• 한국건설순환자원학회논문집
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• 제10권1호
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• pp.48-55
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• 2022

본 연구는 콘크리트의 운반 과정 중 발생하는 레미콘 회수수의 재활용율 증대 및 온실가스 저감을 위한 연구의 일환으로 회수수를 배합수 및 인공경량골재 프리웨팅수로 사용하고 고로슬래그 미분말 및 플라이애시를 시멘트 대체재로 사용한 3성분계 경량 골재 모르타르의 공학적 특성을 검토하였다. 이를 위해 3성분계 경량 골재 모르타르의 플로우, 기건단위질량, 압축강도, 건조수축, 중성화 깊이, 염화물 이온 침투 저항성을 측정하였으며 측정 결과 회수수를 사용할 경우 높아진 알칼리도에 의해 시멘트계 재료들의 반응성이 높아졌으며 3성분계 배합과 함께 사용할 경우 고로슬래그 미분말 15 %, 플라이애시를 5 % 사용할 시 모르타르의 압축강도 및 내구특성 향상에 긍정적인 것으로 나타났다.

• 한국건축시공학회지
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• 제22권4호
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• pp.337-345
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• 2022

해안지역 초고층 콘크리트 건축물 매트기초는 상하층 응력발생으로 인한 결함예방과 원활한 공정관리를 위해 일반적으로 일체타설이 요구되지만 일체타설의 경우 수화열에 의한 온도응력 균열 발생의 우려가 있으며 다짐에 대한 시공성을 확보하기 위해 높은 수준의 자기충전성의 콘크리트 배합이 필요하다. 본 연구에서는 이러한 요구성능을 만족할 수 있도록 고성능 분사제와 혼화재의 사용량을 실험변수로 배합 실험을 통해 최적량을 도출하고자 하였다. 배합 변수별 결과분석을 통해 단위수량은 155kg/m³, 결합재에서 시멘트 비율 18% 일 때 굳지 않은 콘크리트 물성 및 강도발현 목표값을 만족하는 것으로 나타났다. 4성분계(시멘트 18%, 슬래그미분말 50%, 플라이애시 27%, 실리카흄 5%)가 사용되었다.

• Computers and Concrete
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• 제32권2호
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• 한국농공학회논문집
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• 제65권1호
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• pp.51-59
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• 2023

The study is to evaluate the effect of bank protection concrete block products to streams and soils. The effect on three types of bank protection concrete blocks was evaluated.. The first type was manufactured using fly ash, and the second and third type products used fine blast furnace slag powder. The laboratory and field Experiments test results showed the pHs of 9 or less. Also, any heavy metals were not detected in the heavy metal leaching tests. Although some iron (Fe) was partially detected, it still met the water quality standards. In addition, heavy metal was detected from all blocks by the US drinking water evaluation standards method. An on-site water quality and soil contamination tests were performed at the places that the blocks were implemented in practice. The test results showed that the application of the bank protection concrete block product did not lead to the water and soil quality degradation. Therefore, it was found that the hardened bank protection concrete block product did not elute harmful substances such as heavy metals that affect water and soil quality degradation.

• 대한토목학회논문집
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• 제28권1A호
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• pp.155-163
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• 2008

최근 구조물이 대형화, 특수화 및 초고층화 되고 있는 건설현장에서는 일반콘크리트보다 더 뛰어난 고성능 콘크리트의 시험적인 시공이 증가하고 있는 추세이다. 교량의 경우 과거 동바리 공법에 의한 시공은 소음 및 분진과 공사기간의 장기화 등의 문제로 점점 감소되었으며, 최근에는 시공기간 단축 및 도심지 환경에 적합한 프리캐스트로 시공하는 현장이 급속히 증가하고 있다. 교량구조물의 경우 휨에 대한 안전성을 확보하기 위하여 과밀배근된 부재를 생산하고 있는 실정이며, 과밀배근된 부재를 생산하기 위하여 일반콘크리트보다 유동과 충전성능이 월등한 초유동 자기충전 콘크리트가 적용되는 것이 바람직하다고 판단된다. 본 연구에서는 고로슬래그 및 플라이애쉬를 2성분계 및 3성분계 배합을 통하여 과밀배근된 교량구조물에 초유동 자기충전 콘크리트를 적용하기 위한 방법으로 과밀배근된 구조물에 적용할 수 있는 일본 토목학회의 JSCE 1등급 규정에 따른 초유동 자기충전 콘크리트의 유동 특성을 검토하였다.

• Computers and Concrete
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• 제33권2호
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Advances in concrete construction
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• 제16권3호
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• pp.155-167
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• 2023

The penetrated chloride in concrete has different behavior with mix proportions and local exposure conditions, even in the same environments, so that it is very important to quantify surface chloride contents for durability design. As well known, the surface chloride content which is a key parameter like external loading in structural safety design increases with exposure period. In this study, concrete samples containing OPC (Ordinary Portland Cement), GGBFS (Ground Granulated Blast Furnace Slag), and FA (Fly Ash) had been exposed to submerged, tidal, and splash area for 5 years, then the surface chloride contents changing with exposure period were evaluated. The surface chloride contents were obtained from the chloride profile based on the Fick's 2nd Law, and the regression analysis for them was performed with exponential and square root function. After exposure period of 5 years in submerged and tidal area conditions, the surface chloride content of OPC concrete increased to 6.4 kg/m³ - 7.3 kg/m³, and the surface chloride content of GGBFS concrete was evaluated as 7.3 kg/m³ - 11.5 kg/m³. In the higher replacement ratio of GGBFS, the higher surface chloride contents were evaluated. The surface chloride content in FA concrete showed a range of 6.7 kg/m³ to 9.9 kg/m³, which was the intermediate level of OPC and GGBFS concrete. In the case of splash area, the surface chloride contents in all specimens were from 0.59 kg/m³ to 0.75 kg/m³, which was the lowest of all exposure conditions. Experimental constants available for durability design of chloride ingress were derived through regression analysis over exposure period. In the concrete with GGBFS replacement ratio of 50%, the increase rate of surface chloride contents decreased rapidly as the water to binder ratio increased.

• Advances in concrete construction
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• 제16권6호
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• pp.303-316
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• 2023

Using industrial wastes and construction and demolition (C&D) wastes is potentially advantageous for concrete production in terms of sustainability improvement. In this paper, a sustainable Self-Compacting Concrete (SCC) made with industrial wastes and C&D wastes was proposed by considerably replacing natural counterparts with recycled coarse aggregates (RCAs) and supplementary cementitious materials (SCMs) (i.e., Fly ash (FA), ground granulated blast furnace slag (GGBS) and silica fume (SF)). A total of 12 SCC mixes with various RCAs and different combination SCMs were prepared, which comprise binary, ternary and quaternary mixes. The mechanical properties in terms of compressive strength and static elasticity modulus of recycled aggregates (RA-SCC) mixes were determined and analyzed. Microstructural study was implemented to analyze the reason of improvement on mechanical properties. By means of life cycle assessment (LCA) method, the environmental impacts of RA-SCC with various RCAs and SCMs were quantified, analyzed and compared in the system boundary of "cradle-to-gate". In addition, the comparison of LCA results with respect to mechanical properties was conducted. The results demonstrate that the addition of proposed combination SCMs leads to significant improvement in mechanical properties of quaternary RA-SCC mixes with FA, GGBS and SF. Furthermore, quaternary RA-SCC mixes emit lowest environmental burdens without compromising mechanical properties. Thus, using the combination of FA, GGBS and SF as cement substitution to manufacture RA-SCC significantly improves the sustainability of SCC by minimizing the depletion of cement and non-renewable natural resources.

• Steel and Composite Structures
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• 제52권3호
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• 한국구조물진단유지관리공학회 논문집
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• 제10권3호
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• pp.97-105
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• 2006

고성능 콘크리트의 자기수축은 초기균열을 유도할 수 있기 때문에 내구성 측면에서 매우 중요하다. 이에 따라, 본 연구에서는 실험을 통해 혼화재료를 혼입한 고성능 콘크리트의 자기수축 특성을 분석한 후 예측모델을 제안하였다. 이를 위해 다양한 실험변수를 가진 시편에 대해 광범위한 실험을 수행하였다. 주요 실험변수는 혼화재료의 종류 및 혼입률로 설정하였으며 물-시멘트비는 30%로 고정하였다. 실험결과 플라이애시를 치환한 경우에는 자기수축량이 다소 감소하였으며, 고로슬래그를 사용한 경우에는 자기수축이 증가하였다. 또한, 수축저감제 및 팽창재의 혼입량이 클수록 고성능 콘크리트의 자기수축은 감소하는 경향을 보였다. 한편, 본 논문에서는 회귀분석을 통해 혼화재료를 사용한 고성능 콘크리트의 자기수축 예측식을 제안하였으며, 제안된 자기수축 예측식은 실험결과와 비교적 일치하였다