• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.101-102
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• 2021

The compressive strength of concrete is greatly affected by the temperature inside the concrete at the initial age immediately after pouring. The apparent activation energy of cement and the setting time of concrete are major factors influencing the development of compressive strength of concrete. This study measured the apparent activation energy and setting time according to the change in W/B for each mixing rate of Ground Granulated Blast-Furnace Slag (GGBFS). And after calculating the compressive strength prediction model, the accuracy of the prediction model was evaluated by comparing the predicted compressive strength and the compressive strength.

• 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.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.181-195
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• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

• Computers and Concrete
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• v.15 no.6
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• pp.865-878
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• 2015

In order to construct a multiscale model for the compressive strength of plain concrete columns with circular cross section subjected to central longitudinal compressive load, a column failure mechanism is proposed based on the theory of internal instability. Based on an energy analysis, the multiscale model is developed to describe the failure process and predict the column's compressive strength. Comparisons of the predicted results with experimental data show that the proposed multiscale model can accurately represent both the compressive strength of the concrete columns with circular cross section, and the effect of column size on its strength.

• Structural Engineering and Mechanics
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• v.18 no.5
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• pp.691-707
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• 2004

This paper presents the micro-mechanical modeling for predicting concrete behavior under compressive loading. The model is able to represent the heterogeneities in the microstructure up to three phases, i.e., aggregate particles, matrix and interfaces. The smeared crack concept based on non-linear fracture mechanics is implemented in order to formulate the constitutive relation for each component. The splitting tensile strength is considered as a fracture criterion for cracking in micro-level. The finite element method is employed to simulate the model based on plane stress condition by using quadratic triangular elements. The validation of the model is verified by comparing with the experimental results. The influence of tensile strength from both aggregate and matrix phases on the concrete compressive strength is demonstrated. In addition, a guideline on selecting appropriate tensile strength for each phase to obtain specified concrete compressive strength is also presented.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.115-120
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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 55MPa. 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.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.173-178
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• 2000

New prediction model is investigated estimating splitting tensile strength and modulus of elasticity with curing temperature and aging. 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. New prediction model well estimated splittinge tensile strength and elastic modulus as well as compressive strength.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.397-402
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• 2001

Microstructural characteristics such as hydrates and porosity greatly influence the development of concrete strength. In this study, a strength estimation model for early-age concrete considerig, the microstructural characteristics was proposed, which considers the effects of both an increment of degree of hydration and capillary porosity on a strength increment. Hydration modeling and compressive strength test with curing temperature and curing ages were carried out. By comparing test results with estimated strength, it is found that the strength estimation model can estimate compressive strength of early-age concrete with curing ages and curing temperature within a margin of error.

• Computers and Concrete
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• v.8 no.3
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• pp.357-369
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• 2011

In this study, effect of steel fibers on toughness and some mechanical properties of concrete were investigated. Hooked-end steel fibers were used in concrete samples with three volume fractions (${\nu}_f$) of 0.5%, 0.75% and 1% and for two aspect ratios (l/d) of 45 and 65. Compressive and flexural tensile strength and modulus of elasticity of concrete were determined for cylindrical, cubic and prismatic samples at the age of 7 and 28 days. The stress-strain curves of standard cylindrical specimens were studied to determine the effect of steel fibers on toughness of steel-fiber-reinforced concrete (SFRC). In addition, the relationship between compressive strength and the flexural tensile strength of SFRC were reported. Finally, a simple model was proposed to generate the stress-strain curves for SFRC based on strains corresponding to the peak compressive strength and 60% of peak compressive stress. The proposed model was shown to provide results in good correlation with the experimental results.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.82-89
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• 2014

The purpose of this study is to propose a simple model to evaluate the compressive strength development of concrete with various supplementary cementitious materials (SCMs) and cured under different temperatures. For the generalization of the model, the ACI 209 parabola equation was modified based on the maturity function and then experimental constants A and B and 28-day compressive strength were determined from the regression analysis using a total of 265 data-sets compiled from the available literature. To verify the proposed model, concrete specimens classified into 3 Groups were prepared according to the SCM level as a partial replacement of cement and curing temperature. The analysis of existing data clearly revealed that the 28-day compressive strength decreases when the curing temperature is higher and/or lower than the reference curing temperature ($20^{\circ}C$). Furthermore, test results showed that the compressive strength development of concrete cured under $20^{\circ}C$ until an early age of 3 days was marginally affected by the curing temperature afterward. The proposed model accurately predicts the compressive strength development of concrete tested, indicating that the mean and standard deviation of the ratios between predictions and experiments are 1.00 and 0.08, respectively.