• 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 Korea Institute of Building Construction
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• v.2 no.4
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• pp.177-182
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• 2002

The purpose of this study is to develope the strength prediction model by Maturity Method. A maturity function is a mathematical expression to account for the combined effects of time and temperature on the strength development of a cementious mixture. The method of equivalent ages is to use Arrhenius equation which indicates the influence of curing temperature on the initial hydration ratio of cement. For the experimental factors of this study, we selected the concrete mixing of W/C ratio 45, 50, 55 and 60% and curing temperature 5, 10, 20 and $30^{\circ}C$. And we compare and evaluate with logistic model that is existing strength prediction model, because we have to verify adaption possibility of new strength prediction model which is proposed by maturity method. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor.

• 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.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.

• 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.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.139-146
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• 2002

The characteristics and prediction model for the shear strength of unsaturated residual soils was studied. In order to investigate the influence of the net normal stress on the shear strength, unsaturated triaxial tests and SWCC tests were carried out varying the net normal stress, and the experimental data for unsaturated shear strength tests were compared with predicted shear strength envelopes using existing prediction models. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the net normal stress. Therefore, to achieve a truly descriptive shear strength envelope for unsaturated soils, tile effect of the normal stress on the contribution of matric suction to the shear strength has to be taken into consideration. In this paper, a modified prediction model for the unsaturated shear strength was proposed.

• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.377-384
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• 2000

The characteristics and prediction model of the shear strength for unsaturated residual soils was studied. In order to investigate the influence of the initial water content on the shear strength, unsaturated triaxial tests were carried out varying the initial water content, and the applicability of existing prediction models for the unsaturated shear strength was testified. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the initial water content. A sample compacted in the lower initial water content needs a higher suction to get the same degree of saturation while the shear strength of a sample with the lower initial water content displays a lower value. In order to apply the existing prediction models of the unsaturated shear strength to granite residual soils, a correction coefficient, α, on the internal friction angle, ø'was added.