• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.3
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• pp.123-130
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• 2001

The purpose of this study is to suggest the non-destructive equation for the estimation of concrete strength by ultrasonic pulse velocity at the Age of 28day compressive strength of $600{\sim}1000kg/cm^2$. For this purpose, selected test variables were water-hinder ratio, replacement ratio of silica fume, binder content, maximum size of coarse aggregate and sand-aggregate ratio. From the results, the average increase or decrease of ultrasonic pulse velocity is 61m/sec for each 1% of moisture content. And the correlation equation between the ultrasonic pulse velocity and the compressive strength of concrete is as follows. $F_c=896.3V_p-3514$ ($R^2$ = 0.81) where, $F_c$ : compressive strength($kgf/cm^2$), $V_p$ : ultrasonic velocity(km/sec).

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.1
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• pp.129-136
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• 1999

In this paper, the new non-destructive equation will be proposed and evaluated in comparison to the other foreign's non-destructive equation. Through the comparisons cores strength of mock structure with compressive strength obtained from new non-destructive equation ; rebound hammer, ultra-sonic pulse velocity and combined method, it will be analyzed about application of non-destructive equation. The results are following. The new non-destructive equations follow ; (1) $F_c=9.5R{\cdot}N+62.5$ (2) $F_c=243Vp-739$ (3) $F_c=8.1R_o+205.3V_p-802$ where, $F_c$ : Compressive Strength, $R_o$ : Rebound Number. $V_p$ : Ultra-Sonic Pulse Velocity Trough the result of mock structure test, the combined method is superior to rebound method and ultra-sonic pulse velocity method in the estimation of concrete strength. In order to apply the non-destructive equation of concrete strength to the structures, it is necessary that we should be made process study on the non-destructive equation for estimation of concrete strength in the range, time and strength of application under long-term.

• Journal of the Korea Concrete Institute
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• v.14 no.2
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• pp.137-147
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• 2002

Maturity models involving curing temperature and curing ages have been widely used to predict concrete strength, which can accurately estimate concrete strength. However, they may not consider physical quantities such as the characteristics of hydrates and the capillary porosity of microstructures associated with strength development. In order to find out the effects of both factors on a strength increment, the hydration model and the estimation method of the amount of capillary porosity were established, and the compressive strength test of concrete nth various water/cement ratios was carried out considering two test parameters, curing temperature and curing age. In this study, by analyzing the experimental results, a strength estimation model for early-age concrete that can consider the microstructural characteristics such as hydrates and capillary porosity was proposed. Measured compressive strengths were compared with estimated strengths and good agreements were obtained. Consequently, the proposed strength model can estimate compressive strength of concrete with curing age and curing temperature within an acceptable error.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.105-107
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• 2012

This study was conducted to investigate the strength development of fly ash concrete using the strength development estimation for the ready mixed concrete for construction of nuclear reactors. The findings are as follows. First, the higher the curing temperature becomes, the shorter the setting time becomes. In addition, the compressive strength also increased as the curing temperature gets higher. The apparent activation energy derived from ASTM C 1074 showed 34.75 KJ/mol. The results of concrete strength estimation confirmed that Gompertz model formula has good accuracy.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.979-984
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• 2003

Quality control of ready-mixed concrete is most important in the production step because, the performance of hardened concrete is revealed due to ready-mixed concrete. Hardened concrete has several properties physically. Above all things compressive strength of concrete has a greate effect in the design of structures, analysis, and durability. Compressive strength is simple predicted by w/c up to date, but there are some limits because different compressive strengths can be revealed in the same w/c. Therefore this study contributes to the quality control of ready-mixed concrete through statistical analysis for the relation between mix factors in mix design and compressive strength, predictable equation for compressive strength.

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.239-242
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• 2002

This study is intended to investigate the relationship between rebound value of P type schmidt hammer and the compressive strength with various aggregates, and a series of experiments about early strength quality control by P type schmidt hammer was performed. According to the results, the compressive strength of concrete using basalt and limestone aggregate is higher by 3% and lower by 4% than that of concrete using granite aggregate respectively. Concrete using basalt and lime stone aggregate show high rebound value in vertical strike. Estimation of the compressive strength does not show differences in horizontal strike, but the compressive strength is estimated high in order of granite, basalt and limestone aggregate in vertical strike. A good correlation between the rebound value of schmidt hammer and the compressive strength is confirmed regardless of aggregate types, so it could be possible to control the quality of concrete by P type schmidt hammer test when basalt and limestone aggregates are used at the same time.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.257-262
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• 2001

There are only a few tests to ensure concrete quality before placing in domestic situ; One is slump test for workability, the other is air content test for durability, the concrete compressive strength which is one of important factors to influence on concrete Quality has been tested after 28 days placing. Methods on early judgement of concrete strength have been introduced for concrete quality management, but such methods are time consuming, expensive, and required special expertise. Therefore, these have difficulty in situ application for concrete management. This study aimed at reviewing application of estimated equation of compressive strength as means for ready-mixed concrete, making an estimated equation which enables to estimate 28 days compressive strength by using regression formula analysis on basis of mixing designs of ready mixed concrete and results of compressive strength.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.767-772
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• 1998

schumidt hammer and ultra-sonic method are commonly used for concrete compressive strength test in a construction field. At present, various kinds of equations for estimation of strength are present, which have been used in a construction field. The purpose of this study is to evaluate the correlation between estimation strength by presentation equations and destructive strength to test specimen, and find out which is a suitable equation for this construction site. In this study, a strength test was carried out destructive test by means of core sampling. Non destructive test was conducted Schumidt hammer and ultra-sonic method, the experimental parameter were concrete age, test method and strength level. It is demonstrated that the correlation behavior of concrete strength in this study good due to the performs analysis of correlation between core strength and nondestructive strength.

• Computers and Concrete
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• v.4 no.4
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• pp.299-316
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• 2007

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the test on the compressive strength is complicated and time-consuming. More importantly, since the test is usually performed 28 days after the placement of the concrete at the construction site, it is too late to make improvements if unsatisfactory test results are incurred. Therefore, an accurate and practical strength estimation method that can be used before the placement of concrete is highly desirable. In this study, the estimation of the concrete strength is performed using support vector regression (SVR) based on the mix proportion data from two ready-mixed concrete companies. The estimation performance of the SVR is then compared with that of neural network (NN). The SVR method has been found to be very efficient in estimation accuracy as well as computation time, and very practical in terms of training rather than the explicit regression analyses and the NN techniques.