• Journal of KIBIM
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• v.9 no.4
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• pp.51-61
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• 2019

In the general manufacturing sector, prototyping used to reduce the risks that can arise with new conceptual products. However, in AEC area, it does not mass-produce a building, so the prototype itself becomes a building. Therefore, it is challenging to have prototyping of the same scale as the real thing, and the prototyping process in architecture is very inefficient. The prototyping process in the design stage typically assumes making a scaled model, partial model, or digital model. However, it is difficult for these models to correspond to the actual building and the environment of time and space such as scale, material, environment, load, physical properties and deformation, corrosion, etc., unlike the actual building. When using the digital twin concept in the prototyping process, it is possible to measure performance from the design stage to the operation stage. The digital twin was found by a method for monitoring based on physical twins and real-time linkage in the operation stage. Therefore, if the digital twin concept is applied at the design stage, it is possible to predict performance using not only current performance but also history information using real-time information. In order to apply the digital twin concept to the prototyping design process, we analyze the theoretical considerations and the prototyping design process of the digital twin, analyze the cases and research results where the prototyping design was applied, Provide an applied prototyping design process. The proposed process is tested through a pilot project and analyzed for potential use.

• Molecules and Cells
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• v.45 no.4
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• pp.216-230
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• 2022

SERTA domain-containing protein 1 (Sertad1) is upregulated in the models of DNA damage and Alzheimer's disease, contributing to neuronal death. However, the role and mechanism of Sertad1 in ischemic/hypoxic neurological injury remain unclear. In the present study, our results showed that the expression of Sertad1 was upregulated in a mouse middle cerebral artery occlusion and reperfusion model and in HT22 cells after oxygen-glucose deprivation/reoxygenation (OGD/R). Sertad1 knockdown significantly ameliorated ischemia-induced brain infarct volume, neurological deficits and neuronal apoptosis. In addition, it significantly ameliorated the OGD/R-induced inhibition of cell viability and apoptotic cell death in HT22 cells. Sertad1 knockdown significantly inhibited the ischemic/hypoxic-induced expression of p-Rb, B-Myb, and Bim in vivo and in vitro. However, Sertad1 overexpression significantly exacerbated the OGD/R-induced inhibition of cell viability and apoptotic cell death and p-Rb, B-Myb, and Bim expression in HT22 cells. In further studies, we demonstrated that Sertad1 directly binds to CDK4 and the CDK4 inhibitor ON123300 restores the effects of Sertad1 overexpression on OGD/R-induced apoptotic cell death and p-Rb, B-Myb, and Bim expression in HT22 cells. These results suggested that Sertad1 contributed to ischemic/hypoxic neurological injury by activating the CDK4/p-Rb pathway.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Journal of KIBIM
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• v.8 no.4
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• pp.23-33
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• 2018

Recently, the industry actively adopts the cutting-edge technologies of the fourth industrial revolution and uses them to enhance the productivity and service of mass-customization. The manufacturing industry is creating new processes and business models by achieving digital transformations through a lean start-up approach aimed at achieving the highest customer satisfaction with minimal resources. Although attempts are made to manufacture the building by introducing the latest technology in architecture, it is applied sporadically, not as an integrated system, in the entire phase of the architectural project. This paper analyzes the changes in the construction industry through the application of core technologies of the fourth industrial revolution. Design processes are analyzed for the digital transformation of the construction industry by case study of advanced architectural design practice. A novel design concept model 'Architectural lean startup' is proposed by combining the architectural process and the lean start up method. Through the design of the bus stop based on the architectural lean startup concept, it is confirmed that the designer repeats the 'Generate-Test-Analysis' to develop the design and generate the final result.

• Journal of KIBIM
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• v.11 no.1
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• pp.39-46
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• 2021

Urban residents in crowded complexes are making increasing civil complaints about noise and demanding pleasant and comfortable residential environments. Because noise is one of the most important factors related to urban residents' dissatisfaction with their living environments, the present study investigates the direct and indirect effects of noise-related outdoor environmental factors on residential level satisfaction, using noise level data from 29 noise-measuring stations in Seoul. From 62 multi-family apartment complexes near these stations, the authors collected GIS-based environmental attribute data, as well as survey data including the residents' personal characteristics and indicators designed to measure latent psychological characteristics: noise sensitivity and residential noise level satisfaction. This study then utilized structural equation models to analyze the direct variables influencing the latent variables of noise sensitivity and residential noise level satisfaction, as well as the complex relationships among all variables. The result showed that residents who are exposed to less noise, possibly due to living in apartments facing relatively quiet roads, protected by soundproof walls, or surrounded by densely planted trees, tend to be less noise sensitive, which makes them more satisfied with the ambient noise level. Therefore, critical outdoor environmental variables can be used to reduce noise sensitivity and improve residential noise level satisfaction.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• Journal of KIBIM
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• v.12 no.2
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• pp.1-11
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• 2022

Road traffic accidents (RTAs) are the leading cause of accidental death among children. RTA reduction is becoming an increasingly important social issue among children. Municipalities aim to resolve this issue by introducing "Smart Pedestrian Crosswalks" that help prevent traffic accidents near children's facilities. Nonetheless such facilities tend to be installed in relatively limited number of areas, such as the school zone. In order for budget allocation to be efficient and policy effects maximized, optimal location selection based on machine learning is needed. In this paper, we employ machine learning models to select the optimal locations for smart pedestrian crosswalks to reduce the RTAs of children. This study develops an optimal location index using variable importance measures. By using k-means clustering method, the authors classified the crosswalks into three types after the optimal location selection. This study has broadened the scope of research in relation to smart crosswalks and traffic safety. Also, the study serves as a unique contribution by integrating policy design decisions based on public and open data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.38-46
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• 2017

In this study, a query language was developed to supplement the information retrieval and model regeneration in the case of Industry Foundation Classes (IFC)-based civil infrastructure information models. First, the IFC objects to represent the structural components, entities to manage the related properties, and relationships to connect with the mentioned elements were analyzed in a point of information flow. The results confirmed that the end-users could have problems with access and comprehend the properties and its relationships in the IFC file. Second, the IfcPropertySet-focused query method and applicable stand-alone module were proposed referring to the previous Building Information Model Query Language (BimQL). The availabilities of the proposed method were examined using the rail and sleeper information models through information retrieval and model regeneration. The most important advantage of the proposed approach is the IFC-based information retrievals that can guarantee the interoperability between software packages.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.289-296
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• 2014

The rebar detailing is an important work influencing the final performance and quality of RC structures. But it is one of the most irrational and illogical activity in construction site. Many groups of workers, including main contractors, structural engineers, shop drawers, rebar fabricators, and etc., participate in this activity. A loosely-organized process for this activity is apt to produce a big amount of rebar loss or even degraded structures. A 3-dimensional rebar auto-placing system, called as Rebar Hub, has been designed and implemented in this research. Rebar Hub provides a totally integrated service from 3D structural modeling of buildings to rebar auto-placing considering anchorage, splice, and the length of ordered rebar. In addition, Rebar Hub can recognize the 2D drawing CAD files and then build 3D structural models which are used for the start point of 3D rebar auto-placing. After rebar auto-placing, each members of the 3D structural model have rebar information belonging to them. It means that the rebar information can be used for the afterward works such as quantity-survey, manufacturing and fabrication of rebars. Rebar Hub is showing outstanding performance while applying to practical projects. It has almost five times productivity and reduces the rebar loss up to 3~8% of the initially-surveyed amount of rebar.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.14-26
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• 2018

The Biotope Area Ratio (BAR) is a quantitative pre-planning index for sustainable development and an integrated indicator for the balanced development of buildings and outdoor spaces. However, it has been pointed out that there are problems in operations management: errors in area calculation, insufficiency in the underground soil condition and depth, reduction in biotope area after construction, and functional failure as a pre-planning index. To address these problems, this study proposes implementing LIM. Since the weights of the BAR are mainly decided by the underground soil condition and depth with land cover types, the study focused on the terrain and pavements. The model should conform to BIM guidelines and standards provided by government agencies and professional organizations. Thus, the scope and Level Of Detail (LOD) of the model were defined, and the method to build a model with BIM software was developed. An apartment complex on sloping ground was selected as a case study, a 3D terrain modeled, paving libraries created with property information on the BAR, and a LIM model completed for the site. Then the BAR was calculated and construction documents were created with the BAR table and pavement details. As results of the study, it was found that the application of the criteria on the BAR and calculation became accurate, and the efficiency of design tasks was improved by LIM. It also enabled the performance of evidence-based design on the terrain and underground structures. To adopt LIM, it is necessary to create and distribute LIM library manuals or templates, and build library content that comply with KBIMS standards. The government policy must also have practitioners submit BIM models in the certification system. Since it is expected that the criteria on planting types in the BAR will be expanded, further research is needed to build and utilize the information model for planting materials.