• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1995년도 봄 학술발표회 논문집
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• pp.154-159
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• 1995

Cold weather concrete presents the many characteristic variation of quality, according to the mixing and cooling point, the cooling time and the quantity of air besides the compressive strength of concrete. Thus, in this study to verify the character of cold-weather concrete we make the concrete specimens at laboratory and cool them at cooling-melting machine and then test the 7days compressive strength of them, with the variation of compressive strength of concrete, cooling point, cooling time, cooling weather and air quantity. At the results, the compressive strength of concrete decrease in the case of early cooling point, long cooling time, low cooling temperature and the low design compressive strength

• Computers and Concrete
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• 제15권5호
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Computers and Concrete
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• 제6권6호
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• pp.437-450
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• 2009

The effect of six different curing conditions on compressive strength and ultrasonic pulse velocity (UPV) of volcanic pumice concrete (VPC) and normal concrete (NC) has been studied. The curing conditions include water, air, low temperature ($4^{\circ}C$) and different elevated temperatures of up to $110^{\circ}C$. The curing age varies from 3 days to 91 days. The development in the pulse velocity and the compressive strength is found to be higher in full water curing than the other curing conditions. The reduction of pulse velocity and compressive strength is more in high temperature curing conditions and also more in VPC compared to NC. Curing conditions affect the relationship between pulse velocity and compressive strength of both VPC and NC.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 춘계 학술논문 발표대회
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.214-215
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• 2018

This paper has been researched that the earlier compressive strength of the cement pastes containing nano-TiO₂ particle curing 1day, 3days and 7days. For the compressive strength measurements, all samples(dimensions 50×50×50mm) were prepared in accordance with ASTM C109. The compressive strength of the specimens with nano-TiO₂ at the early age(1day, 3days and 7days) stage was lower than that of the reference group. Therefore, nano-TiO₂ has little positive effect on the improvement of the compressive strength of cement pastes during early ages.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.130-131
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• 2018

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, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.

• Computers and Concrete
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• 제11권6호
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 추계 학술논문 발표대회
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• pp.83-84
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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, seven influential factors (W/B ratio, 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. The purpose of this paper is to estimate compressive strength more accurately by applying it to algorithm of the Deep learning.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.90-91
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• 2020

Utilization of upgraded fine fly ash in cement-based materials has been proved by many researchers as an effective method to improve compressive strength of cement based materials at early ages. The addition of fine fly ash has introduced dilution effect, enhanced pozzolanic reaction effect, nucleation effect and physical filling effect into cement-fly ash system. In this study, an integrated reaction model is adpoted to quantify the contributions from cement hydration and pozzolanic reaction to compressive strength. A modified model related to the physical filling effect is utilized to calculate the compressive strength increment considering the gradual dissolution of fly ash particles. Via combination of these two parts, a numerical model has been proposed to predict the compressive strength development of fine fly ash mortar considering fly ash fineness. The reliability of the model is validated through good agreement with the experimental results from previous articles.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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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.