The maximum value of 25.50N/mm2 for the 5% replacement level is found suitable and recommended having attained a 28- day compressive strength of more than 25.0N/mm2. 7). 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. However, it is depicted that the weak correlation between the amount of ISF in the SFRC mix and the predicted CS. 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. This research leads to the following conclusions: Among the several ML techniques used in this research, CNN attained superior performance (R2=0.928, RMSE=5.043, MAE=3.833), followed by SVR (R2=0.918, RMSE=5.397, MAE=4.559). Struct. 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. Compressive strength result was inversely to crack resistance. Hence, various types of fibers are added to increase the tensile load-bearing capability of concrete. Eng. 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 . Supersedes April 19, 2022. & Gao, L. Influence of tire-recycled steel fibers on strength and flexural behavior of reinforced 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). Build. The ideal ratio of 20% HS, 2% steel . ACI members have itthey are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them. Constr. Midwest, Feedback via Email ANN model consists of neurons, weights, and activation functions18. Finally, results from the CNN technique were consistent with the previous studies, and CNN performed efficiently in predicting the CS of SFRC. The primary sensitivity analysis is conducted to determine the most important features. Table 3 provides the detailed information on the tuned hyperparameters of each model. Case Stud. Constr. Chen, H., Yang, J. Mater. The primary rationale for using an SVR is that the problem may not be separable linearly. PubMedGoogle Scholar. Therefore, as can be perceived from Fig. Ray ID: 7a2c96f4c9852428 Constr. Mater. Mech. The CivilWeb Compressive Strength to Flexural Conversion worksheet is included in the CivilWeb Flexural Strength spreadsheet suite. As can be seen in Fig. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Google Scholar, Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. Therefore, based on tree-based technique outcomes in predicting the CS of SFRC and compatibility with previous studies in using tree-based models for predicting the CS of various concrete types (SFRC and NC), it was concluded that tree-based models (especially XGB) showed good performance. Eng. 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. & Liu, J. Determine the available strength of the compression members shown. Tree-based models performed worse than SVR in predicting the CS of SFRC. CAS 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. 12, the W/C ratio is the parameter that intensively affects the predicted CS. Firstly, the compressive and splitting tensile strength of UHPC at low temperatures were determined through cube tests. 5(7), 113 (2021). 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. The feature importance of the ML algorithms was compared in Fig. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. 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 compressive strength and flexural strength were linearly fitted by SPSS, six regression models were obtained by linear fitting of compressive strength and flexural strength. 6(5), 1824 (2010). J. If a model's residualerror distribution is closer to the normal distribution, there is a greater likelihood of prediction mistakes occurring around the mean value6. The impact of the fly-ash on the predicted CS of SFRC can be seen in Fig. It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. 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). 27, 15591568 (2020). The compressive strength of the ordinary Portland cement / Pulverized Bentonitic Clay (PBC) generally decreases as the percentage of Pulverized Bentonitic Clay (PBC) content increases. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. The best-fitting line in SVR is a hyperplane with the greatest number of points. 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. In terms MBE, XGB achieved the minimum value of MBE, followed by ANN, SVR, and CNN. The compressive strength also decreased and the flexural strength increased when the EVA/cement ratio was increased. & Farasatpour, M. Steel fiber reinforced concrete: A review (2011). 3-point bending strength test for fine ceramics that partially complies with JIS R1601 (2008) [Testing method for flexural strength of fine ceramics at room temperature] (corresponding part only). 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. Based on the results obtained from the implementation of SVR in predicting the CS of SFRC and outcomes from previous studies in using the SVR to predict the CS of NC and SFRC, it was concluded that in some research, SVR demonstrated acceptable performance. Asadi et al.6 also reported that KNN performed poorly in predicting the CS of concrete containing waste marble powder. Add to Cart. 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. Nguyen-Sy, T. et al. Kabiru, O. Constr. PubMed Central The raw data is also available from the corresponding author on reasonable request. Whereas, it decreased by increasing the W/C ratio (R=0.786) followed by FA (R=0.521). INTRODUCTION The strength characteristic and economic advantages of fiber reinforced concrete far more appreciable compared to plain concrete. 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). The same results are also reported by Kang et al.18. The result of compressive strength for sample 3 was 105 Mpa, for sample 2 was 164 Mpa and for sample 1 was 320 Mpa. 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. In the meantime, to ensure continued support, we are displaying the site without styles KNN (R2=0.881, RMSE=6.477, MAE=4.648) showed lower accuracy compared with MLR in predicting the CS of SFRC. The correlation of all parameters with each other (pairwise correlation) can be seen in Fig. 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. 10l, a modification of fc geometric size slightly affects the rubber concrete compressive strength within the range [28.62; 26.73] MPa. Mater. Build. 183, 283299 (2018). The capabilities of ML algorithms were demonstrated through a sensitivity analysis and parametric analysis. 6(4) (2009). Eng. Khademi, F., Akbari, M. & Jamal, S. M. Prediction of compressive strength of concrete by data-driven models. 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. Build. 37(4), 33293346 (2021). What factors affect the concrete strength? & Arashpour, M. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Date:11/1/2022, Publication:IJCSM By submitting a comment you agree to abide by our Terms and Community Guidelines. As with any general correlations this should be used with caution. Behbahani, H., Nematollahi, B. 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 values of concrete design compressive strength f cd are given as . Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. 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. Experimental study on bond behavior in fiber-reinforced concrete with low content of recycled steel fiber. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014). Step 1: Estimate the "s" using s = 9 percent of the flexural strength; or, call several ready mix operators to determine the value. The proposed regression equations exhibit small errors when compared to the experimental results, which allow for efficient and accurate predictions of the flexural strength. Li, Y. et al. The stress block parameter 1 proposed by Mertol et al. Article 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. The flexural strength is the strength of a material in bending where the top surface is tension and the bottom surface. 115, 379388 (2019). A., Owolabi, T. O., Ssennoga, T. & Olatunji, S. O. Date:1/1/2023, Publication:Materials Journal The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. In contrast, KNN shows the worst performance among developed ML models in predicting the CS of SFRC. Article (2008) is set at a value of 0.85 for concrete strength of 69 MPa (10,000 psi) and lower. Due to its simplicity, this model has been used to predict the CS of concrete in numerous studies6,18,38,39. However, it is suggested that ANN can be utilized to predict the CS of SFRC. Cloudflare is currently unable to resolve your requested domain. 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. Figure10 also illustrates the normal distribution of the residual error of the suggested models for the prediction CS of SFRC. Adding hooked industrial steel fibers (ISF) to concrete boosts its tensile and flexural strength. As can be seen in Fig. 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. Commercial production of concrete with ordinary . Karahan, O., Tanyildizi, H. & Atis, C. D. An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash. Jamshidi Avanaki, M., Abedi, M., Hoseini, A. ADS 36(1), 305311 (2007). [1] (2.5): (2.5) B L r w x " where: f ct - splitting tensile strength [MPa], f' c - specified compressive strength of concrete [MPa]. Int. Also, C, DMAX, L/DISF, and CA have relatively little effect on the 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. The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. The sensitivity analysis demonstrated that, among different input variables, W/C ratio, fly ash, and SP had the most contributing effect on the CS behavior of SFRC, followed by the amount of ISF. Eur. Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. According to the presented literature, the scientific community is still uncertain about the CS behavior of SFRC. It was observed that ANN (with R2=0.896, RMSE=6.056, MAE=4.383) performed better than MLR, KNN, and tree-based models (except XGB) in predicting the CS of SFRC, but its accuracy was lower than the SVR and XGB (in both validation and test sets) techniques. Xiamen Hongcheng Insulating Material Co., Ltd. View Contact Details: Product List: The flexural strength is the higher of: f ctm,fl = (1.6 - h/1000)f ctm (6) or, f ctm,fl = f ctm where; h is the total member depth in mm Strength development of tensile strength Properties of steel fiber reinforced fly ash concrete. 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. Company Info. Dubai World Trade Center Complex Article A parametric analysis was carried out to determine how well the developed ML algorithms can predict the effect of various input parameters on the CS behavior of SFRC. Accordingly, many experimental studies were conducted to investigate the CS of SFRC. Second Floor, Office #207 Since you do not know the actual average strength, use the specified value for S'c (it will be fairly close). Article 175, 562569 (2018). J. Zhejiang Univ. This method has also been used in other research works like the one Khan et al.60 did. J. Enterp. The flexural loaddeflection responses, shown in Fig. World Acad. Constr. Build. In these cases, an SVR with a non-linear kernel (e.g., a radial basis function) is used. Dumping massive quantities of waste in a non-eco-friendly manner is a key concern for developing nations. Cem. Thank you for visiting nature.com. October 18, 2022. 2 illustrates the correlation between input parameters and the CS of SFRC. 4) has also been used to predict the CS of concrete41,42. It means that all ML models have been able to predict the effect of the fly-ash on the CS of SFRC. MATH Review of Materials used in Construction & Maintenance Projects. 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. 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 factors affecting the flexural strength of the concrete are generally similar to those affecting the compressive strength. 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. Source: Beeby and Narayanan [4]. Invalid Email Address. (b) Lay the specimen on its side as a beam with the faces of the units uppermost, and support the beam symmetrically on two straight steel bars placed so as to provide bearing under the centre of . 12. Today Proc. Mater. D7 FLEXURAL STRENGTH BY BEAM TEST D7.1 Test procedure The procedure for testing each specimen using the beam test method shall be as follows: (a) Determine the mass of the specimen to within 1 kg. Difference between flexural strength and compressive strength? East. 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. Limit the search results from the specified source. Constr. 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. Phone: 1.248.848.3800 Asadi et al.6 also used ANN in estimating the CS of NC containing waste marble powder (LOOCV was used to tune the hyperparameters) and reported that in the validation set, ANN was unable to reach an R2 as high as GB and XGB. All these results are consistent with the outcomes from sensitivity analysis, which is presented in Fig. Using CNN modelling, Chen et al.34 reported that CNN could show excellent performance in predicting the CS of the SFRS and NC. Constr. The forming embedding can obtain better flexural strength. J. Adhes. Build. In other words, in CS prediction of SFRC, all the mixes components must be presented (such as the developed ML algorithms in the current study). 11(4), 1687814019842423 (2019). For instance, numerous studies1,2,3,7,16,17 have been conducted for predicting the mechanical properties of normal concrete (NC). Build. As you can see the range is quite large and will not give a comfortable margin of certitude. A. Depending on how much coarse aggregate is used, these MR ranges are between 10% - 20% of compressive strength.
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