• Journal of the Korea Institute of Building Construction
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• v.5 no.1 s.15
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• pp.97-103
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• 2005

Building costs means capital costs which include cost of land, costs of acquiring and preparing the site, construction costs, professional fees, furnishings, cost of financing the project. and cost of management required to run and maintenance the building for use. There are several phases that determine the building costs : design phase, construction phase, and operation & maintenance phase. So, the cost of work could be set against the examining the full range of complexities that a building program might contain. To solve this problem, it needs to compute building cost systematically. This is still in the development stage, awaiting the organization of rational cost data base. The method of cost saving by cost control could be constituted by detailed knowledge of building costs for all possible combinations of components and subsystems that can be assembled into integration model of cost factor on each phase of project development. The model of cost saving in each building phase is available for procedures of cost control of building systems.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05a
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• pp.105-109
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• 2010

Nowadays, the construction of high-rise building is rapidly increasing. There are many cost-increasing factors to the management in the high-rise building construction. For large and complex projects like the high-rise building construction, the more systematic methods and tools are needed for cost control. It is important to identify major cost control items and to manage them for effective cost management in the high-rise building construction. This study proposes a method of identifying major cost-increasing factors in the high-rise building construction based on the Delphi method.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.220-226
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• 2003

The Residential Building Cost Index which presents price variation of construction resources required in a residential building project in single value, has been compiled to evaluate appropriate level of the price variation. This research reviewed the compilation methodology for Residential Building Cost Index based on comparative analysis on domestic statistics and proposed the Residential Building Cost Index through a variety of case studies. It would facilitate monitoring the price variation of a residential building cost and contributes to enhancing the applicability of construction cost data.

• Journal of KIBIM
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• v.5 no.2
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• pp.34-45
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• 2015

As buildings become larger and more complicated, construction costs have increased with a considerable effect on buildings' Life Cycle Cost (LCC). However, there has been little consideration on economic aspects in the selection of construction materials due to limited information on the materials and dependency in architects' experience and inefficiency in cost estimation, causing design changes, increase in maintenance cost, difficulty in budgeting, and decrease in building performance. To solve these problems, this study proposed a BIM-based material selection model which reflects the comprehensive economic efficiency of building materials. Our cost prediction model can estimates the material-related cost during the entire building life cycle. Furthermore, we implemented the proposed model in connection with BIM, which can analyze and compare LCC by material. Through the validation of the model, we could confirm the necessity of LCC-based material selection in comparison with the conventional cost-centered material selection.

• KIEAE Journal
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• v.16 no.4
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• pp.21-30
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• 2016

Purpose: Energy saving in the built facilities is getting important due to energy crisis. The Korea government has been implemented several energy and green building policies and practices. The both of government and industry also developed green building strategies ant technologies to reduce energy consumption and carbon emission. The purpose of this research is to identify applicable green building strategies and technologies for that can be cost effective and applicable to a multiplex house. Method: This research identified appropriate green building strategies from analysing green building strategies from G-SEED certified apartment projects and popular green building strategies. This study also adopted a survey research method to find out the applicable green building strategies for a multiplex housing. In addition, this research also conduct cost estimating to identify initial cost premium of green building strategies. Results: The research outcomes in this study guide a building owner to know about initial cost premiums of green building strategies and technologies and an architect and contractor to identify appropriate and cost effective green building strategies that can be applicable to a multiplex house.

• Architectural research
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• v.7 no.1
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• pp.11-18
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• 2005

In today's building industry the emphasis has been geared more towards construction, thus building maintenance and life cycle have been neglected until now. A direct result of this neglect is the rapid aging of building, which leads to more cost-effective decision making methods for the prolongation of building life span. The following study is conducted in the area of Daegu and Seoul in order to develop the estimation analysis method of the annual operation cost of the Korean high-rise condominiums for the cost-effective decision making support through mathematical and statistical analyses including the present value and standardized measurement corrections. Based on the assumption that the life expectancy of the high rise condominium is 50 years, initial cost is ￦421,212/$m^2$, and a total sum of yearly operation cost during life expectancy is ￦2,154,499//$m^2$), yearly accumulated operation cost is shown as below: $AOC=0.7097t^4-38.803t^3+806.95t^2+11045t-496.52$ ($R^2=0.98$) (Here, AOC = Accumulated Operation Cost, t = given years)

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.685-691
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• 2009

Recently, public office building projects are being recognized by many construction engineers and researchers, as the critical projects in the construction industry. The project budgets have sometimes exceeded due to the lack of core knowledge, experiences, skills and experts concerned in cost planning and estimating in the pre-construction stage. It has been highlighted that planning and estimating effectively the cost of public office building projects as critical in the design stage. Within this context, some cost data books and systems, such as RSMeans cost data systems and Spon's price book, have been systematically developed and used by many construction cost managers and organizations in order to effectively estimate and use their project budgets. As a result of this research, a cost estimating model for finishes has been developed, considering the cost data used in public office building projects.

• Journal of Construction Engineering and Project Management
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• v.4 no.4
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• pp.9-16
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• 2014

The purpose of this paper is to develop a model for forecasting early design construction cost of building projects using Artificial Neural Network (ANN). Eighty questionnaires distributed among construction organizations were utilized to identify significant parameters for the building project costs. 169 case studies of building projects were collected from the construction industry in Gaza Strip. The case studies were used to develop ANN model. Eleven significant parameters were considered as independent input variables affected on "project cost". The neural network model reasonably succeeded in estimating building projects cost without the need for more detailed drawings. The average percentage error of tested dataset for the adapted model was largely acceptable (less than 6%). Sensitivity analysis showed that the area of typical floor and number of floors are the most influential parameters in building cost.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.33 no.12
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• pp.11-18
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• 2017

The safety inspection cost of the construction work was based on commercial facilities classified as a single building. Therefore, it is not possible to fully reflect the characteristics of the multi-building construction project such as apartment houses. Therefore, this study suggests a reasonable estimation model that can fully reflect the characteristics of the multi-building construction project. The safety inspection cost estimation model proposed two models such as construction cost ratio method and cost plus fixed fee method. And these models were simulated by the apartment construction work and compared with the current standard. As a result, the current construction cost ratio method has shown that the safety inspection cost tends to be overestimated as the construction size increases. Therefore, the proposed model has reflected characteristics of the multi-building construction project, so that it can reasonably estimate the safety inspection cost more than the current standard.