• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.7
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• pp.81-88
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• 2019

The purpose of this study is to investigate the relations between brick and stone brick pagodas in all classes of pagoda with their construction and shape. Research objects of this study are brick and stone brick pagodas of National Treasure and Treasure and masonry pagodas that are similar to brick and stone brick pagoda. This study includes checking preceding researches, drawing questions from these preceding researches, and finding answers from these questions. The results of this study are as follows. First, pagoda of Bunhwangsa Temple, the first pagoda in the Silla Dynasty, was built as a masonry pagoda, not a stone brick pagoda. Second, roofs of stone brick pagoda barrows from brick pagoda's techniques for performance of material and ease construction. Third, brick or stone brick pagodas' base have Type II that has low and extensive foundation with soil and stones usually. Forth, Korean pagodas are categorized by their materials, construction methods, and shapes. Wooden pagodas, stone pagodas, and brick pagodas are categorized by materials, post-and lintel pagodas and masonry stone pagodas are categorized by construction methods, and pitched roof pagodas and terraced roof pagodas are categorized by shapes. Fifth, masonry pagodas of Buddhism that have shape of multi-story building were developed from Doltap, traditional stone stack, and they advanced with brick pagodas and stone pagodas to terraced roof stone pagodas and post-and lintel base brick pagodas.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.7
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• pp.179-186
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• 2019

Air lime is a traditional building material of Korea. It had been used in roofs, walls, floors and masonry joints of traditional buildings until the advent of Portland cement. However, due to its low strength and durability, the lime is currently avoided as a repair or restoration material for the preservation of architectural heritage. Furthermore, due to the current practice of using hydraulic materials such as Portland cement, understanding of the material characteristics of air lime is very poor in practice. In this context, this study intended to improve the mechanical properties of the air lime mortar by reducing water contents, and also the carbonation reaction of the mortar was quantitatively evaluated to clearly understand the characteristics of this material. Accordingly, air lime mortar with a water-to-binder ratio of 0.4 was manufactured using polycarboxylate-type superplasticizer. During the 7 days of sealed curing period, the mortar did not harden at all. In other words, there was no reaction required for hardening since it could not absorb carbon dioxide from the atmosphere. However, once exposed to the air, the compressive strength of the mortar began to rapidly increase due to the carbonation reaction, and the strength increased steadily until the 28th day; after then, the strength development was significantly slowed down. On the 28th day, the mortar exhibit a compressive strength of about 5 MPa, which is equivalent to the European standard regarding strength of hydraulic lime used for preservation of architectural heritage.

• Wind and Structures
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• v.34 no.5
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• pp.395-405
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• 2022

Tornado-induced damages to high-rise buildings and low-rise buildings are quite different in nature. Tornado losses to high-rise buildings are generally associated with building envelope failures while tornado-induced damages to low-rise buildings are usually associated with structural or large component failures such as complete collapses, or roofs being torn off. While studies of tornado-induced structural damages tend to focus mainly on low-rise residential buildings, transmission towers, or nuclear power plants, the current rapid expansion of city centers and development of large-scale building complexes increases the risk of tornadoes impacting tall buildings. It is, therefore, important to determine how tornado-induced load affects tall buildings compared with those based on synoptic boundary layer winds. The present study applies an experimentally simulated tornado wind field to the Commonwealth Advisory Aeronautical Research Council (CAARC) building and estimates and compares its pressure coefficient effects against the Atmospheric Boundary Layer (ABL) flow field. Simulations are performed at the Wind Engineering, Energy and Environment (WindEEE) Dome which is capable of generating both ABL and tornadic winds. A model of the CAARC building at a scale of 1:200 for both ABL and tornado flows was built and equipped with pressure taps. Mean and peak surface pressures for TLV flow are reported and compared with the ABL induced wind for different time-averaging. By following a compatible definition of the pressure coefficients for TLV and ABL fields, the resulting TLV pressure field presents a similar trend to the ABL case. Also, the results show that, for the high-rise building model, the mean and 3-sec peak pressures are larger for the ABL case compared to the TLV case. These results provide a way forward for the code implementation of tornado-induced pressures on high-rise buildings.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.33-40
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• 2022

In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Advances in concrete construction
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• v.15 no.3
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• pp.203-213
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• 2023

New techniques, technologies, and materials should be used to design and build sports stadiums. Since this century, much progress has been made in covering the roofs of sports stadiums, and the possibility of accurate computer calculation has been provided for stadiums, so by choosing a new structure, we can double the beauty and resistance of these stadiums. A stadium has an excellent and valuable design when its structure, shell, building, materials, and joinery follow a high architectural idea at all levels and scales. This article examines the mechanical performance of polymer cement strength in the construction of football stadiums, along with their structural knowledge in the form of the best examples in the world. Portland cement is one of the most used materials for constructing football stadiums. However, its production requires spending a lot of money, wasting energy, and damaging the environment. Considering the disadvantages in the production and consumption of concrete in different environments, it is necessary to find alternative materials. It should be used with cheaper, simpler technology, abundant primary resources, energy saving, less environmental damage, and better chemical and physical properties in concrete. High-strength concrete technology is considered a new development in the construction industry of concrete structures. In hardened concrete, strength and durability are two main factors, and as the compressive strength of concrete increases, concrete becomes more brittle. As a result, its tensile strength does not increase in proportion to the increase in compressive strength and has less strain tolerance. For this reason, the need to use is evident from the fibers in high-strength concrete. Fibers are used in concrete to increase tensile strength, prevent crack propagation, and significantly increase softness. The increase with the change of these resistances depends on the strength of concrete without fibers, the shape of fibers, and the percentage of fibers. This cement is obtained from the wastes of chemical and petrochemical industries and the wastes from coal combustion, which have the properties mentioned as substitutes for Portland cement.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.157-157
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• 2021

2015년 파리에서 체결된 파리협정은 1850년 대비 2100년까지의 지구 평균기온 상승을 1.5℃ 이내로 제한하기 위해 5년마다 참여국에 상향된 온실가스 배출 감축 목표를 제출하게 하고, 탄소 배출 및 온도상승 저감 목표 달성을 위해 도시 내 그린인프라를 적극적으로 도입하는 등 국제사회 공동의 종합적인 이행을 예정하고 있다. 그린인프라의 유형 중 하나인 옥상녹화(Green Roof)는 기후변화 적응을 위한 도시 인프라 구축 방법의 하나로 국내에서도 많은 각광을 받고 있다. 옥상녹화(Green Roof)는 도시의 불투수층인 지붕 면적을 모두 혹은 일부 식생으로 덮어 표면층에 추가의 투수층을 조성하는 것을 지칭한다. 옥상녹화의 경우 별도의 토지면적 확보가 필요하지 않고 기존의 시설물에 추가적인 설치가 가능해 여분의 토지가 부족한 도심지의 녹지 확보를 위한 친환경적인 그린인프라로 각광받고 있다. 현재까지 옥상녹화(Green Roof) 관련 국내 연구 현황은 '옥상 녹화의 공법'을 다룬 비율이 높고 실증적인 결과를 가진 선행연구가 거의 없다. 따라서 본 연구는 동일한 조건하에 4개의 실험동을 설치하고 동질성 검사를 한 후 옥상에 설치된 재료[일반 콘크리트(Bare Concrete), 고반사 도장(High Reflective Paint), 사사(Short Bamboo), 잔디(Grass)]에 따른 건물 내 온도 변화 저감효과에 대한 분석을 수행하였다. 2020년 8월 17일부터 22일까지 측정된 지붕 표면 평균 최고온도 모니터링 결과를 일반 콘크리트 지붕과 비교했을 때. 고반사 도장 지붕의 경우 8.26℃, 옥상녹화(사사, short bamboo) 지붕의 경우 7.21℃, 옥상녹화(잔디, grass)의 경우 10.8℃ 낮은 것으로 측정되었다. 또한 실내 천정 표면 평균 온도의 경우 콘크리트 지붕과 비교하여 고반사 도장 지붕은 6.46℃, 옥상녹화(사사, short bamboo) 지붕은 13.52℃, 옥상녹화(잔디, grass)는 13.3℃ 낮은 것으로 나타났다. 본 연구결과는 옥상녹화의 온도저감 효과를 정량적으로 제시하고 있어, 향후 기후변화 대응 및 적응 전략적 수립에 기여할 수 있을 것으로 판단된다.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.19 no.4
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• pp.27-38
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• 2023

The Government the Republic of Korea is showing a lot of interest in net zero-energy buildings (NZEBs) to reduce energy consumption of buildings and to promote green growth policy in construction sector. The application of building passive technologies and renewable energies is essential to achieving NZEBs. Green remodeling reinforced the insulation of the exterior walls and roofs of the buildings and replaced high-efficiency windows and doors. In this study, the energy performance before and after green remodeling applied in a detached house was comparatively analyzed for baseline scenario and three different ones, ALT 1, ALT 2 and ALT 3. A building modeling and simulation software (DesignBuilder V7.0) with EnergyPlus (V9.4) calculation engine was used to calculate the energy demand and energy consumption for each scenario. Based on the calculation results of the building's energy demand for baseline, it was determined that the target building required more heating energy than cooling energy. The simulation results also showed that the implementation of building envelope performance improvement technologies (ALT 1) could notably decrease the heating energy consumption of the building. After the remodeling (ALT 1), the source energy consumption per unit floor area was assessed to be reduced by 65.2%, compared to prior remodeling of 338.7 kWh/m² -y. Meanwhile, ALT 2 can achieve energy savings of 67.7% and ALT 3 can achieve savings of 73.1%. Following completion of the remodeling project, actual construction costs, and on-site measurements and verification results will be gathered and compared with the simulation results. Additionally, economic analysis including construction costs and payback period will be conducted using actual site data.