• Journal of the Korea Concrete Institute
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• v.13 no.2
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• pp.130-138
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• 2001

This paper presents on the shear behavior prediction of reinforced concrete beams using Transformation Angle Truss Model (TATM). The TATM can evaluate the stress-strain relationships for cracked concrete by transforming stresses and strains for principal plane into those over the crack surfaces. This proposed analytical method simplifies the Fixed Angle Softened Truss Model (FA-STM) and removes the limitation of applicability of the FA-STM. The shear.strength and strain of reinforced concrete beams are predicted by using the TATM. For the verification of proposed method, experimental results of reinforced concrete beams were compared with theoretical results by the TATM, FA-STM and Rotating Angle Softened Truss Model (RA-STM).

• Computers and Concrete
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• v.10 no.5
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• pp.523-539
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• 2012

Silica fume has long been used as a mineral admixture to improve the durability and produce high strength and high performance concrete. And in marine and coastal environments, penetration of chloride ions is one of the main mechanisms causing concrete reinforcement corrosion. In this paper, we proposed a numerical procedure to predict the chloride diffusion in a hydrating silica fume blended concrete. This numerical procedure includes two parts: a hydration model and a chloride diffusion model. The hydration model starts with mix proportions of silica fume blended concrete and considers Portland cement hydration and silica fume reaction respectively. By using the hydration model, the evolution of properties of silica fume blended concrete is predicted as a function of curing age and these properties are adopted as input parameters for the chloride penetration model. Furthermore, based on the modeling of physicochemical processes of diffusion of chloride ion into concrete, the chloride distribution in silica fume blended concrete is evaluated. The prediction results agree well with experiment results of chloride ion concentrations in the hydrating concrete incorporating silica fume.

• 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 Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Magazine of the Korea Concrete Institute
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• v.4 no.4
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• pp.135-147
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• 1992

본 연구는 기존의 이론을 배경으로 전단키에 영향을 미치는 전달전달의 요소가 포함된 기본식을 산정하여, 접합부의 유형에 따라 구체적으로 전단강도를 예측하는 방법을 제안하였다. 접합부 콘크리트와 횡보강철근의 강도 및 장부호과를 고려한 프리캐스트 콘크리트 전단키 접합부의 기본극한강도식은 수정 Mohor-Coulomb의 파괴기준과 항복선의 도입에 의하여 전개하였고, 극한전단능력의 근사해는 상하계법에 의한 극치해석의 수법을 이용하여 구하고 여기에 재료의 유효강도계수를 도입하였다. 또한, 지존의 실험결과와 비교하여 그 적용성을 고찰하였다.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.

• Journal of the Korea Concrete Institute
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• v.12 no.1
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• pp.101-112
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• 2000

In many structures, the masonry infill panels have been used for architectural reasons and their influence on the structure is often ignored by engineers. However, it has been recognized that the presence of masonry infills may debates. Recently, the pushover analysis technique is used for the prediction of the inelastic behaviors of structures in the seismic evaluation of existing buildings. However, the reliability of this analysis method has not been fully checked with the test results, particularly in the case of masonry-infilled frames. The objective of this study is to verify the correlation between the experimental and analytical reponses of a high-rise masonry-infilled reinforced concrete frame using DRAIN-2DX program and the test results performed previously. It is concluded from this comparison that the strength and stiffness of members can be predicted with quite high reliability while the ductility capacity of members can not be described reasonably.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.6
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• pp.37-45
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• 2004

In this study, twelve different glass fiber reinforced polymer concrete composite panel specimens with various rib heights and tensile side and reinforced side thickness were produced, and the flexural tests were conducted to figure out the effect of The height and thickness influencing on the flexural properties of composite panel. Test results of the study are presented. Especially, a prediction equation of the ultimate moment based on the strength design method agrees well with the test results, and it is thought to be useful for the corresponding design of cross-section according to various spans as the glass fiber reinforced polymer concrete composite panel is applied for a permanent mold.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.375-378
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• 2005

In the mechanism of beam shear failure, beam action and arch action always exist simultaneously. According to a/d ratio, the proportion and contribution between these two actions to shear capacity are merely changed. Moreover, the current codes recommendations are founded on the experimental results with normal strength concrete, the applicable range of $f'_{c}$ must be extended. Based on this mechanism and new requirement, an analytical equation is proposed for shear capacity prediction of reinforced concrete beams without stirrups. To reflect contribution change of two actions, stress variation in longitudinal reinforcement along the span is considered with Jenq and Shah Model. Dowel action and shear friction are also taken into account. Size effect is included to derive more precise equation. It is shown that the proposed equation is more accurate than other empirical equations and codes. So, it can be possible that wide range of a/d ratio is considered by one equation.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.341-344
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• 2008

Lightweight concrete is known for its advantage of reducing the self-weight of the structures, reducing the areas of sectional members as well as making the construction convenient. Thus the construction cost can be saved when applied to structures such as long-span bridge and high rise building. However, the lightweight concrete requires specific mix design method that is quite different from the typical concrete, since using the typical mix method would give rise the material segregation as well as lower the strength by the reduced weight of the aggregate. In order to avoid such problems, it is recommended to apply the mix design method of self-consolidating concrete for the lightweight concrete. Therefore experimental tests were performed as such mechanical properties(compressive strength, dry density and structural efficiency) of concrete and dry shrinkage according to ACI committee 209.