• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.191-202
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• 1998

The maturity method in which the strength increase of cement concrete is expressed as a function of an intergral of the curing period and temperature of the concrete has often been applied to its strength prediction. For the purpose of the application of the maturity method to the compressive strength prediction for lightweight polymer mortars using an unsaturated polyester resin as a binder, the lightweight polymer mortars with various catalyst and accelerator contents, are prepared. tested for compressive strength, and the datum temperatures for the maturity equations are estimated. The maturity is calculated by using the maturity equations with the estimated datum temperature. The compressive strengths of the lighweight polymer mortars are predicted from the maturity-compressive strength relationships.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.107-108
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• 2012

The study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation. So it is intended that fundamental data are presented for quality management and process management of concrete mixing Blast Furnace Slag Power in the construction field.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.251-252
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• 2012

The exiting studies on the strength prediction by maturity method is mainly focused on concrete using OPC, meanwhile the study on the concrete mixing blast furnace slag powder (BFSP) is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by existing Maturity functions, i.e., Nurse-saul function Arrhenius function. This study also compared and examined the strength prediction of concrete mixing BGSP using ACI model and Logistic Curve prediction equation. Therefore, it is intended that fundamental data are presented for quality management and process management of concrete mixing BFSP.

• Computers and Concrete
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• v.24 no.2
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• pp.151-158
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• 2019

The sustainable development principle of replacing natural resources with renewable material is an important research topic. In this study, waste LCD (liquid crystal display) glass powder was used to replace cement (0%, 10%, 20% and 30%) through a volumetric method using three water-binder ratios (0.47, 0.59, and 0.71) to make cement mortar. The compressive strength was tested at the ages of 7, 28, 56 and 91 days. The test results show that the compressive strength increases with age but decreases as the water-binder ratio increases. The compressive strength slightly decreases with an increase in the replacement of LCD glass powder at a curing age of 7 days. However, at a curing age of 91 days, the compressive strength is slightly greater than that for the control group (glass powder is 0%). When the water-binder ratios are 0.47, 0.59 and 0.71, the compressive strength of the various replacements increases by 1.38-1.61 times, 1.56-1.80 times and 1.45-2.20 times, respectively, during the aging process from day 7 to day 91. Furthermore, a prediction model of the compressive strength of a cement mortar with waste LCD glass powder was deduced in this study. According to the comparison between the prediction analysis values and test results, the MAPE (mean absolute percentage error) values of the compressive strength are between 2.79% and 5.29%, and less than 10%. Thus, the analytical model established in this study has a good forecasting accuracy. Therefore, the proposed model can be used as a reliable tool for assessing the design strength of cement mortar from early age test results.

• Computers and Concrete
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• v.24 no.4
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• pp.329-345
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• 2019

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.185-188
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• 2005

This study selected three different specified concrete strength types of mixture which were applied to domestic seawater concrete structure and measured compressive strength and chloride diffusion coefficient and composed the formula of prediction model of chloride diffusion coefficient in order to provide the useful data for concrete mix decision of seawater structures. As a result, the formula of prediction model of chloride diffusion coefficient which set W/C and compressive strength as parameters and performed multiplex regression analysis which was based on the mathematical theory was confirmed more reliable than the formula of prediction which was composed existing water-cement ratio function.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.73-74
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• 2020

Predicting the compressive strength of concrete is important for shortening construction time and reducing construction costs. In this study, the coefficients required for maturity method and compressive strength prediction equation were calculated by measuring the cement hydration reaction rate, concrete setting time and ultimate strength. The experiment was conducted in an isothermal environment of 10℃, 20℃ and 30℃ using a normal Portland cement, and the experiment was conducted with a total of 9 levels of W/C (40%, 50%, 60%) of 3 levels for each temperature. As a result of comparing the predicted strength and the measured strength for each blend, only an error of less than 5% was found for all blending and curing periods.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Computers and Concrete
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• v.11 no.6
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• pp.587-602
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• 2013

In the current study, a method for concrete compressive strength prediction (based on cement strength class), incorporated in a software package developed by the authors for the estimation of concrete service life under harmful environments, is presented and validated. Prediction of concrete compressive strength, prior to real experimentation, can be a very useful tool for a first mix screening. Given the fact that lower limitations in strength have been set in standards, to attain a minimum of service life, a strength approach is a necessity. Furthermore, considering the number of theoretical attempts on strength predictions so far, it can be seen that although they lack widespread accepted validity, certain empirical expressions are still widely used. The method elaborated in this study, it offers a simple and accurate, compressive strength estimation, in very good agreement with experimental results. A modified version of the Feret's formula is used, since it contains only one adjustable parameter, predicted by knowing the cement strength class. The approach presented in this study can be applied on any cement type, including active additions (fly ash, silica fume) and age.

• Geomechanics and Engineering
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• v.25 no.4
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• pp.317-330
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• 2021

This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15 MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.