• Structural Engineering and Mechanics
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• 제51권2호
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• pp.249-265
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• 2014

The mechanical characteristics of materials are very essential in structural analysis for the accuracy of structural calculations. The estimation modulus of elasticity of concrete ($E_c$), one of the most important mechanical characteristics, is a very complex area in terms of analytical models. Many attempts have been made to model the modulus of elasticity through the use of experimental data. In this study, the neuro-fuzzy (NF) technique was investigated in estimating modulus of elasticity of concrete and a new simple NF model by implementing a different NF system approach was proposed. A large experimental database was used during the development stage. Then, NF model results were compared with various experimental data and results from several models available in related research literature. Several statistic measuring parameters were used to evaluate the performance of the NF model comparing to other models. Consequently, it has been observed that NF technique can be successfully used in estimating modulus of elasticity of concrete. It was also discovered that NF model results correlated strongly with experimental data and indicated more reliable outcomes in comparison to the other models.

• Computers and Concrete
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• 제12권4호
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• pp.377-392
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• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2008년도 추계 학술발표회 제20권2호
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• pp.709-712
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• 2008

최근 국내에서는 철근콘크리트 초고층 건물이 계획 또는 시공 중에 있다. 콘크리트가 가지는 시간의존적인 재료특성인 크리프 및 건조수축 특성파악이 초고층 건물 시공에서 이슈화 되고 있는 부등축소량 해결의 출발점이 되고 있다. 본 연구는 잠실 제2롯데월드의 부등축소량 해결을 위한 사전단계인 재료연구를 포함하고 있으며 당사 프로젝트 코어부 콘크리트 배합 50, 60, 70MPa에 대한 재료연구의 첫 번째 단계로 진행 중인 콘크리트 테스트 프로그램의 결과 값과 예측 모델식에 의한 결과 값을 전체 건조수축 변형과 단위 응력당전체 크리프 변형(Compliance)에 근거한 분석이 이루어졌으며 사용된 예측 모델식은 ACI209R Model,Ba${\check{z}}$ant-Baweja B3 Model, CEB MC99 Model, 및 GL2000 Model등과 같다. 그리고 사전설계 단계에서 재료실험 없이 콘크리트의 크리프 및 건조수축 특성을 간단히 파악하는 데 도움이 될 수 있도록고성능 고강도 콘크리트에 대한 적절한 예측 모델식을 제안하고자 하였다.

• Earthquakes and Structures
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• 제11권4호
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• pp.723-740
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• 2016

Reinforced concrete corbels are generally used to transfer loads within a structural system, such as buildings, bridges, and facilities in general. They commonly present low aspect ratio, requiring an accurate model for shear strength prediction in order to promote flexural behavior. The model described here, originally developed for walls, was adapted for corbels. The model is based on a reinforced concrete panel, described by constitutive laws for concrete and steel and applied in a fixed direction. Equilibrium in the orthogonal direction to the shearing force allows for the estimation of the shear stress versus strain response. The original model yielded conservative results with important scatter, thus various modifications were implemented in order to improve strength predictions: 1) recalibration of the strut (crack) direction, capturing the absence of transverse reinforcement and axial load in most corbels, 2) inclusion of main (boundary) reinforcement in the equilibrium equation, capturing its participation in the mechanism, and 3) decrease in aspect ratio by considering the width of the loading plate in the formulation. To analyze the behavior of the theoretical model, a database of 109 specimens available in the literature was collected. The model yielded an average model-to-test shear strength ratio of 0.98 and a coefficient of variation of 0.16, showing also that most test variables are well captured with the model, and providing better results than the original model. The model strength prediction is compared with other models in the literature, resulting in one of the most accurate estimates.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1998년도 가을 학술발표논문집(II)
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• pp.468-473
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• 1998

The objective of this paper is to develop a consistent algorithm for the finite element analysis for behavior of concrete under cyclic loading using viscoplastic-damage model. For modeling the behavior of concrete under cyclic loading, consistent algorithms of rate-dependent viscoplastic-damage are employed with a Willam-Warnke 5-parameter failure criterion which can consider the softening behavior of concrete and consistent tangent moduli are derived. Using finite element program implemented with the developed algorithms, the algorithms are verified and the behaviors of concrete under cylic loading are simulated and compared with experimental data.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2001년도 봄 학술발표회 논문집
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• pp.571-576
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• 2001

This paper presents the utilization of waste concrete as vertical drain material. The materials used as vertical drain material were the waste concrete, obtained from the demolished apartments or concrete structure and sand. In this study, laboratory model test was performed to investigate settlement and bearing capacity between sand compaction pile and waste concrete compaction pile. The results of laboratory model test showed that the improvement efficiency of soft ground by waste concrete compaction pile was better than sand compaction pile.

• Computers and Concrete
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• 제9권1호
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• pp.1-19
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• 2012

The size-effect study of various fracture parameters obtained from two parameter fracture model, effective crack model, double-K fracture model and double-G fracture model is presented in the paper. Fictitious crack model (FCM) for three-point bend test geometry for cracked concrete beam of laboratory size range 100-400 mm is developed and the different fracture parameters from size effect model, effective crack model, double-K fracture model and double-G fracture model are evaluated using the input data obtained from FCM. In addition, the fracture parameters of two parameter fracture model are obtained using the mathematical coefficients available in literature. From the study it is concluded that the fracture parameters obtained from various nonlinear fracture models including the double-K and double-G fracture models are influenced by the specimen size. These fracture parameters maintain some definite interrelationship depending upon the specimen size and relative size of initial notch length.

• Steel and Composite Structures
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• 제2권5호
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• pp.379-396
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• 2002

The paper describes the mechanical behavior of short concrete-filled steel tube (CFT) columns with circular section. The efficiency of the steel tube in confining the concrete core depending on concrete strength and the steel tube thickness was examined. Fifteen columns were tested to failure under concentric axial loading. Furthermore, a mechanical model based on the interaction between the concrete core and the steel tube was developed. The model employs a volumetric strain history for the concrete, characterized by the level of applied confining stress. The situation of passive confinement is accounted for by an incremental procedure, which continuously updates the confining stress. The post-yield behavior of the columns is greatly influenced by the confinement level and is related to the efficiency of the steel tube in confining the concrete core. It is possible to classify the post-yield behavior into three categories: strain softening, perfectly plastic and strain hardening behavior. The softening behavior, which is due to a shear plane failure in the concrete core, was found for some of the CFT columns with high-strength concrete. Nevertheless, with a CFT column, it is possible to use high-strength concrete to obtain higher load resistance and still achieve a good ductile behavior.

• Structural Engineering and Mechanics
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• 제84권3호
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• pp.361-373
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• 2022

This paper examines the flexural performance of concrete beams reinforced with glass fibre-reinforced polymer (GFRP) bars under fatigue loading. Experiments were carried out on concrete beams of size 1500×200×100 mm reinforced with 10 mm and 13 mm diameter GFRP bars under fatigue loading. Experimental investigations revealed that fatigue loading affects both strength and serviceability properties of GFRP reinforced concrete. Experimental results indicated that (i) the concrete beams experienced increase in deflection with increase in number of cycles and failed suddenly due to snapping of rebars and (ii) the fatigue life of concrete beams drastically decreased with increase in stress level. Analytical model presented a procedure for predicting the deflection of concrete beams reinforced with GFRP bars under cyclic loading. Deflection of concrete beams was computed by considering the aspects such as stiffness degradation, force equilibrium equations and effective moment of inertia. Nonlinear finite element (FE) analysis was performed on concrete beams reinforced with GFRP bars. Appropriate constitutive relationships for concrete and GFRP bars were considered in the numerical modelling. Concrete non linearity has been accounted through concrete damage plasticity model available in ABAQUS. Deflection versus number of cycles obtained experimentally for various beams was compared with the analytical and numerical predictions. It was observed that the predicted values are comparable (less than 20% difference) with the corresponding experimental observations.

• Computers and Concrete
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• 제19권5호
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.