• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.29-30
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• 2023

In view of the labor-intensive characteristics of the construction industry and the persistent negative image, the problems of continuous aging and manpower shortage in the domestic construction industry are becoming more and more serious, which must be solved for the survival of the domestic construction industry. China has made certain achievements in prefabricated buildings. Through this research, we describe the development process of China's prefabricated buildings in different stages, and analyze the necessary policy promotion and standard support in the development of prefabricated buildings. Analyze and examine the necessary conditions for China's prefabricated building achievements and success factors, and make better proposals for the development of the construction industry in South Korea.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• 한국산업정보학회논문지
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• 제19권3호
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• pp.67-74
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• 2014

Over the last several decades, the United States has experienced a great number of natural disasters. To minimize the impact of the natural hazard events, the U.S. government spent a tremendous amount of money through federal assistant programs. To be eligible for the programs, a mitigation project must be cost effective (more benefits compared to project costs). However, the state and local communities suffering from the natural disasters generally have difficulty in collecting reliable evidence for their damages which can be converted later into benefits when a mitigation project is implemented. Therefore, this paper shows the process of conducting a benefit cost analysis with limited data. Besides, it also provides how to apply the limited data to the analysis through a case study. Consequently, this paper help state and local communities get funding from the federal government, which in turns will improve the image of construction industry by preventing people from natural disasters.

• 한국안전학회지
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• 제19권1호
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• pp.108-116
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• 2004

After the IMF financial crisis, a lot of risks in construction industry have been increased gradually with the expansion of construction industry widely, higher stories of building, and the expansion of the underground space and excavation work. These risks are bringing out construction accidents such as the death, the injury of worker and so on, not so much as it would be effected the corporation's image. In spite of these situations, many construction industries still maintain the wrong methods and not try to decrease construction accidents. Recently, we should focus on the fact that management system of the larger construction also is being good, on the other hand, the condition of the work in the case of smaller construction industries is very poor, construction company have a particular safety management system but it has a difference according to the construction magnitude, construction companies have a particular safety management system but it has a difference depending on the construction according to the construction magnitude. Therefore, this study will suggest the developed way of construction safety management by the comparison and analysis from the difference between the higher and the smaller construction industries.

• 한국조리학회지
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• 제10권4호
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• pp.50-66
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• 2004

We defined life style as something that every members of society have in common. These social and cultural environments build up not only society group or every individual's expectation but also its own life style. In that way, these social and cultural environments leads to particular consumer behavior pattern in this food-service industry. So we regard next generation's trend which consists of rational consumers as important indicator when we make future's plan in foodservice industry. We consider smart map which needs rational and continuous consume pattern as the construction of next generation's main consumer class. Therefore, this study tried to develop of color and image marketing strategies to attract LOHAS and nomadic consumer.

• 대한토목학회논문집
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• 제39권3호
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• pp.399-407
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• 2019

대한민국에서 건설업은 타 업종들과 비교하여 안전사고의 위험성이 가장 높게 나타난다. 따라서 건설업 내 안전성 향상을 도모하기 위해 여러 연구가 예전부터 진행이 되어 왔고, 본 연구에선 건설현장 영상 데이터를 기반으로 물체 탐지 및 분류 알고리즘을 이용해서 효과적인 안전모 자동탐지 시스템을 구축하여 건설현장 노동자들의 안전성 향상에 기여하고자 한다. 본 연구에서 사용된 알고리즘은 Convolutional Neural Network (CNN) 기반의 물체 탐지 및 분류 알고리즘인 Region-based Fully Convolutional Networks (R-FCN)이고 이를 Transfer Learning 기법을 사용하여 딥러닝을 실시하였다. ImageNet에서 수집한 1089장의 사람과 안전모가 포함된 영상으로 학습을 시행하였고 그 결과, 사람과 안전모의 mean Average Precision (mAP)은 각각 0.86, 0.83로 측정되었다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.30-31
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• 2021

The construction industry is an industry in which skilled construction workers should be secured and trained because of the quality and productivity of buildings, but the negative image makes it difficult to supply and demand skilled construction workers. In response, this study derived the factors influencing the intention of transferring construction workers to other occupations and investigated ways to improve the prevention of leaving other occupations by age of construction workers. The factors influencing the turnover of construction workers have also been derived, and work overload, salary, workplace stability, and employment competitiveness have a significant impact on the intention of new employees to change jobs. According to a survey on ways to improve the prevention of leaving other jobs, those in their 20s and 30s are "improving working conditions, such as providing holidays, paying overtime, compliance with working hours," and those in their 50s and 60s.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.546-552
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• 2013

The future competitiveness of construction industry is dependent on K-12 students. However, unfavorable images of construction industry have negative influence on K-12 students' decision-making of their career. This negative image makes them not want to find out what actually happens in construction industry. Consequently, it is important to give K-12 students the opportunity to know what construction employees actually do in their job. Studies show that K-12 students who encounter the job early-on are more likely to choose it as their career. In this context, this paper proposes construction educational game in which it can serve as a medium for capturing K-12 students' interest in Construction Management (CM). Based on the literature reviews, challenges of construction educational game for K-12 students which are edutainment, hands-on experience and social interaction, are derived. To address these issues, conceptual model and scenario are designed. Based on designed scenario, prototype of Simulation based Construction Game in Virtual World (SCGVW) is developed in Second Life (SL) and applicability test to K-12 students are implemented. This paper concludes with a discussion of the lessons learned and the future development steps of the construction educational game for K-12 students.

• 한국자동차공학회논문집
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• 제25권2호
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• pp.219-226
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• 2017

This paper addresses the effective and quantitative image DB construction for the development of front looking camera systems. The automotive industry has expanded the capability of front camera solutions that will help ADAS(Advanced Driver Assistance System) applications targeting Euro NCAP function requirements. These safety functions include AEB(Autonomous Emergency Braking), TSR(Traffic Signal Recognition), LDW(Lane Departure Warning) and FCW(Forward Collision Warning). In order to guarantee real road safety performance, the driving image DB logged under various real road conditions should be used to train core object classifiers and verify the function performance of the camera system. However, the driving image DB would entail an invalid and time consuming task without proper guidelines. The standard working procedures and design factors required for each step to build an effective image DB for reliable automotive front looking camera systems are proposed.

• 대한건축학회논문집:계획계
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• 제35권12호
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• pp.43-53
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• 2019

The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers' knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.