• Journal of the Korea Institute of Building Construction
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• v.22 no.2
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• pp.171-182
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• 2022

The regular assessment of a building is important to understand structural safety and latent risk in the early stages of building life cycle. However, methods of traditional assessment are subjective, atypical, labor-intensive, and time-consuming and as such the reliability of these results has been questioned. This study proposed a method to bring accurate results using a 3D laser scanner and integrate them in Building Information Modeling (BIM) to visualize defective condition. The specific process for this study was as follows: (1) semi-automated data acquisition using 3D laser scanner and python script, (2) scan-to-BIM process, (3) integrating and visualizing defective conditions data using dynamo. The method proposed in this study improved efficiency and productivity in a building assessment through omitting the additional process of measurement and documentation. The visualized 3D model allows building facility managers to make more effective decisions. Ultimately, this is expected to improve the efficiency of building maintenance works.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.369-376
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• 2021

3D architectural visualization animation has become more important in architectural presentations due to the rapid development of digital technology. Unlike games and movies, architectural visualization animation most focuses on delivering visual information, and aims to express the sense of space that viewers feel in an architectural space, rather than simply providing an image of viewing buildings. The sense of space is affected not only by physical elements of architecture, but also by immaterial elements such as light, time, and human actions, and it is more advantageous to express it in animations that can contain temporality compared to a fixed image. Therefore, the purpose of this study is to search for elements to effectively convey a sense of space in architectural visualization animation. To this end, the works of renowned architectural visualization artists that are open to the public were selected and observed to search for elements to effectively convey a sense of space to viewers. The elements that convey the sense of space that are common to the investigated architectural animations can be classified into the movement and manipulation of the camera, the movement of surrounding objects, the change of the light environment, the change of the weather, the control of time, and the insertion of a surreal scene. It will be followed by a discussion on the immersion of architectural contents.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.2
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• pp.117-131
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• 2007

Representation of building internal space is an active research area as the need for more geometrically accurate and visually realistic increases. 3 dimensional representation is common ground of research for disciplines such as computer graphics, architectural design and engineering and Geographic Information System (GIS). In many cases CAD plans are the starting point of reconstruction of 3D building models. The main objectives of building reconstruction in GIS applications are visualization and spatial analysis. Hence, CAD plans need to be preprocessed and edited to adapt to the data models of GIS SW and then georeferenced to enable spatial analysis. This study automated the preprocessing of CAD data using AutoCAD VBA (Visual Basic Application), and the processed data was topologically restructured for further analysis in GIS environment. Accuracy of georeferencing CAD data was also examined by comparing the results of coordinate transformation by using digital maps and GPS measurements as the sources of ground control points. The reconstructed buildings were then applied to visualization and network modeling.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.305-310
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• 2015

Recently, as AR(Augmented Reality) technology develops, it is used in field of diverse industry and specially affects structures and human interaction in field of architecture. This paper proposes 3D visualization application for realistic experience of structures by using outdoor AR technology. Proposed application visualizes structures such as high buildings, bridges, ships, and so on to be constructed in future, considering ambient environment by using outdoor AR technology, provides precisely user structures after completing construction and offers more realistic information and immersion as compared with previous methods.

• Korean Architects
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• no.10 s.223
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• pp.22-23
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• 1987

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.13-18
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• 2017

Recently, the form of traditional families has been disintegrated due to low birthrate, aging, declining marriage, individualism, etc. In particular, the number of single households has increased due to the shift to a low-growth advanced economic structure, women's social participation, diversification of lifestyles, and so on. According to the National Statistical Office, the number of single households living alone by 2015 is estimated to be about 5,060,000 households, which is estimated to account for 34.3% of all households, which has greatly increased compared with about 660,000 (6.9%) in 1985. However, the housing market has not been able to respond to such social changes. Therefore, in this research, we presented a plan to visualize the public data of single household in Seoul city and prediction result of occupancy shape for the purpose of supporting decision making of single household consumers.

• Journal of KIBIM
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• v.14 no.2
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• pp.13-24
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• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.57-68
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• 2023

To maintain the normal function of an aging building, regular inspection, repair, and reinforcement must be performed. However, the form of information and decision-making generated in the current building maintenance stage still depends on inefficient methods. This causes an increase in cost and waste of time in building maintenance. Therefore, this study proposed a COBie-IM document for building inspection and maintenance information management by benchmarking COBie, and suggested a method of checking information by linking it with BIM. In addition, a method for visualizing the designation of colors according to the damage grade of defects occurring in the building was suggested. Through this, we are able to systematically integrate and manage the information generated during inspection and maintenance in the building maintenance stage. This is expected to increase work efficiency by supporting decision-making that determines the priority of repair and reinforcement for defects.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.93-103
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• 2017

There has been various pedestrian-friendly planning for making walkable cities. However, the representation of urban pedestrian spaces that should be the basis of the pedestrian-friendly planning tends to be far from reality. This is due to the absence of a consensual way to represent pedestrian spaces in the city. In this context, this study aims to propose a method to properly represent pedestrian spaces. For this purpose, this study first reviews the patterns of representing pedestrian spaces appearing on city maps and examines their merits and limits. After that, the criteria of pedestrian traversability and the mapping method are proposed on a trial basis for representing pedestrian spaces. Then, applying this to the case sites, Mokdong and Euljiro, this paper demonstrates how the operation of representing pedestrian spaces works. It is expected that the results of this study would be used as the basic foundation for a more developed representation of effective pedestrian-friendly planning.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.73-74
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• 2022

Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the 'InceptionV3'-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.