Materials 13(5), 1072 (2020). The two methods agree reasonably well for concrete strengths and slab thicknesses typically used for concrete pavements. Build. The CivilWeb Flexural Strength of Concrete suite of spreadsheets is available for purchase at the bottom of this page for only 5. 230, 117021 (2020). ML techniques have been effectively implemented in several industries, including medical and biomedical equipment, entertainment, finance, and engineering applications. Adv. The overall compressive strength and flexural strength of SAP concrete decreased by 40% and 45% in SAP 23%, respectively. Compressive behavior of fiber-reinforced concrete with end-hooked steel fibers. However, the CS of SFRC was insignificantly influenced by DMAX, CA, and properties of ISF (ISF, L/DISF). Al-Baghdadi, H. M., Al-Merib, F. H., Ibrahim, A. Question: How is the required strength selected, measured, and obtained? It was observed that overall, the ANN model outperformed the genetic algorithm in predicting the CS of SFRC. Chen, H., Yang, J. volume13, Articlenumber:3646 (2023) Struct. Mater. This paper summarizes the research about the mechanical properties, durability, and microscopic aspects of GPRAC. ; Flexural strength - UHPC delivers more than 3,000 psi in flexural strength; traditional concrete normally possesses a flexural strength of 400 to 700 psi. ML can be used in civil engineering in various fields such as infrastructure development, structural health monitoring, and predicting the mechanical properties of materials. 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. de Montaignac, R., Massicotte, B., Charron, J.-P. & Nour, A. Mechanical and fracture properties of concrete reinforced with recycled and industrial steel fibers using Digital Image Correlation technique and X-ray micro computed tomography. The reason is the cutting embedding destroys the continuity of carbon . Invalid Email Address. Google Scholar. 8, the SVR had the most outstanding performance and the least residual error fluctuation rate, followed by RF. 37(4), 33293346 (2021). Google Scholar. 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. 1 and 2. Sci. 2021, 117 (2021). Deng, F. et al. The flexural modulus is similar to the respective tensile modulus, as reported in Table 3.1. 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 brains functioning is utilized as a foundation for the development of ANN6. Is there such an equation, and, if so, how can I get a copy? 2020, 17 (2020). While this relationship will vary from mix to mix, there have been a number of attempts to derive a flexural strength to compressive strength converter equation. Importance of flexural strength of . It is essential to point out that the MSE approach was used as a loss function throughout the optimization process. Gler, K., zbeyaz, A., Gymen, S. & Gnaydn, O. Also, a specific type of cross-validation (CV) algorithm named LOOCV (Fig. Deng et al.47 also observed that CNN was better at predicting the CS of recycled concrete (average relative error=3.65) than other methods. For example compressive strength of M20concrete is 20MPa. This online unit converter allows quick and accurate conversion . Flexural strength may range from 10% to 15% of the compressive strength depending on the concrete mix. Constr. The least contributing factors include the maximum size of aggregates (Dmax) and the length-to-diameter ratio of hooked ISFs (L/DISF). October 18, 2022. Also, it was concluded that the W/C ratio and silica fume content had the most impact on the CS of SFRC. This algorithm attempts to determine the value of a new point by exploring a collection of training sets located nearby40. Schapire, R. E. Explaining adaboost. Alternatively the spreadsheet is included in the full Concrete Properties Suite which includes many more tools for only 10. Constr. Date:3/3/2023, Publication:Materials Journal Sci. 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. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 23(1), 392399 (2009). Article The sugar industry produces a huge quantity of sugar cane bagasse ash in India. The proposed regression equations exhibit small errors when compared to the experimental results, which allow for efficient and accurate predictions of the flexural strength. Mater. 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). Build. J. Devries. 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). 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 authors declare no competing interests. B Eng. 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. 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: MATH 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. As with any general correlations this should be used with caution. Date:2/1/2023, Publication:Special Publication In fact, SVR tries to determine the best fit line. 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. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Seyed Soroush Pakzad,Naeim Roshan&Mansour Ghalehnovi, You can also search for this author in http://creativecommons.org/licenses/by/4.0/. 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. This property of concrete is commonly considered in structural design. 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. Date:11/1/2022, Publication:IJCSM Then, nine well received ML algorithms are developed on the data and different metrics were used to evaluate the performance of these algorithms. Geopolymer recycled aggregate concrete (GPRAC) is a new type of green material with broad application prospects by replacing ordinary Portland cement with geopolymer and natural aggregates with recycled aggregates. The forming embedding can obtain better flexural strength. 38800 Country Club Dr. 147, 286295 (2017). World Acad. J. Civ. Build. Compos. Shamsabadi, E. A. et al. Privacy Policy | Terms of Use 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. Eng. Ly, H.-B., Nguyen, T.-A. To generate fiber-reinforced concrete (FRC), used fibers are typically short, discontinuous, and randomly dispersed throughout the concrete matrix8. Buy now for only 5. In Artificial Intelligence and Statistics 192204. de-Prado-Gil, J., Palencia, C., Silva-Monteiro, N. & Martnez-Garca, R. To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models. Mater. This is much more difficult and less accurate than the equivalent concrete cube test, which is why it is common to test the compressive strength and then convert to flexural strength when checking the concrete's compliance with the specification. Zhu et al.13 noticed a linearly increase of CS by increasing VISF from 0 to 2.0%. Therefore, according to the KNN results in predicting the CS of SFRC and compatibility with previous studies (in using the KNN in predicting the CS of various concrete types), it was observed that like MLR, KNN technique could not perform promisingly in predicting the CS of SFRC. CAS & Liu, J. Mater. Deepa, C., SathiyaKumari, K. & Sudha, V. P. Prediction of the compressive strength of high performance concrete mix using tree based modeling. Compressive strength prediction of recycled concrete based on deep learning. 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. https://doi.org/10.1038/s41598-023-30606-y, DOI: https://doi.org/10.1038/s41598-023-30606-y. The correlation coefficient (\(R\)) is a statistical measure that shows the strength of the linear relationship between two sets of data. 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. Build. All tree-based models can be applied to regression (predicting numerical values) or classification (predicting categorical values) problems. The rock strength determined by . Performance of implimented algorithms in predicting CS of steel fiber-reinforced sconcrete (SFRC). 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. 301, 124081 (2021). Plus 135(8), 682 (2020). 161, 141155 (2018). Then, among K neighbors, each category's data points are counted. Generally, the developed ML models can accurately predict the effect of the W/C ratio on the predicted CS. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Americans with Disabilities Act (ADA) Info, ACI Foundation Scholarships & Fellowships, Practice oriented papers and articles (338), Free Online Education Presentations (Videos) (14), ACI CODE-350-20: Code Requirements for Environmental Engineering Concrete Structures (ACI 350-20) and Commentary (ACI 350R-20), ACI CODE-530/530.1-13: Building Code Requirements and Specification for Masonry Structures and Companion Commentaries, MNL-17(21) - ACI Reinforced Concrete Design Handbook, SP-017(14): The Reinforced Concrete Design Handbook (Metric) Faculty Network, SP-017(14): The Reinforced Concrete Design Handbook (Metric), ACI PRC-544.9-17: Report on Measuring Mechanical Properties of Hardened Fiber-Reinforced Concrete, SP-017(14): The Reinforced Concrete Design Handbook Volumes 1 & 2 Package, 318K-11 Building Code Requirements for Structural Concrete and Commentary (Korean), ACI CODE-440.11-22: Building Code Requirements for Structural Concrete Reinforced with Glass Fiber-Reinforced Polymer (GFRP) BarsCode and Commentary, ACI PRC-441.1-18: Report on Equivalent Rectangular Concrete Stress Block and Transverse Reinforcement for High-Strength Concrete Columns, Optimization of Activator Concentration for Graphene Oxide-based Alkali Activated Binder, Assessment of Sustainability and Self-Healing Performances of Recycled Ultra-High-Performance Concrete, Policy-Making Framework for Performance-Based Concrete Specifications, Durability Aspects of Concrete Containing Nano Titanium Dioxide, Mechanical Properties of Concrete Made with Taconite Aggregate, Effect of Compressive Glass Fiber-Reinforced Polymer Bars on Flexural Performance of Reinforced Concrete Beams, Flexural Behavior and Prediction Model of Basalt Fiber/Polypropylene Fiber-Reinforced Concrete, Effect of Nominal Maximum Aggregate Size on the Performance of Recycled Aggregate Self-Compacting Concrete : Experimental and Numerical Investigation, Performances of a Concrete Modified with Hydrothermal SiO2 Nanoparticles and Basalt Microfiber, Long-Term Mechanical Properties of Blended Fly AshRice Husk Ash Alkali-Activated Concrete, Belitic Calcium Sulfoaluminate Concrete Runway, Effect of Prestressing Ratio on Concrete-Filled FRP Rectangular Tube Beams Tested in Flexure, Bond Behavior of Steel Rebars in High-Performance Fiber-Reinforced Concretes: Experimental Evidences and Possible Applications for Structural Repairs, Self-Sensing Mortars with Recycled Carbon-Based Fillers and Fibers, Flexural Behavior of Concrete Mixtures with Waste Tyre Recycled Aggregates, Very High-Performance Fiber-Reinforced Concrete (VHPFRC) Testing and Finite Element Analysis, Mechanical and Physical Properties of Concrete Incorporating Rubber, An experimental investigation on the post-cracking behaviour of Recycled Steel Fibre Reinforced Concrete, Influence of the Post-Cracking Residual Strength Variability on the Partial Safety Factor, A new multi-scale hybrid fibre reinforced cement-based composites, Application of Sustainable BCSA Cement for Rapid Setting Prestressed Concrete Girders, Carbon Fiber Reinforced Concrete for Bus-pads, Characterizing the Effect of Admixture Types on the Durability Properties of High Early-Strength Concrete, Colloidal Nano-silica for Low Carbon Self-healing Cementitious Materials, Development of an Eco-Friendly Glass Fiber Reinforced Concrete Using Recycled Glass as Sand Replacement, Effect of Drying Environment on Mechanical Properties, Internal RH and Pore Structure of 3D Printed Concrete, Fresh, Mechanical, and Durability Properties of Steel Fiber-Reinforced Rubber Self-Compacting Concrete (SRSCC), Mechanical and Microstructural Properties of Cement Pastes with Rice Husk Ash Coated with Carbon Nanofibers Using a Natural Polymer Binder, Mechanical Properties of Concrete Ceramic Waste Materials, Performance of Fiber-Reinforced Flowable Concrete used in Bridge Rehabilitation, The effect of surface texture and cleanness on concrete strength, The effect of maximum size of aggregate on concrete strength. If there is a lower fluctuation in the residual error and the residual errors fluctuate around zero, the model will perform better. Chou, J.-S., Tsai, C.-F., Pham, A.-D. & Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Moreover, the results show that increasing the amount of FA causes a decrease in the CS of SFRC (Fig. Flexural strength is however much more dependant on the type and shape of the aggregates used. A., Hassan, R. F. & Hussein, H. H. Effects of coarse aggregate maximum size on synthetic/steel fiber reinforced concrete performance with different fiber parameters. Mater. Search results must be an exact match for the keywords. Therefore, the data needs to be normalized to avoid the dominance effect caused by magnitude differences among input parameters34. Development of deep neural network model to predict the compressive strength of rubber 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). Moreover, CNN and XGB's prediction produced two more outliers than SVR, RF, and MLR's residual errors (zero outliers). Some of the mixes were eliminated due to comprising recycled steel fibers or the other types of ISFs (such as smooth and wavy). Behbahani, H., Nematollahi, B. Today Commun. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. These are taken from the work of Croney & Croney. The user accepts ALL responsibility for decisions made as a result of the use of this design tool. Mahesh et al.19 used ML algorithms on a 140-raw dataset considering 8 different features (LISF, VISF, and L/DISF as the fiber properties) and concluded that the artificial neural network (ANN) had the best performance in predicting the CS of SFRC with a regression coefficient of 0.97.