• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.197-198
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• 2010

The purpose of this study is to compare chloride attack resistibility of light weight aggregate concrete to chloride attack resistibility of normal concrete and confirm the utility. As a result, light weight aggregate concrete's chloride attack resistibility is lower than normal concrete's chloride attack resistibility.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.331-341
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• 2015

The main purpose of this study is reducing the cost and effort for characterization of tensile strength of fiber reinforced concrete, in order to use in structural design. For this purpose, in this study, test for fiber reinforced concrete was carried out. Because fiber reinforced concrete is consisted of diverse material, it is hard to define the correlation between mix proportions and strength. Therefore, compressive strength test and tensile strength test were carried out for the range of smaller than 100 MPa of compressive strength and 0.25~1% of steel fiber volume fraction. as a results of test, two types of tensile strength were highly affected by compressive strength of concrete. However, increase rate of tensile strength was decreased with increase of compressive strength. Increase rate of tensile strength was decreased with increase of fiber volume fraction. Database was constructed using previous research data. Because estimation equations for tensile strength of fiber reinforced concrete should be multiple variable function, linear regression is hard to apply. Therefore, in this study, we decided to use the ANN(Artificial Neural Network). ANN was constructed using multiple layer perceptron architecture. Sigmoid function was used as transfer function and back propagation training method was used. As a results of prediction using artificial neural network, predicted values of test data and previous research which was randomly selected were well agreed with each other. And the main effective parameters are water-cement ratio and fiber volume fraction.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 1994.03a
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• pp.75-80
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• 1994

지반의 진동은 크게 지진과 같은 자연적인 진동과 발파, 항타(piling) 및 중장비의 가동 등에 따른 건설진동 등 인공진동으로 나눌 수 있는데 이들 지반진동은 지상 구조물의 안전도에 큰 영향을 미친다. 특히 양생(養生)중인 모르타르 및 콘크리트의 강도 및 물리적 성질에 큰 영향을 미치기 때문에 선진국에서는 엄격히 제한하고 있다. (중략)

• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.369-374
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• 2005

This study proposes a technique for the recognition of crack patterns, which includes horizontal, vertical, diagonal($-45^{\circ}$), diagonal($+45^{\circ}$), and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed image processing algorithm and artificial neural network. Features were determined using total projection technique, and the structure(no. of layers and hidden neurons) and weight of artificial neural network were determined by learning from artificial crack images. In this process, we adopted Bayesian regularization technique as a generalization method to eliminate overfitting Problem. Numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.

• Journal of the Korea Concrete Institute
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• v.16 no.6 s.84
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• pp.751-757
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• 2004

Artificial lightweight aggregates and solids were manufactured with coal ash(fly ash, bottom ash). In order to apply alkali-activated coal ash(fly ash, bottom ash) artificial lightweight aggregate to concrete, several experimental studies were performed. Thus, it can be noticed the optimal mix proportion, basic characteristies, mechanical properties and environmental safety of alkali-activated coal ash(fly ash, bottom ash) solid and alkali-activated coal ash(fly ash, bottom ash) artificial lightweight aggregate. Also, the freezing-thawing test property of concrete using the alkali-activated coal ash(fly ash, bottom ash) artificial lightweight aggregate was investigated. As a result, the optimal mixing proportion of coal ash(fly ash, bottom ash) solid to make alkali-activated artificial lightweight aggregates was cement $10\%$, water glass $15\%$, NaOH $10\%$, $MnO_2\;5\%$. Alkali-activated coal ash(fly ash, bottom ash) solid can achieve compressive strength of 36.4 MPa, at 7-days, after the paste was cured at air curing after moist curing during 24 hours in $50^{\circ}C$. Alkali-activated coal ash(fly ash, bottom ash) artificial lightweight aggregate that do impregnation to polymer was improved $10\%$ crushing strength $150\%$, and was available to concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.377-380
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• 2008

Structural lightweight concrete will reduced total loads of supporting sections and foundations in archtectural and civil structures. So, the lightweight concrete can use widely for various purpose in the archtectural and civil structures. However, the performance of lightweight concrete is essentially dependent of properties of used lightweight aggregates. So, in this paper were examined the fresh and hardened properties of lightweight concrete that are used 3types of the differences properties of lightweight aggregates from lower water-ratio to higher water-ratio of concrete mixing regions. Lightweight concrete was somewhat exhibit larger slump loss than ordinary concrete. Also, the development of compressive strength was lower than ordinary concrete, however it was not showed a marked difference. According to types of lightweight aggregates, the case of synthetic lightweight aggregate are highest performance in fresh and hardened concrete, but it is should be to evaluate the structural performance testing as anchoring and bond strength with reinforcing steel bars.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.2
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• pp.201-211
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• 2005

Due to the complex mechanism and various parameters that affect shear behavior of reinforced concrete (RC) members, models on shear tend to be complex and difficult to utilize for design of structural members, and empirical relationships formulated with limited test data often work lot members having a specific range of influencing parameters on shear. As an alternative approach tot solving this problem, artificial neural networks have been suggested by some researchers. In this paper, artificial neural networks were used to predict shear strengths of RC beams without shear reinforcement. Especially, a large database that consists of shear test results of 398 RC members without shear reinforcement was used for artificial neural network analysis. Three well known approaches for shear strength of RC members, ACI 318-02 shear provision, Zsutiy's equation, and Okamura's relationship, are also evaluated with test results in the shear database and compared with neural network approach. While ACI 318-02 provided inaccurate predictions for RC members without shear reinforcement, the empirical equations by Zsutty and Okamura provided more improved prediction of Shear strength than ACI 318-02. The artificial neural networks, however provided the best prediction of shear strengths of RC beams without shear reinforcement that was closest to test results.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.144-151
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• 2023

In this study, a database was established by collecting experimental results on various concrete mixtures subjected to freeze-thaw cycles, based on which an artificial neural network-based prediction model was developed to estimate durability resistance of concrete. A regression analysis was also conducted to derive an equation for estimating relative dynamic modulus of elasticity subjected to freeze-thaw loads. The error rate and coefficient of determination of the proposed artificial neural network model were approximately 11% and 0.72, respectively, and the regression equation also provided very similar accuracy. Thus, it is considered that the proposed artificial neural network model and regression equation can be used for estimating relative dynamic modulus of elasticity for various concrete mixtures subjected to freeze-thaw loads.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.135-140
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• 2021

In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.341-347
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• 2018

The purpose of this study is to develop a new type of hybrid artificial reef that can solve the problems of construction, installation and operation of existing concrete type and steel type artificial reef, and to evaluate basic structural performance through static loading test. For this purpose, we evaluated the strengths and weaknesses of concrete type and steel type artificial reef in literature, and developed a new type of hybrid artificial reef which can effectively overcome the problems of each artificial reefs while maintaining the advantages of two artificial reefs as an alternative respectively. In order to evaluate the merits of the proposed hybrid artificial reef, it was confirmed that the possibility of securing the convenience of the proposed hybrid artificial reef in the field and the possibility of securing the desired advantages were confirmed. Also, the static loading test was performed to evaluate the basic structural performance of the artificial reef. Through the above study, it was confirmed that the developed hybrid artificial reef exhibited proper structural performance while securing easiness of making and assemble.mm) can be predicted to have a low value up to 60% of the strength of cylinders without reinforcement.