• Computers and Concrete
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• 제21권6호
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• 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.

• 콘크리트학회논문집
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• 제23권2호
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• pp.185-193
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• 2011

이 연구에서는 FRP 전단보강 및 무보강 콘크리트 보의 전단강도를 정확하게 평가하기 위하여 통합전단설계방법을 개발하였다. 이를 위하여, FRP의 전단강도 기여분과 콘크리트의 전단강도 기여분을 각각 정의하였다. 기존의 FRP 전단강도 평가모델과 실험 결과를 비교 분석한 결과, Triantafillou의 FRP 전단강도 평가모델이 FRP의 유효변형률과 전단강도의 추정이 우수하므로 Triantafillou의 모델을 이용하여 FRP의 전단강도 기여분을 정의하였다. 콘크리트 전단강도 기여분은 선행 연구에서 제안된 변형도 기반 전단강도모델을 이용하여 정의하였다. 콘크리트 단면의 압축대에 작용하는 압축응력과 전단응력의 상관관계를 고려하기 위하여 콘크리트 재료파괴기준을 이용하여 콘크리트 전단강도 기여분을 산정하였다. 제안한 설계방법은 기존 실험 연구 결과와 비교하여 유효성을 검증하였다. 비교 결과 제안한 설계방법은 다양한 설계변수 범위에서 FRP 전단보강 및 무보강 콘크리트 보의 전단강도를 정확하게 평가하는 것으로 나타났다.

• 콘크리트학회지
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• 제8권3호
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• pp.197-207
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• 1996

성숙도는 콘크리트의 양생 온도이력에서 구해지는 것으로 초기강도 예측을 위한 하나의 유용한 지표가 될 수 있다. 즉 성숙도 이론은 배합이 동일할 때 성숙도 값이 같으면 강도값은 같다는 이론이다. 본 연구에서는 성숙도 법칙의 이론적 전개과정을 살펴보고 그 값을 계산하기 위하여 가장 널리 알려진 Nurse-Saul함수를 검토하였다. 또한 성숙도 함수와 기준온도(datum temperature) 설정을 위한 일련의 포괄적인 실험연구를 수행하였다. 성숙도를 강도값에 연관시키기 위한 여러 가지 성숙도-강도함수를 비교해보고 그 장단점을 분석해 보았으며 이를 통하여 유용한 강도예측함수를 제안하였다. 성숙도 이론의 적용에 있어서 고려해야 할 변수들을 도출하기 위하여 양생조건과 시멘트의 종류, 그리고 물-시멘트비등을 달리 하였으며 이 경우에는 습도나 초기양생온도 등이 변수로 채택되었다. 마지막으로 제안된 함수의 타당성과 정확성을 검증하기 위하여 콘크리트 슬래브를 타설하고 시간단계별로 콘크리트 코아를 채취하여 예측된 강도값과 비교하여 보았다. 본 논문은 실제 콘크리트 구조물의 초기강도 예측을 위해 매우 유용할 것으로 사료되며 현장품질관리에 효율적으로 활용할 수 있을 것으로 기대된다.

• International Journal of Concrete Structures and Materials
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• 제2권2호
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• pp.115-122
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• 2008

Ductility is important in the design of reinforced concrete structures. In seismic design of reinforced concrete members, it is necessary to allow for relatively large ductility so that the seismic energy is absorbed to avoid shear failure or significant degradation of strength even after yielding of reinforcing steels in the concrete member occurs. Therefore, prediction of the ductility should be as accurate as possible. The principal aim of this paper is to present the basic data for the ductility evaluation of reinforced high-strength concrete beams. Accordingly, 23 flexural tests were conducted on full-scale structural concrete beam specimens having concrete compressive strength of 40, 60, and 70MPa. The test results were then reviewed in terms of flexural capacity and ductility. The effect of concrete compressive strength, web reinforcement ratio, tension steel ratio, and shear span to beam depth ratio on ductility were investigated experimentally.

• Computers and Concrete
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• 제21권4호
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Computers and Concrete
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• 제25권1호
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• pp.83-94
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• 2020

The use of fly ash in modern-day concrete technology aiming sustainable constructions is on rapid rise. Fly ash, a spinoff from coal calcined thermal power plants with pozzolanic properties is used for cement replacement in concrete. Fly ash concrete is cost effective, which modifies and improves the fresh and hardened properties of concrete and additionally addresses the disposal and storage issues of fly ash. Soft computing techniques have gained attention in the civil engineering field which addresses the drawbacks of classical experimental and computational methods of determining the concrete compressive strength with varying percentages of fly ash. In this study, models based on soft computing techniques employed for the prediction of the compressive strengths of fly ash concrete are collected from literature. They are classified in a categorical way of concrete strengths such as control concrete, high strength concrete, high performance concrete, self-compacting concrete, and other concretes pertaining to the soft computing techniques usage. The performance of models in terms of statistical measures such as mean square error, root mean square error, coefficient of correlation, etc. has shown that soft computing techniques have potential applications for predicting the fly ash concrete compressive strengths.

• Structural Engineering and Mechanics
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• 제41권3호
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• pp.407-420
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• 2012

This study investigated possible ways to replace conventional stirrups used on high-strength concrete members with improved reinforcing materials. Headed bar and high-strength steel were chosen to substitute for conventional stirrups, and an experimental comparison between the shear behavior of high-strength concrete large beams reinforced with conventional stirrups and the chosen stirrup substitutes was made. Test results indicated that the headed bar and the high-strength steel led to a significant reserve of shear strength and a good redistribution of shear between stirrups after shear cracking. This is due to the headed bar providing excellent end anchorage and the high-strength steel successfully resisting higher and sudden shear transmission from the concrete to the shear reinforcement. Experimental results presented in this paper were also compared with various prediction models for shear strength of concrete members.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.897-902
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• 2002

The failure of flat plate connection is successive failure process accompanying with stress redistribution, hence it is necessary to compute the contributions of each resistance components at ultimate state. In the present study, the interactions of resultant forces at each faces of connection, i.e. shear, bending moment and torsional moment are considered in the assessment of strength of slab. As a result the strength prediction model for connection is made up as combination of bending resistance, shear resistance and torsional resistance. The proposed method is verified by the experimental data and numerical data of continuous slabs.

• Computers and Concrete
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• 제12권2호
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.