• Computers and Concrete
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• v.14 no.3
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• pp.247-262
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• 2014

Partial replacement of Portland cement by slag can reduce the energy consumption and $CO_2$ emission therefore is beneficial to circular economy and sustainable development. Compressive strength is the most important engineering property of concrete. This paper presents a numerical procedure to predict the development of compressive strength of slag blended concrete. This numerical procedure starts with a kinetic hydration model for cement-slag blends by considering the production of calcium hydroxide in cement hydration and its consumption in slag reactions. Reaction degrees of cement slag are obtained as accompanied results from the hydration model. Gel-space ratio of hardening slag blended concrete is determined using reaction degrees of cement and slag, mixing proportions of concrete, and volume stoichiometries of cement hydration and slag reaction. Furthermore, the development of compressive strength is evaluated through Powers' gel-space ratio theory considering the contributions of cement hydration and slag reaction. The proposed model is verified through experimental data on concrete with different water-to-binder ratios and slag substitution ratios.

• Computers and Concrete
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• v.19 no.5
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• pp.467-475
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• 2017

The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

• Computers and Concrete
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• v.12 no.4
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• pp.377-392
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• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• Advances in concrete construction
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• v.10 no.6
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• pp.479-488
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• 2020

The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.

• Journal of the Korea Concrete Institute
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• v.11 no.4
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• pp.63-71
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• 1999

In evaluating the ultimate strength of a section for a reinforced concrete flexural member, the effect of member length is not usually considered, even though the strength tends to decrease with increase of member length. In this paper the influence of specimen length on flexural compressive strength of concrete was evaluated. For this purpose, a series of C-shaped specimens subjected to axial compression and bending moment were tested using four different length-to-depth ratios (from 1,2,3 and 4) of specimens with compressive strength of 590 kgf/$\textrm{cm}^2$. Results indicate that for the region of h/c <3.0 the reduction in flexural compressive strength with increase of length-to-depth ratios was apparent. A model equation was depth of an equivalent rectangular stress block was larger than that by ACI. It was also founded that the effect of specimen length on ultimate strain was negligible. Finally more general model equation is also suggested.

• Journal of the Korea Concrete Institute
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• v.12 no.6
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• pp.35-42
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• 2000

In paper I, the relationships between compressive strength and splitting tensile strength or modulus of elasticity were proposed. In this paper, new prediction model is investigated from estimating splitting tensile strength and modulus of elasticity with curing temperature and aging without compressive strength. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values of paper I. To evaluate in-situ applicability of the model, strength and modulus of elasticity tested with variable temperatures are estimated by the prediction model. The prediction model reasonably estimates the strength and the modulus of elasticity of type I and V cement concretes tested in paper I and experimental results with variable temperature tested in this paper.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.7
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• pp.19-26
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• 2018

This study tested 25 lightweight aggregate concrete (LWAC) mixtures using the expanded bottom ash and dredged soil granules to examine the compressive strength gain of such concrete with different ages. The test parameters investigated were water-to-cement ratios and the natural sand content for the replacement of lightweight fine aggregate. The compressive strength gain rate in the basic equation specified in fib model code was experimentally determined in each mixture and then empirically formulated as a function of the water-to-cement ratio and oven-dried density of concrete. When compared with 28-day compressive strength, the tested LWAC mixtures exhibited relatively low gain ratios (0.49~0.82) at an age of 3 days whereas the gain ratios (1.16~1.41) at 91 days were higher than that (1.05~1.15) of the conventional normal-weight concrete. Thus, the fib model equations tend to overestimate the early strength gain of LWAC but underestimate the long-term strength gain. The proposed equations are in good agreement with the measured compressive strength development of LWAC at different ages, indicating that the mean and standard deviation of the normalized root mean square errors determined in each mixture are 0.101 and 0.053, respectively.

• Journal of the Korea Concrete Institute
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• v.12 no.5
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• pp.121-130
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• 2000

Currently, in evaluating a flexural strength of a concrete member, the effect of specimen depth has not been systematically studied, even though its effect on ultimate strength of a section is very important. For all types of loading conditions, the trend is that the strength of a member tends to decrease when the member depth increases. In this study, the influence of specimen depth on flexural compressive strength of concrete member was examined experimentally. A series of C-shaped specimens subjected to axial compressive force and bending moment were tested using three geometrically similar specimens with different length-to depth ratios (h/c = 1, 2 and 4) which have compressive strength of 55 MPa. The results indicate that the flexural compressive strength decreased as the specimen depth increased. A model equation was derived based on regression analyses of the experimental data. Also, the results show that ultimate strain decreases as the specimen depth increases. Finally, a general model equation for the depth effect is proposed.

• Advances in concrete construction
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• v.13 no.6
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• pp.411-422
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• 2022

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.