• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.9-18
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• 2021

Recently, the rapid proliferation, introduction, and application of the fourth industrial revolution technology has emerged as a trend in the construction market. Building Information Model (BIM) technology is a multidimensional information system that forms the basis of the fourth industrial revolution technology. The river sector utilizing this information-based system is also being actively reviewed, for example, the current measures for maintenance. In recent years, active research and current work should be done to reflect the need for river experts to introduce BIM into the river field. In addition, the development of tools and support software for establishing various information systems is essential for the activation of facility maintenance information systems reflecting advanced technology and to establish and operate management plans. A study on the maintenance of river facilities involves using existing drawings to build a three-dimensional (3D) information model, check the damage utilizing it, and inform it, and utilize it as the data for maintenance reinforcement. This study involved determining a method to build a river facility without the existing information system and using the property maintenance information with 3D modeling to provide a more effective and highly utilized management plan to check maintenance operations and manage damages.

• 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 Korea Society of Computer and Information
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• v.25 no.5
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• pp.57-64
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• 2020

In this paper, based on the building's 3D modeling, a digital twin-based building control system using the collection information of wireless sensor box is proposed. The proposed system applies wireless sensors, making sensor modules more expandable and usable, and more intuitive building control possible through three-dimensional modeling. In addition, effective control and visual representation are possible through BIM data. Sensor boxes have been designed for general purpose so that a variety of sensor modules can be added and have been implemented for actual university buildings to demonstrate high availability. The results of this paper could be used to implement a digital twin control platform in the future.

• Korean Journal of Construction Engineering and Management
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• v.25 no.3
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• pp.37-46
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• 2024

Recently, 3D laser scanning technology, which can collect accurate and quick information on phenomena, has been attracting attention among smart construction technologies. 3D laser scanning technology can obtain information most similar to reality at construction sites. In this study, we would like to apply a new member identification method to an actual building and present the possibility of applying point cloud data, which can be collected using 3D laser scanning technology, to measuring progress at construction sites. In order to carry out the research, we collected location information for component identification from BIM, set a recognition margin for the collected location information, and proceeded to identify the components that make up the building from point cloud data. Research results We confirmed that the columns, beams, walls, and slabs that make up a building can be identified from point cloud data. The identification results can be used to confirm all the parts that have been completed in the actual building, and can be used in conjunction with the unit price of each part in the project BOQ for prefabricated calculations. In addition, the point cloud data obtained through research can be used as accurate data for quality control monitoring of construction sites and building maintenance management. The research results can contribute to improving the timeliness and accuracy of construction information used in future project applications.

• Journal of the Korea Institute of Building Construction
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• v.24 no.4
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• pp.447-458
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• 2024

This study investigates the effectiveness of Building Information Modeling(BIM) software, specifically SketchUp and Revit, in reducing errors during quantity take-off(QTO) for complex building elements. While 3D modeling offers advantages, existing software may not fully account for manufacturing discrepancies, such as variations in concrete cover thickness and reinforcing bar radius. To address this limitation, this research proposes a BIM-based QTO method for high-insulation wall panels with intricate details. The method utilizes a BIM family library, focusing on key parameters like concrete cover thickness and inner radius of shear reinforcement. A case study compared the cross-sectional details of a wall panel modeled in Revit with the actual manufactured specimen. The analysis revealed a 12% reduction in modeled concrete cover thickness and a 1.27 times larger modeled inner radius of the shear bar compared to the real-world values. The proposed method incorporates these manufacturing variations into the Revit model of the high-insulation wall panel. Software like Navisworks facilitates the identification and correction of any material interferences arising from these adjustments. Furthermore, the method employs a unit wall concept(1m²) to account for the volume of various materials, including insulation and splice sleeves at joints. This allows for the identification of a similar existing family within the BIM library(e.g., "Double RC wall with embedded insulation") that reflects the actual material quantities used in the wall panel. By incorporating these manufacturing-induced variations, the proposed method offers a more accurate QTO process for complex high-insulation wall panels. The "Double RC wall with embedded insulation" family within the Revit program serves as a valuable tool for material quantity estimation in such scenarios.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.107-118
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• 2021

Scan-to-BIM can be precisely mod eled by measuring structures with Light Detection And Ranging (LiDAR) and build ing a 3D BIM (Building Information Modeling) model based on it, but has a limitation in that it consumes a lot of manpower, time, and cost. To overcome these limitations, studies are being conducted to perform semantic segmentation of 3D point cloud data applying deep learning algorithms, but studies on how segmentation result changes depending on learning data are insufficient. In this study, a parametric study was conducted to determine how the size and track type of railroad tunnels constituting learning data affect the semantic segmentation of railroad tunnels through deep learning. As a result of the parametric study, the similar size of the tunnels used for learning and testing, the higher segmentation accuracy, and the better results when learning through a double-track tunnel than a single-line tunnel. In addition, when the training data is composed of two or more tunnels, overall accuracy (OA) and mean intersection over union (MIoU) increased by 10% to 50%, it has been confirmed that various configurations of learning data can contribute to efficient learning.

• Journal of KIBIM
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• v.9 no.3
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• pp.8-18
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• 2019

Precast concrete manufacturing has proved economies of scale through the repetitive production by means of standardization, automation, and prefabrication. Advanced digital design and fabrication technologies can empower its benefits by enabling mass customization in the building design and construction. This study analyzed five case studies in terms of 1) design intent and background, 2) module development and facade construction, 3) integrated process among project stakeholder. This article has attempted to establish the following three points in conclusion: 1) Form generating digital design tools such as Rhino, CATIA, Generative Component, and Digital Project were implemented to produce parametric surface pattern and rationalization to maximize existing precast manufacturing benefits. Also, BIM program has been used to promote coordination and communication among engineering consultants and contractors, 2) In addition to traditional precast concrete materials, GFRC, RFP, brick cladding precast and 3D printed mould have been introduced to reduce the weight and cost and to comply the code from the zoning, seismic, and fireproof requirements, 3) Design-assist contract, design-assist financial support, and co-location measures have been introduced to facilitate collaboration between architect, fabricator, and contractor from the beginning of the project.

• Journal of the Korean housing association
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• v.24 no.1
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• pp.69-78
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• 2013

Recently, due to the casualties and property damage caused by disasters, it became important to evacuate the victims to a safe place and come up with a space for them to inhabit for a certain period of time. Therefore, this study aims to design and develop a temporary housing system that would quickly provide a safe and comfortable living space until the displaced persons can return to a normal life again when a disaster occurs. As a result, a system of a BIM-based modular housing, a modular town through automatic placement, and a method to calculate the capacity and the total cost was developed. As this system provides the temporary housing facilities and the site in 3D, it can be utilized as a training material on a normal basis, as well as the first case material for rapid decision making when there is a disaster.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.28-36
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• 2019

The APR1400 (Advanced Power Reactor 1400MW) nuclear power plant is a large-scale national infrastructure facility with a total project cost of 8.6 trillion won and a project period of 10 years or more. The total project area is about 2.17 million square meters and consists of more than 20 buildings and structures. And the total number of drawings required for construction is about 65,000. In order to design such a large facility, it is important to establish a design standard that reflects the design intent and can increase conformity between documents (drawings). To this end, a design bases document (DBD) reflecting the design bases that extracted in regulatory requirements (e.g. 10CFR50, Korean Law, etc.) is created. However, although the design bases are important concepts that are a big framework for the whole design of the nuclear power plant, they are managed in 2-dimensional by the experts in each field fragmentarily. Therefore, in order to improve the usability of building information, we developed BIM(Building Information Model) based 3-dimensional design bases management system. For this purpose, the concept of design bases information layer (DBIL) was introduced. Through the simulation of developed system, design bases attribute and element data extraction for each DBIL was confirmed, and walls, floors, doors, and penetrations with DBIL were successfully extracted.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7902-7909
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• 2015

The needs for smart city management technology has been increased for efficiently managing huge and complex social infrastructures. Thus, this research introduces smart bridge facility management system based on sensor and BIM technologies so that the inadequacy of current facility maintenance system and the limitations of data objectivity, consistency and expandability can be improved. For which, current trends on sensor and BIM technologies and standard information system were investigated to derive system develop requirements, and a scenario was suggested to discuss how to utilize the suggested system. Research outcomes are expected to be utilized as a preliminary research for developing the real application framework for bridge facility management.