• 대한공업교육학회지
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• v.43 no.1
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• pp.1-19
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• 2018

As the BIM designing technology has been applied recently in the construction field, architectural design education in the field of work and university has been changing to 3D modeling. Nevertheless, architectural design & drafting education in the construction specialized vocational high school is not responding appropriately to change. Despite the fact that students need to have 3D modeling design ability, there is a very lack of 3D housing design instructional material that can satisfy the change. The purpose of this study is to develop BIM-based 3D modeling instruction material and apply to analyze effect on interest and task performance ability on Housing Design. The 3D modeling instruction material used in this study was developed through four stages of preparation, development, implementation and evaluation according to the PDIE model procedure. Also, the experimental design model for hypothesis testing was used nonequivalent control group pretest-posttest design. Based on the experimental design model, BIM-based 3D modeling instruction material was performed in the experimental group and 2D CAD-based standard instruction material was taught in the control group. Experimental treatment was conducted on the students of construction specialized vocatinonal high school, and applied to the subject of Professional Drafting in the 12 hours. Before and after the experimental treatment, the interest and task performance ability on Housing Design were tested. Based on the test results, we analyzed the effects of the 3D modeling instruction material through the independent samples t-test. The results of the study are as follows. First, BIM-based 3D modeling instruction material was developed of 'Housing Design & Drafting' unit on the subject of Professional Drafting in construction specialized vocational high school. Second, the application of 3D modeling instruction material has shown to be effective in improving students' interest. Third, the application of 3D modeling instruction material has shown to be effective in improving students' task performance ability on Housing Design.

• Journal of KIBIM
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• v.13 no.2
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• pp.29-36
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• 2023

This study explores the potential of utilizing video-based data analysis and machine learning techniques to estimate the number of occupants within a building. The research methodology involves developing a sophisticated counting system capable of detecting and tracking individuals' entry and exit patterns. The proposed method demonstrates promising results in various scenarios; however, it also identifies the need for improvements in camera performance and external environmental conditions, such as lighting. The study emphasizes the significance of incorporating machine learning in architectural and urban planning applications, offering valuable insights for the field. In conclusion, the research calls for further investigation to address the limitations and enhance the system's accuracy, ultimately contributing to the development of a more robust and reliable solution for building occupancy estimation.

• Korean Architects
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• s.477
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• pp.79-84
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• 2009

오늘날 전 세계적으로 급격한 에너지 사용과 이에 따른 온실가스의 증가로 기후변화 현상이 세계 곳곳에서 나타나고 있다. 이러한 지구온난화는 산업화에 따른 에너지소비가 주요한 원인으로 꼽히고 있으며, 선진국에서는 에너지소비와 이산화탄소 방출을 줄이기 위한 노력을 적극적으로 추진하고 있다. 우리나라에서도 2013년부터는 온실가스 감축 의무 이행국에 포함될 것으로 예상되어 지속가능(sustainable)한 국가발전을 위한 노력을 기울이고 있으며, 저탄소 녹색성장을 화두로 적극 대처하고 있다. 우리나라는 세계10대 에너지 소비국이면서 97%의 에너지를 외국에 의존하고 있다. 더욱이 이산화탄소배출량은 세계9위를 차지하고 있다. 따라서 향후 선진국과 경쟁을 하기위해서는 산업구조를 시급히 개선하여 에너지 소비를 줄이고 이산화탄소 배출을 적극적으로 억제하여야 한다. 현재 국내에서 사용되는 전체 에너지 가운데 건물에서 소비되는 에너지는 약 40%정도를 차지하고 있다. 이에 따라 건물에서의 에너지 사용량을 줄이고 환경부하를 저감할 수 있는 친환경 건축물의 구축이 시급하며, 관련 기술 개발 및 실제 건축물에 적용을 위한 노력이 진행되고 있다. 친환경 건축 관련 기술은 오늘날 많은 신축 건물에 적용되고 있으나, 그 성능은 아직까지 미흡한 부분이 많다. 건축물의 설계단체에 환경성능 분석결과가 적절히 반영된다면 적은 노력과 비용으로 매우 우수한 친환경 건축물을 구축할 수 있다. 하지만 기존의 설계절차 및 성능분석 지원 시스템으로는 건축 설계단계에서 에너지 소비량을 포함한 친환경 성능을 분석하기에 많은 시간의 투입과 전문가의 도움이 필요하다. 다행히 최근에 이러한 건축물의 친환경 성능 분석에 건축정보모델링(Building Information Modeling, BIM)기술을 활용할 수 있는 연구가 진행되고 있다. 건축정보모델링은 컴퓨터를 이용하여 건축물의 설계 데이터뿐 만 아니라 관련 모든 정보를 모델링 하여 건축물의 설계단계부터 건물의 폐기단계까지 활용되는 기술이다. 이미 선진 외국에서는 활발한 연구가 진행되어 실용적용 단계에 있으며, 국내에서도 초기 연구가 진행 중이다. 이러한 건축정보모델링 기술이 친환경 건축물 구축기술에 활용된다면, 친환경 건축물 구축 및 성능 향상에 많은 도움이 될 수 있을 것으로 기대된다. 또한, 녹색 성장의 기반이 될 수 있는 건축물의 설계 및 시공, 유지관리가 가능해 질것이다. 따라서 이번 연재에서는 지속가능한 설계와 건축정보모델링을 활용한 건축 환경 성능을 분석에 관한 내용을 주제별로 다루고 그 사례를 살펴보고자 한다.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.591-605
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• 2018

The information on the planar figure of the building envelope is commonly required in various criteria related to the energy performance of the building. However, since the method of creating varies depending on each criterion, the information displayed in the planar figure of the building envelope differs considerably according to the person making the figure. In this regard, this study sought to derive the commonly required information for the unification of the information included in the planar figure of the building envelope, and thus examine the standardization of the planar figure of the building envelope based on BIM. Towards this end, 1) the required information about the planar figure of the building envelope was derived through the literature review and case analysis results submitted to the energy performance evaluation agencies, and 2) the standardized output technology using IFC was investigated based on the required information. Therefore, it is expected that the findings of this study will help to create a general-purpose planar figure for the building envelope, and this study can serve as the preliminary research for automatically extracting the information on the planar figure of the building envelope.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.97-105
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• 2021

The CM at Risk project, which started with the aim of strengthening the global competitiveness of the domestic construction industry through innovation of ordering system, is being implemented in the form of pilot projects in the public sector. However, it seems that it is necessary to enact related laws and regulations to move to the formal ordering system, and the performance analysis of the project is still insufficient. In this paper, the effectiveness and reliability of the project were verified through the performance analysis of the Pre-Construction Service of the four pilot projects currently being implemented. The results of the study were analyzed in five areas including Team Building, Design Book, Construction Period, VE, and BIM. Through the interview survey, the problems were confirmed and improvement plans were presented. The results of this study are expected to be used as basic data for the legalization of ordering system to be implemented in the future.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.260-266
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• 2013

This study for establishing specific standards of atrium design aims to discuss design of atrium to consider energy performance according to the types of atrium of office building. In order to evaluate a type and a scale of atrium at the early design stage, modeling details of mass design were set as standards of conceptual design. In the experiment, Project Vasari was used to analyze modeling and energy consumption, based on the LOD 100-step suggested by AIA, because there is no guideline to specify a level of modeling details at each design process. From this analysis, the correlation among a simple-typed atrium and scale and energy load was understood, and the followings are the considerations for designing an atrium. First, the single-sided atrium reduced energy the most, and it was followed by three-sided, two-sided, four-sided and continuous-typed ones. On the whole, they could decrease energy by up to about 15%. Also, the atrium with a wide facade facing in the south was more favorable to reduce energy. Second, planning an atria within 10~30% of the whole building area was more energy efficient. Third, rather than the depth, adjusting the length in designing an atrium could reduce cooling and heating loads by 1.5% per 1m. As explained above, energy performance evaluation considering types and planning elements of atrium helps to assess alternatives in a reasonable way. In particular, considering the use of building needs to be preceded to select a type of atrium, although it is also important to consider its planning elements.

• Journal of the Korea Institute of Building Construction
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• v.20 no.6
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• pp.577-584
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• 2020

Construction automation and robotics have been widely adopted in the construction industry as a promising solution to such issues like a shortage of skilled labor and the difficulties workers face in harsh working environments. The analysis of the knowledge structure and its evolution from the existing articles helps identify essential knowledge elements and possible future research directions. This study attempts to (1) construct keyword networks from the papers published in the International Symposium on Automation and Robotics in Construction (ISARC), (2) investigate how keywords and keyword communities are associated with each other, and (3) examine the changes in the crucial keywords over time. Through cluster analysis, 79 keywords were categorized into four groups (BIM, Building construction, Sensing, and GPS as representative keywords) with similar structural positions. Research trends show that research themes related to Infrastructure, Construction equipment, and 3D have consistently received a large amount of attention, regardless of geographical region. Research on as-built status model utilization through BIM and Laser scanning and improving Energy performance is taking place more frequently. In contrast, research studies related to problem-solving based on Neural networks are not as common as previously. This study provides useful insights into the construction automation field, at both the macro and micro levels.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.