최근 나노 버블 수의 건설 산업에 적용한 효과에 대하여 검증하고자 나노 버블 수를 혼입한 모르타르의 물리적 특성에 관한 연구를 진행하였으며 산소 및 이산화탄소 기체를 이용하여 나노 버블 수를 사용하였다. 모르타르의 물리적 성능을 비교하기 위하여 응결 시험과 압축강도 시험을 진행하였으며 응결 시간 내 열중량 분석을 통하여 응결 시험의 원인을 분석하고자 하였다. 본 연구 결과로써 산소 나노 버블 수를 사용하였을 경우 모르타르의 미세 응결촉진을 가져왔으며 이산화탄소 나노 버블 수를 사용한 경우 응결시간의 지연이 유의미하게 나타났다. 또한 실험한 기체의 종류에 관계 없이 강도가 증가 하였으며 응결 지연 여부와 관계없이 조기강도가 증가함을 확인하였다. 추가적인 공극구조 및 수화물 구조 분석에 관한 연구가 필요하지만 나노 버블 수가 건설 산업에 긍정적인 영향을 미칠 것으로 판단된다.

PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

세계적으로 환경에 대한 관심이 커지면서, 탄소 저감 및 탄소 중립을 위한 다양한 연구들이 진행되고 있다. 특히 최 근에는 탄소 포집 및 저장 기술인 CCS(Carbon Capture and Storage)에 주목이 높아졌다. 그뿐만 아니라, 대기 중의 탄소를 효과 적으로 저장하는 특성을 가진 바이오차는 탄소 중립에 기여할 수 있는 방안으로 다양한 연구가 진행되고 있다. 건설 산업에서 는 시멘트 대체재를 활용한 탄소 감소 관련 연구가 진행 중이며, 본 연구에서는 바이오차를 콘크리트 및 모르타르의 시멘트 대 체재로 활용하여 시멘트 사용량을 줄이고, 동시에 콘크리트 및 모르타르 내의 탄소를 포집하고 저장하여 탄소 배출량을 감소시 키고자 한다. 이를 위해 바이오차의 시멘트 치환율을 0%, 10%, 20%로 설정하고, 각각의 경우에 대해 콘크리트 및 모르타르의 슬럼프, pH 농도, 그리고 압축강도를 비교하였다.실험 결과에 따르면, 바이오차의 시멘트 치환율이 증가함에 따라 슬럼프와 압 축강도가 감소하는 경향을 보였으며, pH는 유사한 양상을 나타냈다.

This paper investigates the effects of aspect ratio and volume fraction of hooked-end normal-strength steel fibers on the compressive and flexural properties of high-strength concrete with specified compressive strength of 60 MPa. Three types of hooked-end steel fibers with aspect ratios of 64, 67 and 80 were considered and three volume fractions of 0.25%, 0.50% and 0.75% for each steel fiber were respectively added into each high-strength concrete mixture. The test results indicated that the addition of normal-strength steel fibers is effective to improve compressive and flexural properties of high-strength concrete but fiber aspect ratio had little effect on the modulus of elasticity and compressive strength. As steel fiber content and aspect ratio increased, flexural beahvior of notched high-strength concrete beams was effectively improved.

In concrete structures exposed to chloride environments such as seashore structures, chloride ions penetrate into the concrete. Chlorine ions in concrete react with cement hydrates to form Friedel’s salt and change the microstructure. Changes in the microstructure of concrete affect the mechanical performance, and the effect varies depending on the concentration of chloride ions that have penetrated. However, research on the mechanical performance of concrete by chloride ion penetration is lacking. In this study, the effect of chloride ion penetration on the mechanical performance of dry cask concrete exposed to the marine environment was investigated. The mixture proportion of self-compacting concrete is used to produce concrete specimens. CaCl2 was used to add chlorine ions, and 0, 1, 2, and 4% of the binder in weight were added. To evaluate the mechanical performance of concrete, a compressive strength test, and a splitting tensile strength test were performed. The compressive strength test was conducted through displacement control to obtain a stress-strain curve, and the loading speed was set to 10 με/sec, which is the speed of the quasi-static level. The splitting tensile strength test was performed according to KS F 2423. As a result of the experiment, the compressive strength increased when the chloride ion concentration was 1%, and the compressive strength decreased when the chlorine ion concentration was 4%. The effect of the chloride ion concentration on the peak strain was not shown. In order to present a stress-strain curve model according to the chloride ion concentration, the existing concrete compressive stress-strain models were reviewed, and it was confirmed that the experimental results could be simulated through the Popovics model.

When damaged nuclear fuel is stripped and re-fabricated into stabilized pellets, it is necessary to analyze the characteristics of the stabilized pellets, such as density, leaching behavior, and compressive strength, for final disposal. In this study, simulated nuclear fuel with UO2 and burn-up of 35 GWd/tU and 55 GWd/tU was used to measure the compressive strength of the stabilization pellet. In order to change the density of the sintered pellet, a sintered pellet was prepared by heat treatment at 1,550°C and 1,700°C for 6 hours in a reducing atmosphere of 4% H2/Ar. In the case of UO2, the density was 10.4 g/cm3 (94.5% of T.D.) and 10.6 g/cm3 (96.6% of T.D.) depending on the sintering temperature (1,550°C, 1,700°C). In the case of simulated fuel with a burn-up of 35 GWd/tU, the density was 8.8 g/cm3 (80.9% of T.D.) and 10.2 g/cm3 (93.6% of T.D.) depending on the sintering temperature (1,550°C, 1,700°C). In the case of simulated fuel with a burn-up of 55 GWd/tU, the density was 8.3 g/cm3 (77.0% of T.D.) and 10.0 g/cm3 (92.3% of T.D.) depending on the sintering temperature (1,550°C, 1,700°C). It was found that the compressive strength of simulated nuclear fuel decreased with increasing burn-up and increased with increasing density. In the case of UO2, the compressive strengths were 717.8 MPa and 897.4 MPa when the densities were 10.4 g/cm3 and 10.6 g.cm3, respectively. In the case of simulated nuclear fuel with a burn-up of 35 GWd/tU, the compressive strengths were 472.1 MPa and 732.3 MPa when the densities were 8.8 g/cm3 and 10.2 g/cm3. In the case of simulated nuclear fuel with a burn-up of 55 GWd/tU, the compressive strengths were 301.4 MPa and 515.5 MPa when the densities were 8.3 g/cm3 and 10.0 g/cm3, respectively.

본 연구에서는 양생물주기 및 보양을 하지 않은 구조체의 압축강도와 표준 양생 공시체의 압축강도를 비교 평가하였 다. 코어채취를 위해서 980x2090x200mm3 철근콘크리트 슬래브를 준비하였으며, 콘크리트 공시체 25개를 KS F 2405에 따라서 표준 양생, 20개를 구조체와 동일한 환경에서 대기중 양생을 하였다. 실험 결과, 28일 표준공시체 압축강도 대비 코어 채취 압 축강도는 약 4MPa 낮게, 대기중 양생 공시체의 압축강도는 약 7.6MPa 낮게 측정되었다. KCS에서는 양생기간의 온도가 낮을 경 우 온도보정강도를 제시하고 있으며, 실험결과로부터 제시된 값이 적정함을 확인하였다. 7일, 14일에 측정된 대기중 공시체의 압축강도는 코어채취 공시체의 압축강도와 유사하였지만, 28일 압축강도는 현저하게 차이가 나타났다. 초기 구조체의 초기 압축 강도 예측에 대기양생 공시체를 이용하는 것은 가능하지만, 28일 압축강도를 추측하는데 대기중 양생 공시체를 사용하는 것은 무리가 있다고 판단된다.

고성능 콘크리트(HPC) 압축강도는 추가적인 시멘트질 재료의 사용으로 인해 예측하기 어렵고, 개선된 예측 모델의 개발이 필수적 이다. 따라서, 본 연구의 목적은 배깅과 스태킹을 결합한 앙상블 기법을 사용하여 HPC 압축강도 예측 모델을 개발하는 것이다. 이 논 문의 핵심적 기여는 기존 앙상블 기법인 배깅과 스태킹을 통합하여 새로운 앙상블 기법을 제시하고, 단일 기계학습 모델의 문제점을 해결하여 모델 예측 성능을 높이고자 한다. 단일 기계학습법으로 비선형 회귀분석, 서포트 벡터 머신, 인공신경망, 가우시안 프로세스 회귀를 사용하고, 앙상블 기법으로 배깅, 스태킹을 이용하였다. 결과적으로 본 연구에서 제안된 모델이 단일 기계학습 모델, 배깅 및 스태킹 모델보다 높은 정확도를 보였다. 이는 대표적인 4가지 성능 지표 비교를 통해 확인하였고, 제안된 방법의 유효성을 검증하였다.

일반적으로 콘크리트는 골재, 모래, 시멘트, 담수, 혼합재 등 다양한 재료로 구성되어있으며 재령에 따라서 강도가 증 가한다. 콘크리트에 필요한 각 재료의 비율은 혼합 설계를 통해 결정되지만, 콘크리트의 강도는 실험적으로 측정되기 전까지는 알 수 없다. 이러한 한계를 극복하기 위해 실험을 통해 얻은 데이터를 이용하여 콘크리트의 압축 강도를 예측하기 위해 통계수 학과 기계학습 알고리즘을 이용한 많은 연구가 시도되었다. 이전의 연구는 콘크리트 압축 강도 예측에 신경망 기법이 가장 적 합하다고 제안하였다. 그러나 신경망 기법은 다른 기계학습과 비교하여 모델 학습에 계산 비용이 많이 들어 실제로 적용하기 어려운 문제점이 있다. 최근 몇 년 동안 다양한 회귀 분석 모델이 개발되었으므로 본 연구에서는 신경망 대신 최신 회귀 분석 모델을 이용하여 콘크리트 강도 예측모델을 제시하였다. 이를 위해 최근 개발된 회귀 분석 모델에 대한 교차검증을 시행하여 최적의 모델을 선정하였다. 그리드 검색을 통하여 선정된 각 모델의 하이퍼 파라미터를 최적화하고, 국내외 데이터를 활용하여 기계학습 모델을 훈련하고 검증하였다. 이들 중 CatBoost, LGBMR, RFR, XGBoost 회귀모델이 높은 성능을 보여주었다. 특히 그 중에서 XGBoost 회귀 분석 모델이 가장 작은 오차와 높은 정확도를 보여주었다. 이들 중 오류가 가장 큰 LGBMR 모델도 이전 연구에서 제안된 신경망 및 앙상블 모델보다 성능이 우수하였다. 현장 레미콘 콘크리트에 대한 압축 강도 예측을 시행하여 학 습된 모델의 현장 적용 가능성을 확인하였다.

Low-and intermediate level waste (LILW) should be solidified and satisfy the waste acceptance criteria (WAC) to be disposed of in the LILW repository. The LILW should be uniformly solidified and should maintain its structural stability under the expected condition according to the WAC. Compressive strength of cement solidified waste should satisfy at least 3.44 MPa to be disposed of in the repository. In addition, its compressive strength should satisfy at least 3.44 MPa after the irradiation, immersion and leaching test. The compressive strength test and dimension of test specimen differ according to countries. However, measured compressive strength of solidified waste is affected by geometry of specimen and test condition. Diameter, ratio between diameter and height, and porosity are one of factors that affect to the compressive strength of cement solidified waste. Generally, specimen with larger diameter shows higher value of measured compressive strength. The ratio of height and diameter shows similar tendency to the diameter while larger porosity generally lowers the compressive strength. In other hands, higher compressive strength is expected when the loading rate is higher during the compressive strength test. U.S. is applying loading rate from ASTM C39 (0.25±0.05 MPa) for the compressive strength test while Korea is applying loading rate from KS F 2405 (0.6 MPa·s−1). France applies loading rate following FT-02-010 (0.5 MPa·s−1) for cement solidified waste. As the measured compressive strength increases when the loading rate increases, the effect of loading rate to the compressive strength of cement solidified waste should be assessed by quantification and consider its effect on the sight of regulation. In this study, the effect of geometric parameters of specimen and test condition to the compressive strength are checked by manufacturing specimen by solidifying mock sludge waste with cement. To prevent increasing amount of secondary waste, effects of ratio of height and diameter and porosity to the compressive strength are checked while diameter value is fixed. For loading rate, loading rate from ASTM C39 and KS F 2405 were compared. Existence of significant variance of measured compressive strengths of cement solidified waste are check by performing statistical analysis. Finally, by analyzing the relationship between test condition and measured compressive strength, the test method that measures the compressive strength conservatively is aimed to be derived.

Concrete masonry prisms are strengthened with steel fiber-reinforced mortar (SFRM) overlay and tested for compressive and diagonal tension strength. Masonry prisms are produced in poor condition considering standard workmanship for masonry buildings in Korea. Amorphous steel fibers are adopted for SFRM, and appropriate mixing ratios of SFRM are derived considering constructability and strength. Masonry prisms are strengthened with different fiber volume ratios, while numerous strengthened faces and additional reinforcing meshes are produced for compression and diagonal tension tests. Compression and diagonal tension strength are increased by up to 122% and 856%, respectively, and the enhancement effect for diagonal tension strength was superior compared to compression strength. Finally, the test results and strength prediction equations based on existing literature and regression analysis are compared.

The mechanical properties and microstructures of hexagonal boron nitride (h-BN)-reinforced cement composites are experimentally studied for three and seven curing days. Various sizes (5, 10, and 18 μm) and concentrations (0.1%, 0.25%, 0.5%, and 1.0%) of h-BN are dispersed by the tip ultrasonication method in water and incorporated into the cement composite. The compressive strength of the h-BN reinforced cements increases by 40.9%, when 0.5 wt% of 18 μm-sized h-BN is added. However, the compressive strength decreases when the 1.0 wt% cement composite is added, owing to the aggregation of the h-BNs in the cement composite. The microstructural characterization of the h-BN-reinforced cement composite indicates that the h-BNs act as bridges connecting the cracks, resulting in improved mechanical properties for the reinforced cement composite.