• Journal of Korean Society of Steel Construction
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• v.25 no.5
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• pp.509-518
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• 2013

This study is aimed at developing of the sustainable interface module for structural design. It is dealt with the Midas/Gen for analysis and design and Tekla Structures for Building Information Modeling. In this research, it is improved the applicability and function of the existing interface module developed by authors. Although model information was functionally well linked by the existing module in both directions, the applicability is limited due to the difference of attributes between structural analysis model and structural detail model. In this study, we analyzed the problems that occur in existing module. We have developed the interface module to solve the problems and improved the applicability of the existing module.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.339-346
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• 2018

An Industry Foundation Classes(IFC), which is a data schema developed focusing on architecture, is being expanded to civil engineering structures. However, it is difficult to create an information model based on extended IFC since the BIM software cannot provide support functions. To manage a railway track based on the extended IFC, this paper proposed a method to create an alignment-centered separated railway track model and convert it to an extended IFC-based information model. First, railway track elements have been classified into continuous and discontinuous structures. The continuous structures were created by an alignment-based software, and discontinuous structures were created as independent objects through linkage of the discretized alignment. Second, a classification system and extended IFC schema for railway track have been proposed. Finally, the semantic information was identified by using the property of classification code and user interface. The availability of the methods was verified by developing an extended IFC-based information model of the Osong railway site.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1075-1084
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• 2022

The use of Hi-Tech in cultural heritage preservation and the promotion of cultural heritage values in general, particularly artifacts, opens new opportunities for attracting tourists while also posing a challenge due to the need to reward high-quality excursions to visitors historical and cultural values. Building Information Modeling (BIM) and Hi-Tech in new building management have been widely adopted in the construction industry; however, Historic Building Information Modeling (HBIM) is an exciting challenge in 3D modeling and building management. For those reasons, the Scan-to-HBIM approach involves generating an HBIM model for existing buildings from the point cloud data collected by Terrestrial 3D Laser Scanner integrated with Virtual Reality (VR), Augmented Reality (AR), contributes to spatial historic sites simulation for virtual experiences. Therefore, this study aims to (1) generate the application of Virtual Reality, Augmented Reality to Historic Building Information Modeling - based workflows in a case study which is a monument in the city; (2) evaluate the application of these technologies to improve awareness of visitors related to the promotion of historical values by surveying the experience before and after using this application. The findings shed light on the barriers that prevent users from utilizing technologies and problem-solving solutions. According to the survey results, after experiencing virtual tours through applications and video explanations, participant's perception of the case study improved. When combined with emerging Hi-Tech and immersive interactive games, the Historic Building Information Modeling helps increase information transmission to improve visitor awareness and promote heritage values.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.181-189
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• 2021

In order to increase the practical usability of the virtual reality(VR)-based BIM object in the construction site, the difference between the virtual image and the real image should be resolved, and when it is applied to the construction schedule management function, it is necessary to reduce the image gap between the virtual completion and the actual completion. In this study, in order to solve this problem, a prototype of 4D model is developed in which augmented reality (AR) and 3D printing technologies are linked, and the practical usability of a 4D model linked with two technologies is verified. When a schedule simulation is implemented by combining a three-dimensional output and an AR object, it is possible to provide more intuitive information as a tangible image-based schedule information when compared to a simple VR-based 4D model. In this study, a methodology and system development of an AR implementation system in which subsequent activities are simulated in 4D model using markers on 3D printing outputs are attempted.

• 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 Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.35-41
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• 2020

3D laser scanners are an effective way to quickly acquire a large amount of data about an object. Recently, it is used in various fields such as surveying, displacement measurement, 3D data generation of objects, construction of indoor spatial information, and BIM(Building Information Model). In order to utilize the point cloud data acquired through the 3D laser scanner, it is necessary to make the data acquired from many stations through a matching process into one data with a unified coordinate system. However, analytical researches on the accuracy of point cloud data according to the registration method are insufficient. In this study, we tried to analyze the accuracy of registration method of point cloud data acquired through 3D laser scanner. The point cloud data of the study area was acquired by 3D laser scanner, the point cloud data was registered by the ICP(Iterative Closest Point) method and the shape registration method through the data processing, and the accuracy was analyzed by comparing with the total station survey results. As a result of the accuracy evaluation, the ICP and the shape registration method showed 0.002m~0.005m and 0.002m~0.009m difference with the total station performance, respectively, and each registration method showed a deviation of less than 0.01m. Each registration method showed less than 0.01m of variation in the experimental results, which satisfies the 1: 1,000 digital accuracy and it is suggested that the registration of point cloud data using ICP and shape matching can be utilized for constructing spatial information. In the future, matching of point cloud data by shape registration method will contribute to productivity improvement by reducing target installation in the process of building spatial information using 3D laser scanner.

• Journal of KIBIM
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• v.11 no.2
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• pp.43-53
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• 2021

The 'public agency oil joint purchase system' was introduced to lower public sector oil prices and contribute to the stability of the overall consumer oil market. The present study used spatial regression to analyze the factors affecting domestic gasoline price, focusing on the impact of potential implicit collusion among gas stations in determining domestic gasoline prices. Also, this study investigated the effect the location characteristics of the market convention gas stations and government offices on the pressure of price competition in the market and the gasoline price at general gas stations. To summarize the results of the spatial lag model (SLM), the individual characteristics of gas stations such as convenience stores (+), self-fuelling (-), commercial areas (+), subway stations (+), population density (-), and sales (-) are correlated to gasoline prices at gas stations, and the institutional location factors of gas stations (+) affected the average of 9 won per liter, 11 won per liter. In order to solve these problems, the establishment of a monitoring system reflecting the location characteristics of the region and the ongoing review of the system should be carried out. In addition, separate, expanded and promotional measures should be prepared for the convenience of general and public oil buyers.

• 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.6 no.2
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.