• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.457-468
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• 2011

Recently, the non-vibration mass excavation method is used on sewage pipes and road construction sites in Jeju. However, a construction cost estimation based on the unit quantity does not provide a proper unit price. In this study, a comparison of the productivity of the standard quantity-per-unit costing method and the one of the work crew combination method was made based on the site monitoring of the non-vibration mass excavation method that is used in construction sites near Jeju. For this, data of 35 sites were collected: a regression equation was derived from the 30 data, and verification was carried out through the remaining 5 data. The analysis concluded that a day's workload is $16.43m^3$. In addition, a combination of the equipment considering the site conditions and the amount of labor, which varies with the number of work crew was obtained in order to estimate the construction cost of the work crew combination method. The construction cost was calculated based on the one-day workload ($16.43m^3$) derived from the regression analysis. The cost then was analyzed and compared with the standard quantity-per-unit costing method.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.26-35
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• 2022

The 7th education reform in 1997 has led changes in the way buildings were constructed and such changes drove educational facilities to steadily consume more energy every year. Also, these facilities take several years' estimated expenditure as well as the increased unit price of electricity into account when planning their annual operating budget. Such circumstances may adversely affect the establishment of their budget plan since improper allocation of operating costs could take place. To propose educational facilities' operating budget management plan on electrical energy consumption, this study developed a model that help oversee the facilities' consumption of electrical energy. For the model development, the primary core variables related to electrical energy factors from the aspects of surroundings, physics, policy, etc. were derived from taking both literature research and the characteristics of these facilities into account. The secondary core variables were then derived using the correlation analysis. Lastly, the electric energy use prediction model was developed by performing regression analysis based on the derived secondary core variables.