• KIEAE Journal
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• 제13권4호
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• pp.33-41
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• 2013

The impervious area on the surface of urban area has been increased as buildings and artificial land cover have continually been increased. Urban development has gradually decreased the green zone in downtown and alienated the city from the natural environment on outskirt area devastating the natural ecosystem. There arise the environmental problems to urban area including urban heat island phenomenon, urban flood, air pollution and urban desertification. As one of urban plans to solve such problems, green roof system is attracting attentions. The purpose of this study was to investigate flood discharge and heat reduction effect according to the green roof system and to quantify effect by analyzing through simulation water and heat cycle before and after green roof system. For the analysis, Distributed hydrologic model, WEP (Water and Energy transfer Processes) and WEP+ model were used. WEP was developed by Dr. Jia, the Public Works Research Institute in Japan (Jia et al., 2005), which can simulate water and heat cycle of an urban area with complex land uses including calculation of spatial and temporal distributions of water and heat cycle components. The WEP+ is a visualization and analysis system for the WEP model developed by Korea Institute of Construction Technology (KICT).

• 한국건축시공학회지
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• 제23권3호
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• pp.273-284
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• 2023

건설프로젝트 진행 시 골조공사 후 시공상태를 점검하는 업무가 필수적이며, 이에 골조의 수직 및 수평 정확도를 점검하고 결함에 대한 보수작업을 수행한다. 하지만 기존의 업무방식은 점검자의 주관적 판단 및 인적오류의 발생 가능성으로 인한 신뢰성 문제, 수작업으로 인한 인력 및 시간 소모적 문제 등이 존재한다. 이에 본 연구는 상기 문제점을 해결하고, 골조공사 시공상태 점검 및 결과공유 과정의 효율을 높이고자 LiDAR 및 AR 기술의 활용방안을 제안하였다. 본 연구에서는 LiDAR를 통해 골조의 3D Point Cloud 데이터를 취득하여 시공상태 점검에 적용하는 방안과, 점검결과 데이터를 BIM 모델에 입력 후, AR을 통해 시각화하는 방안을 제안하였다. 이는 기존 방식 대비 점검과정의 객관성, 결과공유의 신속성 및 정확도 측면에서 효율적인 방식임을 확인하였으며, 더불어 건설프로젝트 전체의 품질 및 생산성 향상에 기여할수 있을 것으로 기대된다.

• 국제학술발표논문집
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• The 7th International Conference on Construction Engineering and Project Management Summit Forum on Sustainable Construction and Management
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• pp.24-31
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• 2017

As the concept of sustainability becomes more and more popular, a large amount of literature have been recorded recently on intelligent green building and smart city (IGB&SC). It is therefore needed to systematically analyse the existing knowledge structure as well as the future new development of this domain through the identification of the thematic trends, landmark articles, typical keywords together with co-operative researchers. In this paper, Citespace software package is applied to analyse the citation networks and other relevant data of the past eleven years (from 2006 to 2016) collected from Web of Science (WOS). Through this, a series of professional document analysis are conducted, including the production of core authors, the influence made by the most cited authors, keywords extraction and timezone analysis, hot topics of research, highly cited papers and trends with regard to co-citation analysis, etc. As a result, the development track of the IGB&SC domains is revealed and visualized and the following results reached: (i) in the research area of IGB&SC, the most productive researcher is Winters JV and Caragliu A is most influential on the other hand; (ii) different focuses of IGB&SC research have been emerged continually from 2006 to 2016 e.g. smart growth, sustainability, smart city, big data, etc.; (iii) Hollands's work is identified with the most citations and the emerging trends, as revealed from the bursts analysis in document co-citations, can be concluded as smart growth, the assessment of intelligent green building and smart city.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• 한국BIM학회 논문집
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• 제4권4호
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• pp.1-12
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• 2014

As interest for visualization and real time processing of construction management information was heightened recently, a diversity of management tools have been developed and corresponding systems also have been changed. In the case of BIM(Building Information Modeling) which is being spotlighted as such kind of management tool, its outcome such as technical innovation and time saving through overseas success case, reducing effect of unnecessary work, and so forth has continuously been established. The BIM in which various information related with life cycle of project are managed with the base of 3D object is useful as an information management tool of construction project which becomes complex and large in its scale. The aim of this paper is analysis or reliability on BIM design for expressway. To solve these issues, this study analyzes the design document, road design handbook, guide. Also this paper performed analysis of reliability 2D vs. BIM quantity calculation. This pilot projects "BIM design at Daegu expressway Lot No.5" lays the foundation for future work on BIM based civil engineering design.

• 한국BIM학회 논문집
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• 제7권2호
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• pp.8-15
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• 2017

Building Information modeling is playing an important role in transforming the construction industry. It helped the industry with better visualization, minimum design errors, and excellent planning of the construction activities. Time and cost saving can be effectively achieved by using BIM for any construction project. It improves information exchange between all the project stakeholders. However, the development of earthwork 3D BIM is still underway and has not been fully implemented yet. This paper presents the study of a complete process for Earthwork BIM design using Autodesk Civil 3D. A real site road construction project is used as a case study to explain the process of earthwork modeling, starting from laser scanning to 3D model. Quantity take off calculation is very important part of any road construction project so during this study earthwork volume from two 3D earthwork model is calculated. The results obtained through this study will be the basis for future work which has been concluded in this paper.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.54-59
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• 2021

In the process of designing a building, the process of expressing the designer's ideas through images is essential. However, it is expensive and time consuming for a designer to analyze every individual case image to generate a hypothetical design. This study aims to visualize the basic design draft sketch made by the designer as a real image using the Generative Adversarial Network (GAN) based on the continuously accumulated architectural case images. Through this, we proposed a method to build an automated visualization environment using artificial intelligence and to visualize the architectural idea conceived by the designer in the architectural planning stage faster and cheaper than in the past. This study was conducted using approximately 20,000 images. In our study, the GAN algorithm allowed us to represent primary materials and shades within 2 seconds, but lacked accuracy in material and shading representation. We plan to add image data in the future to address this in a follow-up study.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.135-136
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• 2022

Defects caused by apartment houses have the term of warranty liability according to the enforcement ordinance of Acts of the Management of Apartment Houses. In case when defects occur during the term, free defect maintenance can be provided from the construction company. Yet, there occur conflicts between the construction company and residents, as to whether there occur defects or not. To resolve these conflicts, this study aimed to analyze construction classification and types that need managing, based on defects of apartment houses occurring during the term of warranty liability. This research analyzed 138,576 cases of data, as of five apartment house complexes. For the construction classification for defects of apartment houses, wooden flooring products accounted for the highest rate, followed by paper hanging, and wooden window. For the construction types of defects, torn/scratching took up with the highest rate, followed by the condition of defect in fixing and operating. In order to embody defects occurring during the term of warranty liability, into the visualization technique, this researcher utilized the word cloud method. This study will pursue the method for maintaining defects during the term of warranty liability, in the subsequent research, using the data that this research presented.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.13-22
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• 2020

Reinforcement steel fixing is a skilled and manually intensive construction trade. Current practice for the quality assessment of reinforcement steel fixing is normally performed by fabricators and has high potential in having errors due to the tedious nature of the work. In order to overcome the current inspection limitation, this study presents an approach that provides visual assistance and inspection enhancement for inspectors to assess the dimensional layout of reinforcement steel fixing. To this end, this study aims to establish an end-to-end framework for rebar layout quality inspection using laser scanning and Augmented Reality (AR). The proposed framework is composed of three parts: (1) the laser-scanned rebar data processing; (2) the rebar inspection procedure integrating with AR; and (3) the checking and fixing the rebar layout through AR visualization. In order to investigate the feasibility of the proposed framework, a case study assessing the rebar layout of a lab-scaled formwork containing two rebar layers is conducted. The results of the case studies demonstrate that the proposed approach using laser scanning and AR has the potential to produce an intuitive and accurate quality assessment for the rebar layout.