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.

본 논문은 CFRP 그리드의 정착길이에 따른 콘크리트 보의 휨거동을 평가한 실험적 연구를 보고한다. 실험을 위해 폭 250 mm, 높이 125 mm, 길이 2000 mm의 실험체가 제작되었다. 실험변수로 CFRP 그리드의 정착길이(1000 mm, 800 mm, 700 mm, 600 mm, 500 mm, 400 mm, 300 mm, 200 mm)가 고려되었다. 실험체 제작 후 3점 휨실험이 수행되었다. 실험결과 모든 실 험체의 강성은 정착길이에 영향을 받지 않고 유사한 것으로 나타났다. 초기 균열하중은 모든 실험체에서 유사하게 나타났으나, 정착길이가 감소됨에 따라 최대하중, 최대하중 시의 처짐 및 변형률은 감소하는 경향을 나타냈다. 특히 부재 길이에 80% 이상 이 정착된 경우 부재 길이와 동일한 정착길이를 가진 콘크리트 보의 약 90% 이상의 구조성능을 확보할 수 있는 것으로 확인되 었다. 제안된 수치해석 모델은 CFRP 그리드의 정착길이가 감소됨에 따른 극한하중의 저하를 유사하게 예측하였으며, 극한하중 의 오차는 평균 13.1%로 CFRP 그리드의 정착길이에 따른 콘크리트 보의 휨성능을 비교적 정확하게 예측하는 것으로 나타났다.

PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

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.

By developing molds and facilities to horizontally mold the functional part of the dry-cast concrete block, We intend to develop molds and a series of facilities to horizontally mold the functional part of the dry-cast concrete block to increase production per cycle while maintaining existing production methods and major facilities. In order to do so, CAE analysis is first required to develop molds and facilities for horizontally molding the functional part of the drycast concrete block in the horizontal direction. The procedure will be carried out by reviewing the validity of boundary conditions and physical properties, 3D modeling, grid generation, construction of analysis models, model validity, analysis according to frequency changes, and analysis according to physical properties. First, through the comparison of two-point support, three-point support, and two-point and three-point support in the constraint conditions, We would like to compare it with the actual molded product in the horizontal direction. But first of all, it is considered two-point support in the constraint conditions in this paper.

This paper describes the seismic performance evaluation of reinforced concrete bridge columns under constant and varying axial forces. For this purpose, nine identical circular reinforced concrete columns were designed seismically by KIBSE (2021) and KCI (2021). A comparison of lateral forces with theoretical strength shows that the safety factor for columns under varying axial forces is less marginal than those under constant axial forces. In addition, columns under varying axial forces exhibit significant fluctuations in the hysteretic response due to continuously varying axial forces. This is particularly prominent when many varying axial force cycles within a specific lateral loading cycle increase. Moreover, the displacement ductility of columns under varying axial forces does not meet the code-specified required ductility in the range of varying axial forces. All varying axial forces affect columns' strength, stiffness, and displacement ductility. Therefore, axial force variation needs to be considered in the lateral strength evaluation of reinforced concrete bridge columns.

The current study investigates the seismic performance of shear-dominant RC columns retrofitted with iron-based shape memory alloy (Fe SMA). Three RC columns with insufficient transverse reinforcement were designed and fabricated for lateral cyclic loading tests. Before testing, two specimens were externally confined with carbon fiber-reinforced polymer (CFRP) sheets and self-prestressed Fe SMA strips. The test results showed that both CFRP and Fe SMA performed well in preventing severe shear failure exhibited by the unretrofitted control specimen. Furthermore, the two retrofitted specimens showed ductile flexural responses up to the drift ratios of ±8%. In terms of damage control, however, the Fe SMA confinement was superior to CFRP confinement in that the spalling of concrete was much less and that the rupture of confinement did not occur.

The Wolsong Nuclear Power Plant (NPP) operates an on-site spent fuel dry storage facility using concrete silo and vertical module systems. This facility must be safely maintained until the spent nuclear fuel (SNF) is transferred to an external interim or final disposal facility, aligning with national policies on spent nuclear fuel management. The concrete silo system, operational since 1992, requires an aging management review for its long-term operation and potential license renewal. This involves comparing aging management programs of different dry storage systems against the U.S. NRC’s guidelines for license renewal of spent nuclear fuel dry storage facilities and the U.S. DOE’s program for long-term storage. Based on this comparison, a specific aging management program for the silo system was developed. Furthermore, the facility’s current practices—periodic checks of surface dose rate, contamination, weld integrity, leakage, surface and groundwater, cumulative dose, and concrete structure—were evaluated for their suitability in managing the silo system’s aging. Based on this review, several improvements were proposed.

PURPOSES : There has been increasing interest in South Korea on warm-mix asphalt (WMA) and cold-mix asphalt (CMA) technologies that allow production of asphalt pavement mixtures at comparatively lower temperatures than those of hot-mix asphalt (HMA) for use in pavement engineering. This study aims to evaluate the feasibility of replacing HMA pavement with WMA pavement with the goal of reducing CO2 emissions associated with asphalt production for road construction. METHODS : Changes in the dynamic modulus characteristics of WMA and HMA according to short-term and long-term aging were evaluated. In addition, the effects of water damage were evaluated for short- and long-term aging stages. RESULTS : For WMA, in the process of mixing and short-term aging, early-age dynamic modulus decreased owing to low temperature and reduced short-term aging (STA) time. This could result in early damage to the asphalt pavement depending on the applied traffic load and environmental load. CONCLUSIONS : Mastercurves of the dynamic modulus were used for comparative analysis of WMA and HMA. Compared to the dynamic modulus after STA of HMA, the estimated aging time determined by experiments for WMA to achieve the required stiffness was more than 48 hours, which is equiva-lent to approximately 4 to 5 years real service life when converted. It is considered that further studies are needed for performance optimization to achieve early-age performance of the asphalt mixes.

MCCI, one of the ex-vessel phenomena during a severe accident, is generally caused by mutual reactions between molten core and reactor building basemat concrete, but occurs between sacrificial materials made by concrete and corium in APR nuclear power plant with PECS applied. In this paper, MCCI analysis was performed to design and apply a device to block the core melt at the junction connected to the ICI cavity sump from the core catcher installed in the reactor cavity of the APR. Unlike the sacrificial material, which has a cooling effect by inducing erosion of the ex-vessel core melt, the device is composed of concrete applied as a barrier to protect the ICI cavity sump. The decent thickness of the barrier wall of the device was calculated.

PURPOSES : In this study, heating concrete is developed using heating artificial binder, which is more conductive and less expensive than ordinary Portland cement, and the heating effect is verified through laboratory tests and numerical analysis. METHODS : Based on the test results, the range of heat influence of the Heating concrete is calculated through numerical analysis. As a result of the laboratory test, the temperature rises to 58℃ after 10 minutes when heat generation started at the outdoor temperature of 12℃ and the initial temperature of the concrete specimen of 19.1℃. RESULTS : The heating effect is up to 50 cm in width and 90 cm in height centered on the heating concrete through numerical analysis to analyze the influence range of the Heating concrete based on the laboratory test results. However, when the distance from the heating concrete is greater than about 20 cm, the influence becomes very small, and the rate of temperature decrease drops significantly. CONCLUSIONS : From the test and numerical analysis, it can be used as an eco-friendly heating material suitable for concrete pavements.

본 연구는 추가적인 장비 없이 UAV만을 사용한 균열폭 측정 및 균열의 3차원 재구성 방법을 제안한다. UAV 사진측량법 및 CNN을이용한 균열의 3차원 재구성 및 균열폭 측정 검증을 위해 5곳의 균열이 존재하는 벽면을 대상으로 균열의 3차원 재구성을 하였 으며 UAV와 균열 사이의 거리 4가지에 대해 균열폭을 측정하고 균열 현미경 측정값과 비교하여 정확성을 검증하였다. 대부분의 균열 에서 균열폭을 정확히 측정하였으나 균열폭이 0.5mm보다 작은 경우와 벽면이 심하게 그늘져 어두운 곳에서는 측정 유효성이 떨어지 는 결과를 보였다.

The asphalt concrete industry, accounting for >90% of road pavement, is a crucial contributor to construction waste. This study focuses on the recycling of asphalt concrete recycled aggregates, which currently exhibits a low rate. We investigated the application of these aggregates, combined with hardener and mixing water, in the development of ecofriendly road base materials using circular aggregates. Results revealed that the 13-mm asphalt concrete recycled aggregates met all quality standards. However, the 25-mm aggregates did not conform to the reclaimed asphalt content standard; however, they met other quality standards. Moreover, the experimental results for the hardener and mixing water indicated compliance with all quality standards.

Fiber-reinforced polymer (FRP) exhibits superior tensile strength and corrosion resistance compared to steel but has a lower elasticity. Recently, researchers have addressed this by proposing composite sections of FRP and concrete. To ensure the intended composite behavior, these FRP–concrete sections should exhibit sufficient stress transfer between the two elements through a shear connection. Herein, various shear connection methods were proposed to improve the composite behavior of glass fiber–reinforced polymer (GFRP) plates and concrete. Through push-out tests, the behavior characteristics of the prepared specimens were analyzed. The findings confirm that an FRP shear key (FSK) with a small cross-section resists high shear stresses, making it suitable for sections vulnerable to damage from bolt drilling. Additionally, combining an FSK with bolts as shear connectors on a GFRP plate proves beneficial in preventing the fracture of the plate and improving the shear resistance.

This study reports an analytical investigation on the development of SB4-grade separated concrete median barriers. The proposed barrier sections comprise three designs, with heights of 810, 1000, and 1270 mm and upper widths of 90, 120, and 120 mm, respectively. Before conducting collision analyses on the proposed sections, the considered collision analysis model was validated using real collision test results; the model was found to accurately predict the real collision test results. The proposed cross-sections were modeled to perform collision analysis according to SB4-grade collision conditions. Results indicated that increasing the cross-section height increased the damaged area and decreased the strength, while the occupant protection performance remained mostly unaffected. Furthermore, the proposed cross-sections met the strength and occupant protection performance criteria specified in domestic guidelines, suggesting their suitability as a separated concrete median barrier for bridges.

This study reports an experimental and analytical exploration of concrete columns laterally confined with Fe-based shape-memory alloy (Fe-SMA) spirals. For performing experiments, Fe-SMA rebars with a 4% prestrain and diameter of 10 mm were fabricated and concrete columns with internal Fe-SMA spiral reinforcement were constructed with a diameter of 200 mm and height of 600 mm. An acrylic bar with an attached strain gauge was embedded in the center of the specimen to measure local strains. Experimental variables encompassed the Fe-SMA spiral reinforcement, spacing, and activation temperature. Uniaxial compression tests were conducted after applying active confinement to the concrete columns through electrical-resistance heating. Notably, as the Fe-SMA spiral spacing decreased, the local failure zone length and compressive fracture energy of the prepared specimens increased. Additionally, a model incorporating compressive fracture energy was proposed to predict the stress–strain behavior of the. This model, accounting for active and passive confinement effects, demonstrated accurate predictions for the experimental results of this study as well as for previously reported results.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

Chelating agents, such as EDTA, NTA, and citric acid, possess the capacity to establish complexes with radionuclides, potentially enhancing the migration of such radionuclides from the disposal sites. Hence, quantification of these chelating agents in radioactive wastes is required to ensure secure disposal protocols. The determination of chelating agents in radioactive wastes is mainly composed of two steps, e.g. extraction and detection. However, there are little information on the extraction of the chelators in various radioactive wastes. We endeavored to optimize the extraction conditions for citric acid (CA) found within concrete, a prevalent component in the context of dismantled waste materials. Given the inherent high solubility of CA in water, we applied an aliquot of deionized water to the concrete and conducted a one-hour ultrasonic leaching procedure to facilitate chelate extraction. Subsequently, following the preparation of the concrete leachate via vacuum filtration and centrifugation to yield a clarified solution, we quantified the concentration of CA within the solution using Ion Chromatography (IC). To enhance leaching efficiency, we examined the % recovery variation with respect to the pH of the leaching solution. The optimized extraction method will be applied to diverse chelating agents and radioactive waste categories.