• 한국지반신소재학회논문집
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• 제18권4호
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• pp.243-252
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• 2019

최근 지하공간의 활용도가 높아짐에 따라 지하굴착의 수요가 증가하는 추세에 있다. 본 연구에서는 기존의 주열식 흙막이 말뚝공법의 문제점을 크게 개선하면서 향후 도심지에서 30m 이상의 대심도 지반 굴착시 발생 가능한 지반함몰 및 주변지반의 영향을 최소화할 수 있는 흙막이 CS-H 벽체의 개발 및 안정성 평가를 위하여 지반 신소재를 이용한 콘크리트 배합을 실시하였다. 지반신소재 벽체 재료로는 강섬유, 합성섬유, 유리섬유의 섬유보강재 배합과 풍쇄슬래그, 페로니켈의 잔골재 대체재를 선택하여 배합시험을 실시하였다. 또한 각 배합별 적절한 배합비를 선정하기 위하여 슬럼프시험, 압축강도 및 탄성계수, 휨강도, 할렬인장강도, 삼축투수 시험을 수행하였다. 시험결과 개발하고자 하는 CS-H 벽체에서 강섬유 배합조건의 시험값이 다른 기준값에 비해 매우 우수한 결과를 나타내어 가장 적합한 것으로 평가되었다.

• 한국도로학회논문집
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• 제15권3호
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• pp.1-8
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• 2013

PURPOSES : This study is to evaluate the feasibility of using the alkali activated cement concrete for application of partial-depth repair in pavement. METHODS : This study analyzes the compressive strength of alkali activated cement mortar based on the changes in the amount/type/composition of binder(portland cement, fly ash, slag) and activator(NaOH, $Na_2SiO_3$, $Na_2CO_3$, $Na_2SO_4$). The mixture design is divided in case I of adding one kind-activator and case II of adding two kind-activators. RESULTS : The results of case I show that $Na_2SO_4$ based mixture has superior the long-term strength when compared to other mixtures, and that $Na_2CO_3$ based mixture has superior the early strength when compared to other mixtures. But the mixtures of case I is difficult to apply in the material for early-opening-to-traffic, because the strength of all mixtures isn't meet the criterion of traffic-opening. The results of case II show that NaOH-$Na_2SiO_3$ based mixtures has superior the early/long-term strength when compared to NaOH-$Na_2SiO_3$ based mixtures. In particular, the NaOH-$Na_2SiO_3$ based some mixtures turned out to pass the reference strength(1-day) of 21MPa as required for traffic-opening. CONCLUSIONS : With these results, it could be concluded that NaOH-$Na_2SiO_3$ based mixtures can be used as the material of pavement repair.

• Computers and Concrete
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• 제12권6호
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• Computers and Concrete
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• 제19권3호
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• 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.

• Advances in concrete construction
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• 제9권6호
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• pp.547-556
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• 2020

This study aimed to develop reference materials (RMs) that are chemically stable and can simulate the flow characteristics of cement paste. To this end, the candidate components of RMs were selected considering the currently required properties of RMs. Limestone, slag, silica, and kaolin were selected as substitutes for cement, while glycerol and corn syrup were selected as matrix fluids. Moreover, distilled water was used for mixing. To select the combinations of materials that meet all the required properties of RMs, flow characteristics were first analyzed. The results revealed that silica and kaolin exhibited bilateral nonlinearity. When an analysis was conducted over time, slag exhibited chemical reactions, including strength development. Moreover, fungi were observed in all mixtures with corn syrup. On the other hand, the combination of limestone, glycerol, and water exhibited a performance that met all the required properties of RMs. Thus, limestone, glycerol, and water were selected as the components of the RMs. When the influence of each component of the RMs on flow characteristics was analyzed, it was found that limestone affects the yield value, while the ratio of water and glycerol affects the plastic viscosity. Based on this, it was possible to select the mixing ratios for the RMs that can simulate the flow characteristics of cement paste under each mixing ratio. This relationship was established as an equation, which was verified under various mixing ratios. Finally, when the flow characteristics were analyzed under various temperature conditions, cement paste and the RMs exhibited similar tendencies in terms of flow characteristics. This indicated that the combinations of the selected materials could be used as RMs that can simulate the flow characteristics of cement paste with constant quality under various mixing ratio conditions and construction environment conditions.

• 한국건축시공학회지
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• 제11권6호
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• pp.547-556
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• 2011

경화 콘크리트의 물성에 있어서 황산염 저항에 가장 큰 영향을 미치는 두 요소로서 포틀랜드 시멘트의 화학적 성분과 그 양을 들 수 있다. 본 연구에서는 황산염 침투에 대한 영향을 고찰하기 위하여 ASTM C1012의 규정을 바탕으로 여러 종류의 모르타르를 제작하여 실험을 수행하였다. 본 연구에서 황산염 침투에 대한 영향을 평가하기 위해 TYPE I, 두 가지의 TYPE I-II 시멘트와 TYPE V의 시멘트 등 네 가지 모르타르를 사용하여 실험을 수행하였다. 또한 각각의 모르타르 혼합물들에 사용된 무기혼합물의 경우에도 세 가지 종류를 사용하였다. F타입 플라이애시와 C타입 플라이애시, 고로슬래그를 부피비를 기준으로 대체하여 사용하였으며, 실험을 통한 콘크리트의 팽창률을 ASTM 규정의 권장 팽창 기준을 바탕으로 비교 분석하였다.

• 자원리싸이클링
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• 제23권4호
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• pp.37-46
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• 2014

건설 분야 중 건설 재료와 건자재 산업에서 발생하는 $CO_2$는 약 6,700만톤으로 건설 분야에서 발생하는 $CO_2$의 약 30 %를 점유하고 있다. 건설 분야에서 $CO_2$ 저감은 건자재 산업에서 $CO_2$를 발생시키는 2차, 3차 양생을 줄여 소비되는 화석연료 사용과 배출가스 저감의 조절이 필수적이다. 따라서 본 연구는 시멘트 결합재를 기초로 하여 결합재를 40 % 까지 대체하여 모르타르를 제조한 후 양생방법을 달리하여 응결 및 강도 특성을 분석하였다. 결합재 치환율에 따른 강도 특성 결과 증기양생 후 고로슬래그와 CSA 15%, CAMC 5%를 치환한 시험체의 강도 증진이 활발하였다. 특히 고로슬래그 50%, CSA 15%, CAMC 5%를 치환한 시험체가 가장 높게 강도가 증진되었다. 하지만 CAMC 10%의 경우 열팽창에 의한 균열과 온도차에 의한 건조수축, 과도하게 생성된 에트린가이트에 의해 강도가 저하되는 결과를 가져왔다.

• Computers and Concrete
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• 제17권6호
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• pp.787-799
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• 2016

Climate anomalies in recent years, numerous natural disasters caused by landslides and a large amount of entrained sands and stones in Taiwan have created significant disasters and greater difficulties in subsequent reconstruction. How to respond to these problems efficaciously is an important issue. In this study, the sands and stones were doped with recycled materials (waste LCD glass sand, slag powder), and material was mixed for recycled ready-mixed soil. The study is based on security and economic principles, using flowability test to determine the water-binder ratio (W/B=2.4, 2.6, and 2.8), a fixed soil: sand ratio of 6:4 and a soil: sand: glass ratio of 6:2:2 as fine aggregate. Slag (at concentrations of 0%, 20%, and 40%) replaced the cement. The following tests were conducted: flowability, initial setting time, unit weight, drop-weight and compressive strength. The results show that the slump values are 220 -290 mm, the slump flow values are 460 -1030 mm, and the tube flow values are 240-590 mm, all conforming to the objectives of the design. The initial setting times are 945-1695 min. The unit weight deviations are 0.1-0.6%. The three groups of mixtures conform to the specification, being below 7.6 cm in the drop-weight test. In the compressive strength test, the water-binder ratios for 2.4 are optimal ($13.78-17.84kgf/cm^2$). The results show that Recycled ready-mixed soil materials (RRMSM) possesses excellent flowability. The other properties, applied to backfill engineering, can effectively save costs and are conducive to environmental protection.

• 한국건설순환자원학회논문집
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• 제7권1호
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• pp.50-56
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• 2019

본 연구에서는 자원 재활용 가능성을 위해 재활용 제품으로 레드머드와 순환 굵은골재를 사용한 무시멘트 인조석재의 제조와 특성에 대한 분석을 실시하였다. 레드머드의 치환율이 증가함에 따라 무시멘트 경화체의 유동성, 공기량, 흡수율은 감소하는 것으로 나타났으며 밀도와 강도는 증가하는 것으로 보인다. 순환 굵은골재를 사용한 인조석재의 경우, 순환 굵은골재의 치환율이 증가할수록 유동성과 흡수율은 감소하며 공기량과 밀도는 증가하는 것으로 나타났다. 순환 굵은골재의 치환율 60%까지 강도는 증가하지만 이후 치환율이 증가하면서 강도는 저하되는 것으로 보인다. 무시멘트 인조석재의 출석률은 순환 굵은골재 치환율과 비례하여 증가하는 것으로 나타났다.