• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Journal of the Korea Concrete Institute
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• v.13 no.3
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• pp.237-243
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• 2001

The prediction model is proposed to estimate the variation of compressive strength of fly ash concrete with aging. After analyzing the experimental result with the model, the regression results are presented according to fly ash replacement content and water-cement ratio. Based on the regression results, the influence of fly ash replacement content and water-cement ratio on apparent activation energy was investigated. According to the analysis, the model provides a good estimate of compressive strength development of fly ash concrete with aging. As the fly ash replacement content increases, the limiting relative compressive strength and initial apparent activation energy become greater. The concrete with water-cement ratio smaller than 0.40 shows that the limiting relative compressive strength and apparent activation energy are nearly constant according to water-cement ratio. But, the concrete with water-cement ratio greater than 0.40 has the increasing limiting relative compressive strength and apparent activation energy with increasing water-cement ratio.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• Computers and Concrete
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• v.11 no.6
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• pp.603-620
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• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• Journal of the Korea Institute of Building Construction
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• v.7 no.4
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• pp.145-151
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• 2007

In this paper, tests are carried out in order to investigate the strength development of concrete under various binder types, W/B and curing temperature ranged from $5{\sim}20^{\circ}C$. Fly ash and blast furnace slag were incorporated by as much as 30%, respectively. Strength development of concrete are estimated using Logistic model and strength ratio of concrete at 28days to that at early age are also investigated. According to experimental results, it is found that good agreements are obtained between measured values and calculated ones using logistic model below $20^{\circ}C$. Strength ratio of concrete at 28days to that at early age increases in case W/B decreases and curing temperature increases. Tables and graphs for strength ratio of concrete are provided in this paper. It is capable of obtaining and predicting the periods to attain design strength by considering increment factor of strength easily with the table and graphs presented in this paper. This paper presents the reference data to decide removal time of form, time to reach target strength and strength inspection of remicon whether the test specimens meet the specified criteria of compressive strength. Multi regression models with respect to the relationship between 7days compressive strength and 28 days compressive strength depending on W/B and admixture types are presented.

• International Journal of Ocean System Engineering
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• v.1 no.1
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• pp.32-36
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• 2011

EG/AD/S type model ice was originally selected as the primary model ice material for the MOERI ice tank in Korea. The existence of a sugar component in the EG/AD/S mixture may cause a serious maintenance problem. In order to understand the influence of sugar in the original model ice, a series of tests with EG/AD/S and EG/AD model ices were performed, and their material properties compared. Because the target strength of model ice in the full-scale MOERI ice tank is expensive and difficult to control, tests were performed under cold room conditions using a miniature ice tank. This paper describes the material properties of EG/AD/S and EG/AD model ices, such as flexural strength, compressive strength and elastic modulus. In order to obtain the desired strength and stiffness levels for the model ice, a warm-up process was introduced.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.837-851
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• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.

• International Journal of Concrete Structures and Materials
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• v.10 no.3
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• pp.257-269
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• 2016

Early-strength-concrete (ESC) made of Type I cement with a high Blaine value of $500m^2/kg$ reaches approximately 60 % of its compressive strength in 1 day at ambient temperature. Based on the 210 compressive test results, a generalized rateconstant material model was presented to predict the development of compressive strengths of ESC at different equivalent ages (9, 12, 18, 24, 36, 100 and 168 h) and maximum temperatures (20, 30, 40, 50 and $60^{\circ}C$) for design compressive strengths of 30, 40 and 50 MPa. The developed material model was used to find optimum curing regimes for precast prestressed members with ESC. The results indicated that depending on design compressive strength, conservatively 25-40 % savings could be realized for a total curing duration of 18 h with the maximum temperature of $60^{\circ}C$, compared with those observed in a typical curing regime for concrete with Type I cement.

• Magazine of the Korea Concrete Institute
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• v.11 no.2
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• pp.95-103
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• 1999

The reduction phenomena of concrete compressive strength with the size of specimens have been extensively investigated, but till now the adequate analysis technique is not fixed. The existing research results show that the bigger the member size, the smaller the strength. This means the nonlinear fracture mechanics theory is needed in order to analyze the fracture behaviors of concrete and the size effect. There is a few model equations that is to predict the size effect of compressive strength of standard and non-standard cylindrical specimen. However, theses equations did not considered the difference of fracturing mechanism which depends on the strength level. In this paper, model equations to predict compressive strength of concrete considering the size effect and strength level are suggested. The size effect model suggested in this paper shows good prediction compared with the existing test data of various concrete size and strength level.