• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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• pp.117-124
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• 1997

In this study, mechanical properties of type V cement concrete with different curing temperature were investigated. The tests for mechancial properties, i.e., compressive strength and modulus of elasticity, were carried out on two kinds of type V cement concrete mixes. concrete cylinders cured at 10, 23, 35 and 50℃ were tested at 1, 3, 7 and 8 days. The 'rate constant model' was used to described the combined effects of time and temperature on compressive strength development. Test results show that concrete subjected to high temperature at early age attains greater strength than concrete to low temperature but eventually attains lower later-age strength than that. With type V cement concrete, the linear and Arrhenius rate constant models both accurately describe the development of relative strength as afunction of the equivalent age.

• Computers and Concrete
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• 제19권4호
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• pp.419-427
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• 2017

With the rising global environmental awareness on energy saving and carbon reduction, as well as the environmental transition and natural disasters resulted from the greenhouse effect, waste resources should be efficiently used to save environmental space and achieve environmental protection principle of "sustainable development and recycling". This study used recycled cement mortar and adopted the volumetric method for experimental design, which replaced cement (0%, 10%, 20%, 30%) with recycled materials (fly ash, slag, glass powder) to test compressive strength and ultrasonic pulse velocity (UPV). The hyperbolic function for nonlinear multivariate regression analysis was used to build prediction models, in order to study the effect of different recycled material addition levels (the function of $R_m$(F, S, G) was used and be a representative of the content of recycled materials, such as fly ash, slag and glass) on the compressive strength and UPV of cement mortar. The calculated results are in accordance with laboratory-measured data, which are the mortar compressive strength and UPV of various mix proportions. From the comparison between the prediction analysis values and test results, the coefficient of determination $R^2$ and MAPE (mean absolute percentage error) value of compressive strength are 0.970-0.988 and 5.57-8.84%, respectively. Furthermore, the $R^2$ and MAPE values for UPV are 0.960-0.987 and 1.52-1.74%, respectively. All of the $R^2$ and MAPE values are closely to 1.0 and less than 10%, respectively. Thus, the prediction models established in this study have excellent predictive ability of compressive strength and UPV for recycled materials applied in cement mortar.

• 한국건축시공학회지
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• 제23권1호
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• pp.35-43
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• 2023

본 연구에서는 시멘트 대체 재료로서 비소성 황토(NHT)를 혼합한 콘크리트의 역학적 특성을 평가하였으며, 초음파 속도 분석을 통한 콘크리트의 강도 예측식을 제안하였다. 혼합된 NHT의 시멘트 치환율을 0, 15 및 30%로 설정하였으며, 시멘트 및 NHT의 분체량에 대한 영향을 평가하기 위해 목표 강도를 30 및 45MPa로 설정하였다. 평가한 항목은 압축 강도, 초음파 속도 및 탄성계수로 설정하였으며, 재령 1, 3, 7 및 28일마다 설정한 항목을 측정하였다. 실험 결과, NHT 치환율이 증가함에 따라 역학적 특성은 감소하는 경향을 보였으며. 또한, 압축 강도와 초음파 속도의 상관관계 분석 결과 상관계수(R²)는 NHT를 혼합한 콘크리트의 경우 약 0.95로 높은 관계성을 보였다.

• Computers and Concrete
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• 제20권6호
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• 한국농공학회지
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• 제44권6호
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• pp.115-123
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• 2002

This study was performed to provide basic data for development and construction of reinforced soil wall that mixed with reinforcements such as calcium carbonate, monofilament fiber. In order to determine proper moisture content and mixing ratio by weight of reinforcement, Poisson's ratio and compressive strength tests for sandy soil had been conducted. Model tests for long-term behavior of reinforced soil wall were carried out to investigate the effect of reinforcement during loads and under static loads. The results of creep and model tests for sandy soil compared with clayey soil. Reinforced sandy soil mixed with calcium carbonate and cement showed brittle rupture by shear but that of mixed with monofilament fiber showed ductile rupture due to the tension force of fiber. It was shown that when age increased, creep strain of reinforced soil under sustained load approached constant values.

• Smart Structures and Systems
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• 제25권2호
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• pp.183-195
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• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• 콘크리트학회논문집
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• 제23권3호
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• pp.365-376
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• 2011

현행 콘크리트구조설계기준(2007)은 콘크리트 구조물의 설계에 적용하는 콘크리트의 압축응력 분포로 ACI 318의 등가 직사각형 응력 분포를 규정하고 있다. 단면의 휨강도 해석에는 등가 직사각형 응력 분포가 충분하겠지만, 성능 중심 설계의 한계 상태 검증에는 실제와 가까운 압축응력-변형률 관계가 필요하다. 또 등가 직사각형 응력 분포는 고강도 콘크리트 기둥의 휨강도 해석에 비안전측의 결과를 준다는 것이 알려져 있으므로, 이를 대신하는 새로운 응력 분포 모델이 필요하다. 이 연구에서는 Eurocode와 일본 토목학회의 설계기준에서 채택하고 있는 포물선-직선 형상의 새로운 모델을 제안하였다. 이 응력 분포 모델은 이 연구에서 수행된 압축응력 분포 실험과 타 연구자들의 실험 결과를 분석하여 도출된 것으로서, 보통 강도뿐만 아니라 고강도 콘크리트를 포함한 것이다. 제안 모델의 특성은 미국 ACI 318, 캐나다 CSA, 유럽의 Eurocode, 일본 토목학회 설계기준의 응력 분포 모델과 함께 실험 결과와 비교하여 정리하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 추계 학술논문 발표대회
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• pp.13-14
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• 2014

Ultra high performance concrete (UHPC) consists of cement, silica fume (SF), sand, fibers, water and superplasticizer. Typical water/binder-ratios are 0.15-0.20 with 20-30% of silica fume. The development off properties of hardening UHPC relates with both hydration of cement and pozzolanic reaction of silicafume. In this paper, by considering the production of calcium hydroxide in cement hydration and its consumption in the pozzolanic reaction, a numerical model is proposed to simulate the hydration of UHPC. The degree of hydration of cement and degree of reaction of silica fume are obtained as accompanied results from the proposed hydration model. The properties of hardening UHPC, such as degree of hydration of cement, calcium hydroxide contents, and compressive strength, are predicted from the contribution of cement hydration and pozzolanic reaction. The proposed model is verified through experimental data on concrete with different water-to-binder ratios and silica fume substitution ratios.

• Computers and Concrete
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• 제7권3호
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• pp.221-237
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• 2010

In this study 72 different high strength concrete (HSC) mixes were produced according to the Taguchi design of experiment method. The specimens were divided into four groups based on the range of their compressive strengths 40-60, 60-80, 80-100 and 100-125 MPa. Each group included 18 different concrete mixes. The slump and air-content values of each mix were measured at the production time. The compressive strength, splitting tensile strength and water absorption properties were obtained at 28 days. Using this data the Genetic Programming technique was used to construct models to predict mechanical properties of HSC based on its constituients. These models, together with the cost data, were then used with a Genetic Algorithm to obtain an HSC mix that has minimum cost and at the same time meets all the strength and workability requirements. The paper describes details of the experimental results, model development, and optimization results.

• Structural Engineering and Mechanics
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• 제16권1호
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).