• Korean Journal of Construction Engineering and Management
• /
• v.9 no.6
• /
• pp.268-276
• /
• 2008

In highway construction projects, concrete pavement productivity has been challenged with constructors and decision-makers; at present there are few methods available to accurately evaluate the factors impacting on it. Any inefficient method to analyze it leads to the excessive schedule, higher rehabilitation costs, shorter service life, and reduction of ride quality. To implement these negative outcomes, constructors or decision-makers need a systematic tool that can be used to categorize the factors related to construction productivity. This paper applies multiple regression technique for productivity analysis of the Jointed Plane Concrete Pavement (JPCP), identifies the significant factors, and provides a predictive model assisting in monitoring and managing the productivity of the JPCP construction process. The completed and progressive projects are employed to derive and assess the proposed model. The results are analyzed to illustrate its capabilities.

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

• Korean Journal of Agricultural Science
• /
• v.37 no.3
• /
• pp.413-433
• /
• 2010

South Dakota is an important agricultural state in the United States with annual cash receipts from agricultural products exceeding $9 billion dollars. This production is possible because of large areas of productive soils. This publication describes the general characteristics and qualities of the major soil groups recognized in South Dakota. The soil forming factors are briefly described, soil classification is introduced, and the genesis of typical Udalf and Ustoll soils are discussed. Soil management issues impacting the use of SD soils are considered. Long-term (>70 yrs) cultivation has significantly reduced surface soil organic carbon levels (>30% reduction) when compared to non-cultivated soil. Soil test phosphorus levels significantly increased in cultivated fields due to commercial P fertilization. The major long-term production problems for SD soils are conservation of soil moisture, organic matter and nitrogen losses, fertility management, and wind and water erosion control.