• Journal of the Korea Concrete Institute
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• v.18 no.3 s.93
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• pp.361-370
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• 2006

The results of previous tests by many researchers have been compiled to evaluate the flexural strength of steel-fiber reinforced concrete beams. Existing prediction equations for flexural strength of such beams were examined, and a new equation based on mechanical and empirical observations, was proposed. In other words, the constitutive models for steel fiber reinforced concrete(SFRC) were proposed, which incorporate compressive and tensile strength. A steel model might also exhibit stain-hardening characteristics. Predictions based on the model are compared with the experimental data. For the collection of tests, a variation of the Henager equations, modified to apply to fiber-reinforced concrete beams, provided reliable estimates of flexural strength. The proposed equations accounted for the influence of fiber-volume fraction, fiber aspect ratio, concrete compressive strength and flexural steel reinforcement ratio. The proposed equations gave a good estimation for 129 flexural specimens evaluated.

• KCI Concrete Journal
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• v.11 no.3
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• pp.5-17
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• 1999

In recent years. the concept of the modified compression field theory (MCFT) was develped and applied to the analysis of reinforced concrete beams subjected to shear, moment, and axial load. Although too complex for regular use in the shear design or beams. the procedure has value in its ability to provide a rational method of anlysis and design for reinforced concrete members. The objective of this paper is to review the MCFT and apply it for the prediction of the response and shear strength of reinforced concrete beams A Parametric analysis was Performed on a reinforced T-section concrete beam to evaluate and compare the effects of concrete strength. longitudinal reinforcement ratio shear reinforcement ratio, and shear span to depth ratio in two different approaches the MCFT and the ACI code. The analytical study showed that the concrete contribution to shear strength by the MCFT was higher than the one by the ACI code in beams without stirrups, while it was lower with stirrups. On the other hand. shear reinforcement contribution predicted by the MCFT was much higher than the one by the ACI code. This is because the inclination angle of shear crack is much smaller than 45$^{\circ}$assumed in the ACI code.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.19-27
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• 2017

This study investigated to predict the compressive strength of unsaturated polyester resin based polymer concrete using the maturity method. The test results show that the development of the compressive strength increased exponentially until an age of 24 hours. After 24 hours, the development of the compressive strength just increased gradually. This test result shows that the strength of unsaturated polyester resin based polymer concrete was developed mainly at the early age. Estimated datum temperature of unsaturated polyester resin based polymer concrete was $-20.67^{\circ}C$ which was much lower than of datum temperature ($-10^{\circ}C$) of Portland cement concrete. Also, this study result shows that the existing maturity index associated with Portland cement concrete was not applicable for polymer concrete because curing time of Portland cement concrete is different clearly with curing time of polymer concrete. The cause of different curing time was that there were different curing mechanisms between Portland cement concrete and polymer concrete. In order to best apply the experimental data to a model, CurveExpert Professional, the commercial software, was used to determine the predictive model regarding the compressive strength of unsaturated polyester resin based polymer concrete. As a result, Gompertz Relation or Weibull Model was an appropriate model as a predictive model. The proposed model can be used to predict the compressive strength, especially, it is more useful when the maturity is in the range between $40^{\circ}C{\cdot}h^{0.4}$ and $900^{\circ}C{\cdot}h^{0.4}$.

• Computers and Concrete
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• v.22 no.2
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• pp.197-207
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• 2018

In the present study, group method of data handling networks (GMDH) are adopted and evaluated for shear strength prediction of both FRP-reinforced concrete members with and without stirrups. Input parameters considered for the GMDH are altogether 12 influential geometrical and mechanical parameters. Two available and very recently collected comprehensive datasets containing 112 and 175 data samples are used to develop new models for two cases with and without shear reinforcement, respectively. The proposed GMDH models are compared with several codes of practice. An artificial neural network (ANN) model and an ANFIS based model are also developed using the same databases to further assessment of GMDH. The accuracy of the developed models is evaluated by statistical error parameters. The results show that the GMDH outperforms other models and successfully can be used as a practical and effective tool for shear strength prediction of members without stirrups ($R^2=0.94$) and with stirrups ($R^2=0.95$). Furthermore, the relative importance and influence of input parameters in the prediction of shear capacity of reinforced concrete members are evaluated through parametric and sensitivity analyses.

• Advances in concrete construction
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• v.11 no.2
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• pp.111-126
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• 2021

Concrete cracking due to brittle tension strength significantly prevents fully utilization of the materials for "flexural-shear failure" type shear walls. Theoretical and experimental studies applying fiber reinforced concrete (FRC) have achieved fruitful results in improving the seismic performance of "flexural-shear failure" reinforced concrete shear walls. To come to an understanding of an optimal design strategy and find common performance prediction method for design methodology in terms to FRC shear walls, seismic performance on shear walls with PVA and steel FRC at edge columns and plastic region are compared in this study. The seismic behavior including damage mode, lateral bearing capacity, deformation capacity, and energy dissipation capacity are analyzed on different fiber reinforcing strategies. The experimental comparison realized that the lateral strength and deformation capacity are significantly improved for the shear walls with PVA and steel FRC in the plastic region and PVA FRC in the edge columns; PVA FRC improves both in tensile crack prevention and shear tolerance while steel FRC shows enhancement mainly in shear resistance. Moreover, the tensile strength of the FRC are suggested to be considered, and the steel bars in the tension edge reaches the ultimate strength for the confinement of the FRC in the yield and maximum lateral bearing capacity prediction comparing with the model specified in provisions.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.91-98
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• 2009

Recently, production cost of ready mixed concrete has been increased due to the rising cost of raw materials such as cement and aggregate etc. cause by the upturn of oil price and increase of shipping charge. The delivery cost of ready mixed concrete companies, however, has been decreased owing to their excessive competition in sale. Consequently, ready mixed concrete companies began to manufacture the concrete by mixing ground granulated blast furnace slag(GGBF) and fly-ash in order to lower the production cost. Therefore, the objective of this study was to predict 28 days strength of GGBF slag concrete by early strength(warm and hot water curing method) for the sake of managing with ease the quality of ready mixed concrete. In experimental results, the prediction equation for 28 days compressive strength of GGBF slag concrete could be produced through the linear regression analysis of early strength and 28 days strength. In order to acquire the reliability, all mixture were repeated as 3 times and each mixture order was carried out by random sampling. The prediction equation for 28 days strength of GGBF slag concrete by 1 day compressive strength(accelerated testing) according to warm and hot water curing method won the good reliability.

• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.337-338
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• 2010

In this study, the test results on creep properties of high strength concrete are shown compared with various prediction equations about creep. So in order to investigate the creep properties of high strength concrete, the application possibility of prediction equations about creep is studied compared with the equations in ACI-209 and CEB/FIP-90 for concrete produced using Portland cement.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Journal of the Korea Concrete Institute
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• v.11 no.3
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• pp.23-34
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• 1999

In designing PSC box girder bridge, the dead load, prestressing force, creep and shrinkage of concrete are the main factors which influence the camber and deflection of segmental concrete structure under construction. Among these factors the creep and shrinkage are the functions of the time-dependent property which, therefore, must considered with time. The prediction model for estimating creep and shrinkage of concrete has been suggested by ACI, CEB/FIP, JSCE and KSCE design code. In this study the creep and shrinkage test were carried out for four curing ages of concrete which was applied to the pretressed concrete box-girder bridge at a construction site, and the results of test were compared to the values of prediction by the design code. Shrinkage test shows that the test results are similar to KSCE-96 and JSCE-96 but very higher than other prediction model and creep test results are generally similar to ACI-209 and DSCE-96 but lower than other prediction models in contrast to shrinkage test.