamsabadi, E. A. et al. PubMed Central Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. Where an accurate elasticity value is required this should be determined from testing. ML is a computational technique destined to simulate human intelligence and speed up the computing procedure by means of continuous learning and evolution. In Artificial Intelligence and Statistics 192204. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Sci Rep 13, 3646 (2023). 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. Depending on the test method used to determine the flex strength (center or third point loading) an ESTIMATE of f'c would be obtained by multiplying the flex by 4.5 to 6. 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. The compressive strength also decreased and the flexural strength increased when the EVA/cement ratio was increased. However, the understanding of ISFs influence on the compressive strength (CS) behavior of concrete is still questioned by the scientific society. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. 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. Mater. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. Build. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. Appl. Source: Beeby and Narayanan [4]. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. Li et al.54 noted that the CS of SFRC increased with increasing amounts of C and silica fume, and decreased with increasing amounts of water and SP. To try out a fully functional free trail version of this software, please enter your email address below to sign up to our newsletter. Provided by the Springer Nature SharedIt content-sharing initiative. Phone: +971.4.516.3208 & 3209, ACI Resource Center
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. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. Corrosion resistance of steel fibre reinforced concrete-A literature review. Review of Materials used in Construction & Maintenance Projects. Kang et al.18 observed that KNN predicted the CS of SFRC with a great difference between actual and predicted values. Today Proc. Build. Also, Fig. To adjust the validation sets hyperparameters, random search and grid search algorithms were used. Adv. 230, 117021 (2020). 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. Date:2/1/2023, Publication:Special Publication
The value for s then becomes: s = 0.09 (550) s = 49.5 psi 12). Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Article Hence, various types of fibers are added to increase the tensile load-bearing capability of concrete. You do not have access to www.concreteconstruction.net. 12, the SP has a medium impact on the predicted CS of SFRC. Sci. From the open literature, a dataset was collected that included 176 different concrete compressive test sets. . Fluctuations of errors (Actual CSpredicted CS) for different algorithms. | Copyright ACPA, 2012, American Concrete Pavement Association (Home). It uses two general correlations commonly used to convert concrete compression and floral strength. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. ; The values of concrete design compressive strength f cd are given as . 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. For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). ; Compressive Strength - UHPC's advanced compressive strength is particularly significant when . http://creativecommons.org/licenses/by/4.0/. 5(7), 113 (2021). The current 4th edition of TR 34 includes the same method of correlation as BS EN 1992. Civ. Fax: 1.248.848.3701, ACI Middle East Regional Office
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. 4: Flexural Strength Test. As can be seen in Fig. Constr. A 9(11), 15141523 (2008). Determine the available strength of the compression members shown. 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 . Build. Moreover, GB is an AdaBoost development model, a meta-estimator that consists of many sequential decision trees that uses a step-by-step method to build an additive model6. 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. Res. Civ. STANDARDS, PRACTICES and MANUALS ON FLEXURAL STRENGTH AND COMPRESSIVE STRENGTH ACI CODE-350-20: Code Requirements for Environmental Engineering Concrete Structures (ACI 350-20) and Commentary (ACI 350R-20) ACI PRC-441.1-18: Report on Equivalent Rectangular Concrete Stress Block and Transverse Reinforcement for High-Strength Concrete Columns Int. 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). 12), C, DMAX, L/DISF, and CA have relatively little effect on the CS. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. In the meantime, to ensure continued support, we are displaying the site without styles PubMed Central These are taken from the work of Croney & Croney. (3): where \(\hat{y}\), \(x_{n}\), and \(\alpha\) are the dependent parameter, independent parameter, and bias, respectively18. A good rule-of-thumb (as used in the ACI Code) is: The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. Technol. 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. Date:1/1/2023, Publication:Materials Journal
fck = Characteristic Concrete Compressive Strength (Cylinder) h = Depth of Slab More specifically, numerous studies have been conducted to predict the properties of concrete1,2,3,4,5,6,7. 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). The feature importance of the ML algorithms was compared in Fig. 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. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Effects of steel fiber content and type on static mechanical properties of UHPCC. Compos. Date:10/1/2022, Publication:Special Publication
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. Constr. 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. Limit the search results from the specified source. Cloudflare is currently unable to resolve your requested domain. The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements.