• Korean Journal of Construction Engineering and Management
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• v.9 no.5
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• pp.75-85
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• 2008

The construction industry has been leading the growth of the nation's economy not only by providing with various infrastructure projects but also by positively impaction related industries such as crating numerous job opportunities. Relevant statistics show the production amount of construction taking about 17.5% of the GDP (Gross Domestic Product). In spite of its positive impacts on the economy, image of Korea construction industry is generally reflected as negative mainly because of its environmental disruption, low payment, bribe, fraudulent work and inefficiency. It brings students to be reluctant choosing the construction industry as their carrier path and governmental and principal research status. Therefore it has been difficult to recruit highly qualitied human resources to the industry while the morale of the whole industry has gradually become demoralized. To improve this stand, many domestic researchers carried out research projects for improving the image of Korea Construction Industry. This study also sympathizes with necessity of improving the negative image of construction industry to remain as one of the leading industry in the 21st century. Especially, this study focused on finding important factors which have significant influences on the image of the industry. Through out the research, image influence factors was identified by rigorous literature review and interviews as industrial and academic experts. Factors, then, categorized and used as the main framework for the survey which designed to fine the degree of impacts on the image of the construction industry. In analyzing the survey results, various statistical techniques was employed including factor analysis, Chi-Square-Test, Correlation Analysis and Multiple Linear Regression. Identified as the most influent factors to the image of the construction industry include morale of construction employee, and prospects the industry which of the judgement by payment, impacts on nation's economy, future of the industry, etc.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.93-106
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• 2008

Domestic construction industry that accounts for nearly 17.5 percent of GDP is one of the major industries that has been leading domestic economy development. However, accumulated negative image such as corruption, fraudulent work and 3D industries shadowing the remarkable achievement is rampant. In order to improve the image for construction industry, this research investigated various cases and activities related to the image enhancement. Cases analyzed include activities of National Center for Construction Education and Research (NCCER) and Construction Industry Training Board (CITB) such as 'Build Your Future', 'Construct My Future', 'Positive Image 2004', and so on. Based on the analysis results, a strategic framework to improve the image of Korean construction industry was proposed. It is envisioned that improved image of the industry will positively affect the growth of the construction industry by attracting more qualified human resources.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.225-226
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• 2022

The construction industry, which has a labour-intensive and conservative nature, is exclusive to adopt new technologies. However, the construction industry is viably introducing the 4th Industrial Revolution technologies represented by artificial intelligence, Internet of Things, robotics and unmanned transportation to promote change into a smart industry. An image-based artificial intelligence technology is a field of computer vision technology that refers to machines mimicking human visual recognition of objects from pictures or videos. The purpose of this article is to explore image-based artificial intelligence technologies which would be able to apply to the construction sites. In this study, we show two examples which is one for a construction waste classification model and another for cast in-situ anchor bolts defection detection model. Image-based intelligence technologies would be used for various measurement, classification, and detection works that occur in the construction projects.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.8-9
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• 2014

At the moment, construction industry has negative images such as faulty construction, 3D industry and corruption/bribe and they are being projected onto normal people and those engaged in the industry through various news media. Not only does this worsen the lack of skilled manpower by affecting high school students who will be responsible for the construction industry in the future but also makes the future of the industry uncertain by promoting the avoidance of industrial jobs. Therefore, this study suggested basic data for improving the images of construction industry by investigating interest and images of workers in the industry that high school students think as promising construction manpower, and extracting the present negative images through evaluation of associated images.

• Journal of the Korea Institute of Building Construction
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• v.16 no.5
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• pp.471-477
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• 2016

The construction industry is very important to supply the facilities and infrastructure in Korea. However, the images of construction industry are negative in actual. In addition, the studies to improve the images of construction industry up to now have a limitation to research the images about that time. So, in this study, the authors examined and analyzed the peoples' trend changes of the images of construction industry with time. The results showed that the positive images of construction industry about the contribution to national economy and the future prospect are diminished. And it is revealed that the negative images of construction industry about the morality are decreased, too. The authors proposed the strategies to improve the images of construction industry by the based on these results.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.128-129
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• 2015

Domestic construction industry has seen a negative image in the sense that various accident occurs frequently. Status of construction accidents in the case of domestic workers is reduced. However, if the situation of foreign workers is increasing every year. This study aims to create high-temperature exposure standard table for foreign workers and domestic workers in the analysis through the discomfort index of fatalities and using the WBGT index for reducing fatalities foreign workers to improve the image of the construction industry.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.134-135
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• 2015

This study was conducted to present basic improvement measures to improve the negative construction industry image of construction workers in the future, by investigating the construction industry images that construction workers have, through previous studies and analyzing the factors influencing these images, based on the multiple regression model. But this study is limited to construction workers and failed to establish specific improvement measures. Therefore, continued research which remedies the limitation of this study, will performed in the future.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.62-70
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• 2016

Construction engineers are the core resource for the sustainable development of construction industry and positive images toward construction engineers give much impacts on recruit and retainment of the young and talented. Although previous studies reported on negative aspects of the image of construction industry, the image of construction engineers has not been properly investigated to date. The objective of the study is to compare images of construction engineers as perceived by general public and construction engineers themselves in order to identify key characteristics and implications. The result shows that construction engineers themselves, compared with general public, have more negative tendencies on images of construction engineers. The study suggests that it is critical to enhance the understanding of roles of construction engineers and their morality in order to improve general public's perception on construction engineers images. On the other hand, fundamental measures to be taken to restore the environments of construction industry for construction engineers to improve their self-perception on images of construction engineers.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.281-282
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• 2012

Construction performance and productivity improvement are key focus areas in construction industry for any nation. There have been frequent delays and cost overruns in construction projects and poor productivity is one of the major contributions. For this reasons, there have been many research studies performed on the improvement of construction productivity for several decades. However, measuring productivity on a construction job site is still not an easy work. Because collecting reliable data consistently from the job site requires a lot of personnel efforts causing extra time and cost. This paper provides a basic study on the application of image processing technology for measuring unit productivity. It presented the possibility of unit productivity measurement by image processing technology through case study.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.106-115
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• 2021

Classifying work trades using images can serve an important role in a multitude of advanced applications in construction management and automated progress monitoring. However, images obtained from work sites may not always be clean. Defective images can damage an image classifier's accuracy which gives rise to a needs for a method to enhance a work trade image classifier's performance. We propose a method that uses work dependency information to aid image classifiers. We show that using work dependency can enhance the classifier's performance, especially when a base classifier is not so great in doing its job.