• 콘크리트학회논문집
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• 제11권6호
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• pp.35-46
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• 1999

The steel fiber reinforced concrete may affect substantially to the tension stiffening at post cracking behavior. Even if several tension stiffening models exist, they are for plain and normal strength concrete. Thus, the development of tension stiffening models for steel fibrous high strength RC members are necessary at this time when steel fiber reinforced and high strength concretes are common in use. This paper presents tension stiffening effects from experimental results on direct tension members with the main variables such as concrete strength, concrete cover depth, steel fiber quantity and aspect ratio. The comparison of existing models against experimental results indicated that linear reduced model closely estimated the test results at normal strength level but overestimated at high strength level. Discontinuity stress reduced model underestimated at both strength levels. These existing models were not valid enough in applying at steel fibrous high strength concrete because they couldn't consider the concrete strength nor section area. Thus, new tension stiffening models for high strength and steel fiber reinforced concrete were proposed from the analysis of experimental results, considering concrete strength, rebar diameter, concrete cover depth, and steel fiber reinforcement.

• Computers and Concrete
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• 제3권6호
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• pp.439-454
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• 2006

This paper develops an artificial neural network (ANN) model for uniformly loaded restrained reinforced concrete (RC) slabs incorporating membrane action. The development of membrane action in RC slabs restrained against lateral displacements at the edges in buildings and bridge structures significantly increases their load carrying capacity. The benefits of compressive membrane action are usually not taken into account in currently available design methods based on yield-line theory. By extending the existing knowledge of compressive membrane action, it is possible to design slabs in building and bridge decks economically with less than normal reinforcement. The processes involved in the development of ANN model such as the creation of a database of test results from previous research studies, the selection of architecture of the network from extensive trial and error procedure, and the training and performance validation of the model are presented. The ANN model was found to predict accurately the ultimate strength of fully restrained RC slabs. The model also was able to incorporate strength enhancement of RC slabs due to membrane action as confirmed from a comparative study of experimental and yield line-based predictions. Practical applications of the developed ANN model in the design process of RC slabs are also highlighted.

• Structural Engineering and Mechanics
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• 제48권2호
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• pp.221-239
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• 2013

This paper focuses on a development of strut-and-tie model (STM) to predict the capacity of an improved longitudinal joint for decked bulb-tee bridge systems. Nine reinforced concrete beam/slab specimens anchored by spliced headed bars with different details were tested. Test results were evaluated and compared with an anticipation of the validated STM. The proposed STM provides a lower bound of the ultimate capacity of the joint zone. It shows that the lap length of headed bars has a significant effect on structural behaviors of the improved joint. To develop a full strength joint, the range of the lap length can be determined by the strength and compatibility requirement. Design recommendations to spliced headed bars, concrete strength, as well as lacer bars in the joint zone are proposed for developing a full strength joint.

• Computers and Concrete
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• 제7권2호
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• pp.169-190
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• 2010

The focus of this research effort was characterization of the flexural and tensile properties of a specific ultra-high-strength, fiber-reinforced concrete material. The material exhibited a mean unconfined compressive strength of approximately 140 MPa and was reinforced with short, randomly distributed alkali resistant glass fibers. As a part of the study, coupled experimental, analytical and numerical investigations were performed. Flexural and direct tension tests were first conducted to experimentally characterize material behavior. Following experimentation, a micromechanically-based analytical model was utilized to calculate the material's tensile failure response, which was compared to the experimental results. Lastly, to investigate the relationship between the tensile failure and flexural response, a numerical analysis of the flexural experiments was performed utilizing the experimentally developed tensile failure function. Results of the experimental, analytical and numerical investigations are presented herein.

• 대한금속재료학회지
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• 제48권7호
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• pp.598-604
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• 2010

Tensile tests are widely used for evaluating mechanical properties of materials including flow curves as well as Young's modulus, yield strength, tensile strength, and yield point elongation. This research aims at analyzing the plastic flow behavior of high strength steels for automotive bodies using the finite element method in conjunction with the viscoplastic model considering the yield point elongation phenomenon. The plastic flow behavior of the high strength steel was successfully predicted, by considering an operating deformation mechanism, in terms of normalization dislocation density, and strain hardening and accumulative damage of high strength steel using the modified constitutive model. In addition, the finite element method is employed to track the properties of the high strength steel pertaining to the deformation histories in a skin pass mill process.

• 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.

• 콘크리트학회논문집
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• 제15권1호
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• pp.87-94
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• 2003

현재 건설생산현장에서 이루어지고 있는 거푸집 제거 시기 결정, 설계기준강도 확보 등의 강도관리는 그 시점을 예측할 수 없다는 단점이 있기 때문에 건설생산현장에서의 공정계획 및 강도관리에서 한계가 있을 수밖에 없다. 이에 따라 콘크리트의 강도를 예측할 수 있으면 보다 합리적인 강도관리 및 공정계획이 가능하게 된다. 본 연구는 적산온도 방법에 의해 새로 제안된 강도예측모델의 적용가능성을 검증하기 위해 기존 강도예측모델 중 Logistic 모델과 비교 평가하였으며, 모의부재에서 채취한 코어공시체와 현장양생공시체의 압축강도를 비교 평가한 후 새로운 강도예측모델에 의해 강도를 예측하여 거푸집 제거시기를 결정하는 것에 대한 합리성을 검증하고자 하였다. 실험결과 Freiesleben의 활성화에너지를 이용한 등가재령함수에 있어서 콘크리트의 강도는 양생온도에 관계없이 유사한 강도수준을 나타내고 있으나 강도-적산온도의 상관성을 높이기 위해서는 등가재령 계산시 이용되는 활성화에너지에 대한 검토가 필요할 것으로 사료된다. 새로 제안된 모델의 경우 Logistic 모델에 비해 초기재령에 있어서 강도예측이 보다 정확한 것으로 나타났으며, SSE는 작고 결정계수는 높게 나타나고 있어 이를 이용한 강도예측이 보다 합리적일 것으로 판단된다. 본 연구의 범위 내에서 양생온도 10~15$^{\circ}C$의 경우 강도관리 측면에서 새로운 강도예측모델 사용시 압축강도 50kgf/${cm}^2$ 발현시점이 기존에 제안된 기간과 비교하여 빠르게 나타나고 있어 이를 건설생산현장에서 적용할 경우 거푸집제거시기의 단축에 의한 공기단축이 가능할 것으로 사료된다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 봄 학술발표회 논문집(I)
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• pp.659-664
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• 1999

The majority of published conclusions about structural configuration effects of bond strength were based on the observed performance of test specimens and their interpretations are mostly empirical and statistical. The empirical and statistical interpretation on bond strength have to be replaced by rational models based on simple, sound and verifiable mechanical principles. It is likely that such models also represent the key to a deeper understanding of some existing experimental data on bond strength. The presented truss model is capable of explaining failure modes involving bond slip that cannot be explained by current truss model.

• Computers and Concrete
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• 제14권3호
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• pp.233-245
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• 2014

In this paper, a rule based Mamdani type fuzzy logic model for prediction of slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes were discussed. In the model steel rebar diameters and development lengths were used as inputs. The FL model and experimental results, the coefficient of determination R2, the Root Mean Square Error were used as evaluation criteria for comparison. It was concluded that FL was practical method for predicting slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes.

• 한국정밀공학회지
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• 제24권4호
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.