Phone: 1.248.848.3800, Home > Topics in Concrete > topicdetail, View all Documents on flexural strength and compressive strength , Publication:Materials Journal Hadzima-Nyarko, M., Nyarko, E. K., Lu, H. & Zhu, S. Machine learning approaches for estimation of compressive strength of concrete. 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). Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner Angle . 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. Characteristic compressive strength (MPa) Flexural Strength (MPa) 20: 3.13: 25: 3.50: 30: However, this parameter decreases linearly to reach a minimum value of 0.75 for concrete strength of 103 MPa (15,000 psi) or above. Please enter this 5 digit unlock code on the web page. Iex 2010 20 ft 21121 12 ft 8 ft fim S 12 x 35 A36 A=10.2 in, rx=4.72 in, ry=0.98 in b. Iex 34 ft 777777 nutt 2010 12 ft 12 ft W 10 ft 4000 fim MC 8 . Constr. Appl. Experimental Evaluation of Compressive and Flexural Strength of - IJERT ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Relationships between compressive and flexural strengths of - Springer The flexural properties and fracture performance of UHPC at low-temperature environment ( T = 20, 30, 60, 90, 120, and 160 C) were experimentally investigated in this paper. In this paper, two factors of width-to-height ratio and span-to-height ratio are considered and 10 side-pressure laminated bamboo beams are prepared and tested for flexural capacity to study the flexural performance when they are used as structural members. 115, 379388 (2019). The sensitivity analysis investigates the importance's magnitude of input parameters regarding the output parameter. Compos. 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). Therefore, based on the sensitivity analysis, the ML algorithms for predicting the CS of SFRC can be deemed reasonable. 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. Meanwhile, AdaBoost predicted the CS of SFRC with a broader range of errors. Effects of steel fiber content and type on static mechanical properties of UHPCC. 1.1 This test method provides guidelines for testing the flexural strength of cured geosynthetic cementitious composite mat (GCCM) products in a three (3)-point bend apparatus. Adv. 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). Lee, S.-C., Oh, J.-H. & Cho, J.-Y. 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. Google Scholar. Polymers 14(15), 3065 (2022). 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). InInternational Conference on Applied Computing to Support Industry: Innovation and Technology 323335 (Springer, 2019). 313, 125437 (2021). Mater. KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. Build. Ly, H.-B., Nguyen, T.-A. Compressive Strength Conversion Factors of Concrete as Affected by 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 correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. Flexural test evaluates the tensile strength of concrete indirectly. Cloudflare is currently unable to resolve your requested domain. This useful spreadsheet can be used to convert the results of the concrete cube test from compressive strength to . 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. Constr. Flexural strength of concrete = 0.7 . Constr. 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. 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. An. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. Eng. Figure8 depicts the variability of residual errors (actual CSpredicted CS) for all applied models. Moreover, the ReLU was used as the activation function for each convolutional layer and the Adam function was employed as an optimizer. Kabiru, O. Rathakrishnan, V., Beddu, S. & Ahmed, A. N. Comparison studies between machine learning optimisation technique on predicting concrete compressive strength (2021). 41(3), 246255 (2010). It is also observed that a lower flexural strength will be measured with larger beam specimens. Civ. The presented work uses Python programming language and the TensorFlow platform, as well as the Scikit-learn package. 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. 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. sqrt(fck) Where, fck is the characteristic compressive strength of concrete in MPa. Build. Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete. : Validation, WritingReview & Editing. Artif. 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. 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. Hameed, M. M. & AlOmar, M. K. Prediction of compressive strength of high-performance concrete: Hybrid artificial intelligence technique. Flexural strenght versus compressive strenght - Eng-Tips Forums & Aluko, O. J. Comput. Heliyon 5(1), e01115 (2019). Marcos-Meson, V. et al. 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). Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. SVR is considered as a supervised ML technique that predicts discrete values. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. The linear relationship between two variables is stronger if \(R\) is close to+1.00 or 1.00. Corrosion resistance of steel fibre reinforced concrete-A literature review. Normalization is a data preparation technique that converts the values in the dataset into a standard scale. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). 147, 286295 (2017). Date:9/30/2022, Publication:Materials Journal 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. ACI World Headquarters 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. Build. : Conceptualization, Methodology, Investigation, Data Curation, WritingOriginal Draft, Visualization; M.G. 324, 126592 (2022). Farmington Hills, MI Download Solution PDF Share on Whatsapp Latest MP Vyapam Sub Engineer Updates Last updated on Feb 21, 2023 MP Vyapam Sub Engineer (Civil) Revised Result Out on 21st Feb 2023! 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. Compressive strength, Flexural strength, Regression Equation I. Parametric analysis between parameters and predicted CS in various algorithms. The CivilWeb Compressive Strength to Flexural Strength Conversion spreadsheet is included in the CivilWeb Flexural Strength of Concrete suite of spreadsheets. Statistical characteristics of input parameters, including the minimum, maximum, average, and standard deviation (SD) values of each parameter, can be observed in Table 1. 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). Khan et al.55 also reported that RF (R2=0.96, RMSE=3.1) showed more acceptable outcomes than XGB and GB with, an R2 of 0.9 and 0.95 in the prediction CS of SFRC, respectively. & Maerefat, M. S. Effects of fiber volume fraction and aspect ratio on mechanical properties of hybrid steel fiber reinforced concrete. The focus of this paper is to present the data analysis used to correlate the point load test index (Is50) with the uniaxial compressive strength (UCS), and to propose appropriate Is50 to UCS conversion factors for different coal measure rocks. (2) as follows: In some studies34,35,36,37, several metrics were used to sufficiently evaluate the performed models and compare their robustness. Normalised and characteristic compressive strengths in Constr. Sci. Cem. Provided by the Springer Nature SharedIt content-sharing initiative. Hence, After each model training session, hold-out sample generalization may be poor, which reduces the R2 on the validation set 6. In contrast, KNN shows the worst performance among developed ML models in predicting the CS of SFRC. Consequently, it is frequently required to locate a local maximum near the global minimum59. For quality control purposes a reliable compressive strength to flexural strength conversion is required in order to ensure that the concrete satisfies the specification. Constr. As you can see the range is quite large and will not give a comfortable margin of certitude. Low Cost Pultruded Profiles High Compressive Strength Dogbone Corner It tests the ability of unreinforced concrete beam or slab to withstand failure in bending. & Hawileh, R. A. 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. 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. 16, e01046 (2022). PubMedGoogle Scholar. & Tran, V. Q. For CEM 1 type cements a very general relationship has often been applied; This provides only the most basic correlation between flexural strength and compressive strength and should not be used for design purposes. 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. Buy now for only 5. 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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