• Computers and Concrete
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• v.15 no.5
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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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• v.6 no.6
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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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.542-549
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• 2004

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network (PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Adaptive probabilistic neural network (APNN) was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment algorithm. The conventional PNN and APNN were applied to predict the compressive strength of concrete using actual test data of a concrete company. APNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.135-140
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• 1999

The size effect of axial compressive strength of concrete in notched specimens was experimentally investigated. Based on the concept of the fracture mechanics and size effect law, theoretical studies for axial compressive failure of concrete were reviewed, and two failure modes of concrete specimen under compression were discussed. In this study, experiment of axial compressive failure, which is one of the two failure modes, was carried out by using double cantilever fracture specimens. By varying the slenderness of cantilevers and the eccentricity of applied loads with respect to the axis of each cantilever, the size effect of axial compressive strength of concrete was investigated, and predicted by Bazant's size effect law. The test results show that size effect appears conspicuously for all series of specimens. For the eccentricity of loads, the influence of tensile and compressive stress at the notch tip are significant and so that the size effect is varied. In other words, if the influence of tensile stress at the notch tip grows up, the size effect of concrete increases. And the fact that the fracture process zone must be sufficiently secured for more accurate experiment was affirmed.

• Structural Engineering and Mechanics
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• v.58 no.6
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• pp.989-999
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• 2016

Moist curing of concrete is a time consuming procedure. It takes minimum 28 days of curing to obtain the characteristic strength of concrete. However, under certain situations such as shortage of time, weather conditions, on the spot changes in project and speedy construction, waiting for entire curing period becomes unaffordable. This situation demands early strength of concrete which can be met using accelerated curing methods. It becomes necessary to obtain early strength of concrete rather than waiting for entire period of curing which proves to be uneconomical. In India, accelerated curing methods are used to arrive upon the actual strength by resorting to the equations suggested by Bureau of Indian Standards' (BIS). However, it has been observed that the results obtained using above equations are exaggerated. In the present experimental investigations, the results of the accelerated compressive strength of the concrete are used to develop the regression models for predicting the short term and long term compressive strength of concrete. The proposed regression models show better agreement with the actual compressive strength than the existing model suggested by BIS specification.

• 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.

• Journal of the Korea Concrete Institute
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• v.12 no.5
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• pp.121-130
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• 2000

Currently, in evaluating a flexural strength of a concrete member, the effect of specimen depth has not been systematically studied, even though its effect on ultimate strength of a section is very important. For all types of loading conditions, the trend is that the strength of a member tends to decrease when the member depth increases. In this study, the influence of specimen depth on flexural compressive strength of concrete member was examined experimentally. A series of C-shaped specimens subjected to axial compressive force and bending moment were tested using three geometrically similar specimens with different length-to depth ratios (h/c = 1, 2 and 4) which have compressive strength of 55 MPa. The results indicate that the flexural compressive strength decreased as the specimen depth increased. A model equation was derived based on regression analyses of the experimental data. Also, the results show that ultimate strain decreases as the specimen depth increases. Finally, a general model equation for the depth effect is proposed.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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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.