• Korean Journal of Construction Engineering and Management
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• v.9 no.4
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• pp.92-100
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• 2008

Productivity is acknowledged as a very important factor for successful construction projects. Various data items collected daily form a construction site can be used for monitoring its productivity by analyzing them. However, no analytical methods for that purpose have been established in the domestic construction industry yet. Previous researches that utilized OLAP and data mining to analyze the factors that affect the productivity did not do well with predicting future cases with sufficient reliability. This research therefore proposes a new analytical process which is capable of figuring out the factors that would affect the productivity of future projects, through qualitative and quantitative analysis of the data collected from past projects.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.422-426
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• 2006

Productivity is one of the very important index that measures efficiency of production activities in industry, enterprises and the building industry as well. None the less, the concept of construction productivity is not so clear that productivity management in the building industry have been performed by experience or intuition, productivity related data have not been analyzed through effective productivity management, because structured definition and classification of factors influencing construction productivity did not exist so that it has not been known what information explain each of them. In order to solve this problem, at first construction productivity and factors influencing construction productivity are defined and classified into three groups; (1)Project factors influencing construction productivity (2)Management factors influencing construction productivity (3)Activity factors influencing construction productivity. To find out relation between construction productivity and factors influencing construction productivity, a questionnaire survey for construction managers in the building industry has been conducted.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.548-553
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• 2006

Activity-based productivity data can be used as an significant reference in many areas of project management such as performance evaluation and project planning. However, the existence of various factors influencing construction productivity makes it difficult to collect and analyze the productivity data. In the most of the domestic construction sites, there is no systematic method to collect and analyze the productivity data along with information on influencing factors; it is common to heavily rely on experience and intuition of field managers when dealing with construction productivity data. Therefore it is necessary to develop a management system for collecting and utilizing the productivity data as well as the factors influencing construction productivity. The main objective of this research is to define the construction productivity and its influencing factors at the activity level. In addition, methodologies on how to analyze the productivity data and to estimate productivity of future projects are proposed.

• Korean Journal of Construction Engineering and Management
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• v.8 no.1 s.35
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• pp.132-140
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• 2007

There is an increase in interest and investment in high-rise housing as it is perceived to be a new value-added market in the construction industry. In constructing a high-rise housing, the finishing works are executed in accompaniment with many other activities that are progressed repeatedly and spontaneously on each floor. It was reported that the duration of finishing works differs according to the management ability of the executing company and has a significant effect on the entire project duration. We suggest a need to concentrate on important management factors by analyzing the factors affecting the productivity of finishing works based on the site characteristics in high-rise housing. There are various complex productivity-affecting factors including the technical factors involved in planning and managing the processes of finishing works. From the viewpoint of planning and management factors, the importance of productivity-affecting factors was analyzed using the Analytic Hierarchy Process (AHP). A continuous examination of the management of high-importance factors will make it possible to improve productivity by enhancing the understanding of productivity-affecting factors of finishing works and suggesting a practical management direction.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.306-311
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• 2007

Proper management of construction productivity is one of the main drivers of project success. This research focuses on the development of a construction productivity analysis system based on Web and OLAP in order to effectively manage construction productivity. Throughout the research effort, a construction productivity analysis system was modeled using IDEFO & ERD techniques and was deployed on the WWW(World Wide Web). Based on the system modeling, this research studies the methodology for system development of construction productivity that can consult the decision of managers on job site.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.149-150
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• 2021

Earthwork is the starting work of construction process, which has the greatest impact on the productivity of the construction project among the works. However, it is difficult to derive accurate productivity becuase the earthwork plan is affected by geological condition of the jobsite. Therefore, a simulation model for productivity analysis of earthwork was developed using CYCLONE modeling method in this study. In this paper, simulation model was made considering the impact factors of earthwork productivity. The proposed model can be utilized for sensitivity analysis in future studies.

• Journal of the Korea Institute of Building Construction
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• v.9 no.3
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• pp.51-58
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• 2009

The intention of this study is to analyze safety consciousness of construction labors in Gawang-Ju region. According to Korea Occupational Safety & Health Agency, the rate of disaster in construction industry is very high comparing with other fields. Most of all, the consciousness of construction labors is very important because accidents are caused from it mainly. For this reason, it is necessary to recognize safety consciousness of labors who directly work in construction field. For decreasing the rate of disaster in construction, I examined and analyzed safety consciousness of construction labors and then, groped improvement of safety activity. Finally, this study deducted improvement of safety activities and management.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.195-217
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• 2014

This paper has explored when implementing SW development project of the IT service corporations, what factors affect its productivity in SW development methodology. The importance of the AHP analysis surveyed from the experts in IT service companies and related committee, was shown in the order of the following processes : 1) Deployment process 2) Management process 3) Establishment process. It is measured the methodology levels of establishment, deployment and management in each project using the survey results collected from project execution members and analyzed the productivity of projects that have been executed within 2 years. Using project methodology level, productivity correlation analysis, and regression analysis, this study confirms that the methodology deployment level brings positive effects significantly to SW development productivity. The significance of this study would be not only to research and analyze SW development productivity using the real project execution data but also to underline the necessities and the importance of steady research, deployment and support for SW development productivity improvement.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.123-127
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• 2008

As buildings becoming higher and more enormous the portion of steel works has been increased, which makes the schedule planning and management more significant. However, in actual construction sites, management is more based on a manager's construction experience than productivity data accumulated in previous projects. Moreover, most of the existing studies also featured a theoretical approach rather than an analysis of data straightforwardly collected in sites. In this study, a steel-erection site was visited to collect productivity data. The study found that there were significant disparities between aboveground work productivity and underground work. However, the productivities of 'first node on ground' and 'second node on ground' were estimated similar. The productivity data collected and factors affecting the productivity will help managers to plan and control their similar steel-erection works. This study will also be beneficial for those performing related studies.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.3-12
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• 2019

Despite the importance and function of productivity information, in the Korean construction industry, the method of collecting and analyzing productivity data has not been organized. Also, in most cases, productivity management is reliant on the experience and intuitions of field managers, and productivity data are rarely being utilized in planning and management. Accordingly, this study intends to develop a prediction model for interior finishes of apartment using deep learning techniques, so as to provide a foundation for analyzing the productivity impacting factors and predicting productivity. The result of the study, productivity prediction model for interior finishes of apartment using deep learning techniques, can be a basic module of apartment project management system by applying deep learning to reliable productivity data and developing as data is accumulated in the future. It can also be used in project engineering processes such as estimating work, calculating work days for process planning, and calculating input labor based on productivity data from similar projects in the past. Further, when productivity diverging from predicted productivity is discovered during construction, it is expected that it will be possible to analyze the cause(s) thereof and implement prompt response and preventive measures.