Metals | Free Full-Text | Flexural Behavior of Stainless Steel V Struct. 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. 12, the SP has a medium impact on the predicted CS of SFRC. 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. World Acad. Mater. Eur. Mater.
Investigation of Compressive Strength of Slag-based - ResearchGate The sugar industry produces a huge quantity of sugar cane bagasse ash in India. To obtain From Table 2, it can be observed that the ratio of flexural to compressive strength for all OPS concrete containing different aggregate saturation is in the range of 12.7% to 16.9% which is. Flexural strength is an indirect measure of the tensile strength of concrete. Graeff, . G., Pilakoutas, K., Lynsdale, C. & Neocleous, K. Corrosion durability of recycled steel fibre reinforced concrete. As can be seen in Table 4, the performance of implemented algorithms was evaluated using various metrics. Nguyen-Sy, T. et al. 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. As shown in Fig. Whereas, Koya et al.39 and Li et al.54 reported that SVR showed a high difference between experimental and anticipated values in predicting the CS of NC. The capabilities of ML algorithms were demonstrated through a sensitivity analysis and parametric analysis. 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. Hu, H., Papastergiou, P., Angelakopoulos, H., Guadagnini, M. & Pilakoutas, K. Mechanical properties of SFRC using blended manufactured and recycled tyre steel fibres. Please enter this 5 digit unlock code on the web page. Date:1/1/2023, Publication:Materials Journal
Date:2/1/2023, Publication:Special Publication
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. You've requested a page on a website (cloudflarepreview.com) that is on the Cloudflare network. In LOOCV, the number of folds is equal the number of instances in the dataset (n=176). A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures.
flexural strength and compressive strength Topic 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). 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. (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. 308, 125021 (2021). Eventually, 63 mixes were omitted and 176 mixes were selected for training the models in predicting the CS of SFRC. Young, B. 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. Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. Chen, H., Yang, J. Phys. Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Li, Y. et al. Appl. Where as, Flexural strength is the behaviour of a structure in direct bending (like in beams, slabs, etc.) 161, 141155 (2018). Compared to the previous ML algorithms (MLR and KNN), SVRs performance was better (R2=0.918, RMSE=5.397, MAE=4.559). The loss surfaces of multilayer networks. Gupta, S. Support vector machines based modelling of concrete strength. Appl. 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% . The use of an ANN algorithm (Fig. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. Mater. Sci Rep 13, 3646 (2023). 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. The brains functioning is utilized as a foundation for the development of ANN6. & Hawileh, R. A. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. Compressive strength estimation of steel-fiber-reinforced concrete and raw material interactions using advanced algorithms. Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. Fiber-reinforced concrete with low content of recycled steel fiber: Shear behaviour. Company Info.
DETERMINATION OF FLEXURAL STRENGTH OF CONCRETE - YouTube 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. 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. In recent years, CNN algorithm (Fig. The air content was found to be the most significant fresh field property and has a negative correlation with both the compressive and flexural strengths. Mech. The value for s then becomes: s = 0.09 (550) s = 49.5 psi CNN model is a new architecture for DL which is comprised of several layers that process and transform an input to produce an output. The main focus of this study is the development of a sustainable geomaterial composite with higher strength capabilities (compressive and flexural). Leone, M., Centonze, G., Colonna, D., Micelli, F. & Aiello, M. A. 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. This online unit converter allows quick and accurate conversion . 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. East. 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. Google Scholar. 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. Fluctuations of errors (Actual CSpredicted CS) for different algorithms. (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. October 18, 2022. Therefore, these results may have deficiencies. Flexural strength is measured by using concrete beams.
PDF Compressive strength to flexural strength conversion Flexural test evaluates the tensile strength of concrete indirectly. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. TStat and SI are the non-dimensional measures that capture uncertainty levels in the step of prediction. Compressive strength, Flexural strength, Regression Equation I. The CS of SFRC was predicted through various ML techniques as is described in section "Implemented algorithms". Parametric analysis between parameters and predicted CS in various algorithms. Case Stud. However, it is suggested that ANN can be utilized to predict the CS of SFRC. Southern California
CAS Based upon the results in this study, tree-based models performed worse than SVR in predicting the CS of SFRC. Mater.
PDF DESIGN'NOTE'7:Characteristic'compressive'strengthof'masonry PDF THE STATISTICAL ANALYSIS OF RELATION BETWEEN COMPRESSIVE AND - Sciendo Effects of steel fiber length and coarse aggregate maximum size on mechanical properties of steel fiber reinforced concrete. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). Mansour Ghalehnovi. To adjust the validation sets hyperparameters, random search and grid search algorithms were used. 33(3), 04019018 (2019). fck = Characteristic Concrete Compressive Strength (Cylinder). Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Meanwhile, the CS of SFRC could be enhanced by increasing the amount of superplasticizer (SP), fly ash, and cement (C). 260, 119757 (2020). The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. 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). Beyond limits of material strength, this can lead to a permanent shape change or structural failure. 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. Compressive Strength The main measure of the structural quality of concrete is its compressive strength. Compressive strength test was performed on cubic and cylindrical samples, having various sizes. MAPE is a scale-independent measure that is used to evaluate the accuracy of algorithms. Article Flexural strength of concrete = 0.7 . Struct. Constr. MathSciNet 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. Flexural strength calculator online - We'll provide some tips to help you select the best Flexural strength calculator online for your needs. 49, 20812089 (2022). A. 2020, 17 (2020). Build. The Offices 2 Building, One Central
Thank you for visiting nature.com. 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. Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength. the input values are weighted and summed using Eq. The rock strength determined by .